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Shen J, Jiang L, Wang K, Wang A, Chen F, Newcombe PJ, Haiman CA, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction. Genet Epidemiol 2024; 48:241-257. [PMID: 38606643 DOI: 10.1002/gepi.22562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/27/2024] [Accepted: 03/27/2024] [Indexed: 04/13/2024]
Abstract
Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.
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Affiliation(s)
- Jiayi Shen
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Lai Jiang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Kan Wang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Anqi Wang
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Fei Chen
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Paul J Newcombe
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher A Haiman
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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2
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Judd J, Spence JP, Pritchard JK, Kachuri L, Witte JS. Investigating the Role of Neighborhood Socioeconomic Status and Germline Genetics on Prostate Cancer Risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.31.24311312. [PMID: 39132496 PMCID: PMC11312637 DOI: 10.1101/2024.07.31.24311312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Background Genetic factors play an important role in prostate cancer (PCa) development with polygenic risk scores (PRS) predicting disease risk across genetic ancestries. However, there are few convincing modifiable factors for PCa and little is known about their potential interaction with genetic risk. We analyzed incident PCa cases (n=6,155) and controls (n=98,257) of European and African ancestry from the UK Biobank (UKB) cohort to evaluate the role of neighborhood socioeconomic status (nSES)-and how it may interact with PRS-on PCa risk. Methods We evaluated a multi-ancestry PCa PRS containing 269 genetic variants to understand the association of germline genetics with PCa in UKB. Using the English Indices of Deprivation, a set of validated metrics that quantify lack of resources within geographical areas, we performed logistic regression to investigate the main effects and interactions between nSES deprivation, PCa PRS, and PCa. Results The PCa PRS was strongly associated with PCa (OR=2.04; 95%CI=2.00-2.09; P<0.001). Additionally, nSES deprivation indices were inversely associated with PCa: employment (OR=0.91; 95%CI=0.86-0.96; P<0.001), education (OR=0.94; 95%CI=0.83-0.98; P<0.001), health (OR=0.91; 95%CI=0.86-0.96; P<0.001), and income (OR=0.91; 95%CI=0.86-0.96; P<0.001). The PRS effects showed little heterogeneity across nSES deprivation indices, except for the Townsend Index (P=0.03). Conclusions We reaffirmed genetics as a risk factor for PCa and identified nSES deprivation domains that influence PCa detection and are potentially correlated with environmental exposures that are a risk factor for PCa. These findings also suggest that nSES and genetic risk factors for PCa act independently.
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Affiliation(s)
- Jonathan Judd
- Department of Genetics, Stanford University, Stanford CA
| | | | - Jonathan K. Pritchard
- Department of Genetics, Stanford University, Stanford CA
- Department of Biology, Stanford University, Stanford CA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford CA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
| | - John S. Witte
- Department of Genetics, Stanford University, Stanford CA
- Department of Epidemiology and Population Health, Stanford University, Stanford CA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA
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3
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Zou Y, Carbonetto P, Xie D, Wang G, Stephens M. Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.14.536893. [PMID: 37425935 PMCID: PMC10327118 DOI: 10.1101/2023.04.14.536893] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
We introduce mvSuSiE, a multi-trait fine-mapping method for identifying putative causal variants from genetic association data (individual-level or summary data). mvSuSiE learns patterns of shared genetic effects from data, and exploits these patterns to improve power to identify causal SNPs. Comparisons on simulated data show that mvSuSiE is competitive in speed, power and precision with existing multi-trait methods, and uniformly improves on single-trait fine-mapping (SuSiE) in each trait separately. We applied mvSuSiE to jointly fine-map 16 blood cell traits using data from the UK Biobank. By jointly analyzing the traits and modeling heterogeneous effect sharing patterns, we discovered a much larger number of causal SNPs (>3,000) compared with single-trait fine-mapping, and with narrower credible sets. mvSuSiE also more comprehensively characterized the ways in which the genetic variants affect one or more blood cell traits; 68% of causal SNPs showed significant effects in more than one blood cell type.
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Affiliation(s)
- Yuxin Zou
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA
| | - Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Dongyue Xie
- Department of Statistics, University of Chicago, Chicago, IL, USA
| | - Gao Wang
- Gertrude. H. Sergievsky Center, Department of Neurology, Columbia University, New York, NY, USA
| | - Matthew Stephens
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
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4
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Jiang L, Shen J, Darst BF, Haiman CA, Mancuso N, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data. Genet Epidemiol 2024. [PMID: 38887957 DOI: 10.1002/gepi.22577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 04/04/2024] [Accepted: 05/15/2024] [Indexed: 06/20/2024]
Abstract
Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.
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Affiliation(s)
- Lai Jiang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Jiayi Shen
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Burcu F Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
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5
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Shen S, Sobczyk MK, Paternoster L, Brown SJ. From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics. J Invest Dermatol 2024; 144:1189-1199.e8. [PMID: 38782533 DOI: 10.1016/j.jid.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 05/25/2024]
Abstract
Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.
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Affiliation(s)
- Silvia Shen
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Institute for Evolution and Ecology, School of Biological Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
| | - Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sara J Brown
- Centre for Genomic & Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom; Department of Dermatology, NHS Lothian, Edinburgh, United Kingdom
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6
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Lorincz-Comi N, Yang Y, Zhu X. simmrd: An open-source tool to perform simulations in Mendelian randomization. Genet Epidemiol 2024; 48:59-73. [PMID: 38263619 DOI: 10.1002/gepi.22544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 01/25/2024]
Abstract
Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.
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Affiliation(s)
- Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
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7
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Li X, Sham PC, Zhang YD. A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis. Am J Hum Genet 2024; 111:213-226. [PMID: 38171363 PMCID: PMC10870138 DOI: 10.1016/j.ajhg.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
The aim of fine mapping is to identify genetic variants causally contributing to complex traits or diseases. Existing fine-mapping methods employ Bayesian discrete mixture priors and depend on a pre-specified maximum number of causal variants, which may lead to sub-optimal solutions. In this work, we propose a Bayesian fine-mapping method called h2-D2, utilizing a continuous global-local shrinkage prior. We also present an approach to define credible sets of causal variants in continuous prior settings. Simulation studies demonstrate that h2-D2 outperforms current state-of-the-art fine-mapping methods such as SuSiE and FINEMAP in accurately identifying causal variants and estimating their effect sizes. We further applied h2-D2 to prostate cancer analysis and discovered some previously unknown causal variants. In addition, we inferred 369 target genes associated with the detected causal variants and several pathways that were significantly over-represented by these genes, shedding light on their potential roles in prostate cancer development and progression.
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Affiliation(s)
- Xiang Li
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yan Dora Zhang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China.
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8
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Gao B, Zhou X. MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association studies. Nat Genet 2024; 56:170-179. [PMID: 38168930 DOI: 10.1038/s41588-023-01604-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 10/30/2023] [Indexed: 01/05/2024]
Abstract
Fine-mapping in genome-wide association studies attempts to identify causal SNPs from a set of candidate SNPs in a local genomic region of interest and is commonly performed in one genetic ancestry at a time. Here, we present multi-ancestry sum of the single effects model (MESuSiE), a probabilistic multi-ancestry fine-mapping method, to improve the accuracy and resolution of fine-mapping by leveraging association information across ancestries. MESuSiE uses summary statistics as input, accounts for the diverse linkage disequilibrium pattern observed in different ancestries, explicitly models both shared and ancestry-specific causal SNPs, and relies on a variational inference algorithm for scalable computation. We evaluated the performance of MESuSiE through comprehensive simulations and multi-ancestry fine-mapping of four lipid traits with both European and African samples. In the real data, MESuSiE improves fine-mapping resolution by 19.0% to 72.0% compared to existing approaches, is an order of magnitude faster, and captures and categorizes shared and ancestry-specific causal signals with enhanced functional enrichment.
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Affiliation(s)
- Boran Gao
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
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9
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Wang A, Shen J, Rodriguez AA, Saunders EJ, Chen F, Janivara R, Darst BF, Sheng X, Xu Y, Chou AJ, Benlloch S, Dadaev T, Brook MN, Plym A, Sahimi A, Hoffman TJ, Takahashi A, Matsuda K, Momozawa Y, Fujita M, Laisk T, Figuerêdo J, Muir K, Ito S, Liu X, Uchio Y, Kubo M, Kamatani Y, Lophatananon A, Wan P, Andrews C, Lori A, Choudhury PP, Schleutker J, Tammela TL, Sipeky C, Auvinen A, Giles GG, Southey MC, MacInnis RJ, Cybulski C, Wokolorczyk D, Lubinski J, Rentsch CT, Cho K, Mcmahon BH, Neal DE, Donovan JL, Hamdy FC, Martin RM, Nordestgaard BG, Nielsen SF, Weischer M, Bojesen SE, Røder A, Stroomberg HV, Batra J, Chambers S, Horvath L, Clements JA, Tilly W, Risbridger GP, Gronberg H, Aly M, Szulkin R, Eklund M, Nordstrom T, Pashayan N, Dunning AM, Ghoussaini M, Travis RC, Key TJ, Riboli E, Park JY, Sellers TA, Lin HY, Albanes D, Weinstein S, Cook MB, Mucci LA, Giovannucci E, Lindstrom S, Kraft P, Hunter DJ, Penney KL, Turman C, Tangen CM, Goodman PJ, Thompson IM, Hamilton RJ, Fleshner NE, Finelli A, Parent MÉ, Stanford JL, Ostrander EA, Koutros S, Beane Freeman LE, Stampfer M, Wolk A, Håkansson N, Andriole GL, Hoover RN, Machiela MJ, Sørensen KD, Borre M, Blot WJ, Zheng W, Yeboah ED, Mensah JE, Lu YJ, Zhang HW, Feng N, Mao X, Wu Y, Zhao SC, Sun Z, Thibodeau SN, McDonnell SK, Schaid DJ, West CM, Barnett G, Maier C, Schnoeller T, Luedeke M, Kibel AS, Drake BF, Cussenot O, Cancel-Tassin G, Menegaux F, Truong T, Koudou YA, John EM, Grindedal EM, Maehle L, Khaw KT, Ingles SA, Stern MC, Vega A, Gómez-Caamaño A, Fachal L, Rosenstein BS, Kerns SL, Ostrer H, Teixeira MR, Paulo P, Brandão A, Watya S, Lubwama A, Bensen JT, Butler EN, Mohler JL, Taylor JA, Kogevinas M, Dierssen-Sotos T, Castaño-Vinyals G, Cannon-Albright L, Teerlink CC, Huff CD, Pilie P, Yu Y, Bohlender RJ, Gu J, Strom SS, Multigner L, Blanchet P, Brureau L, Kaneva R, Slavov C, Mitev V, Leach RJ, Brenner H, Chen X, Holleczek B, Schöttker B, Klein EA, Hsing AW, Kittles RA, Murphy AB, Logothetis CJ, Kim J, Neuhausen SL, Steele L, Ding YC, Isaacs WB, Nemesure B, Hennis AJ, Carpten J, Pandha H, Michael A, Ruyck KD, Meerleer GD, Ost P, Xu J, Razack A, Lim J, Teo SH, Newcomb LF, Lin DW, Fowke JH, Neslund-Dudas CM, Rybicki BA, Gamulin M, Lessel D, Kulis T, Usmani N, Abraham A, Singhal S, Parliament M, Claessens F, Joniau S, den Broeck TV, Gago-Dominguez M, Castelao JE, Martinez ME, Larkin S, Townsend PA, Aukim-Hastie C, Bush WS, Aldrich MC, Crawford DC, Srivastava S, Cullen J, Petrovics G, Casey G, Wang Y, Tettey Y, Lachance J, Tang W, Biritwum RB, Adjei AA, Tay E, Truelove A, Niwa S, Yamoah K, Govindasami K, Chokkalingam AP, Keaton JM, Hellwege JN, Clark PE, Jalloh M, Gueye SM, Niang L, Ogunbiyi O, Shittu O, Amodu O, Adebiyi AO, Aisuodionoe-Shadrach OI, Ajibola HO, Jamda MA, Oluwole OP, Nwegbu M, Adusei B, Mante S, Darkwa-Abrahams A, Diop H, Gundell SM, Roobol MJ, Jenster G, van Schaik RH, Hu JJ, Sanderson M, Kachuri L, Varma R, McKean-Cowdin R, Torres M, Preuss MH, Loos RJ, Zawistowski M, Zöllner S, Lu Z, Van Den Eeden SK, Easton DF, Ambs S, Edwards TL, Mägi R, Rebbeck TR, Fritsche L, Chanock SJ, Berndt SI, Wiklund F, Nakagawa H, Witte JS, Gaziano JM, Justice AC, Mancuso N, Terao C, Eeles RA, Kote-Jarai Z, Madduri RK, Conti DV, Haiman CA. Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants. Nat Genet 2023; 55:2065-2074. [PMID: 37945903 PMCID: PMC10841479 DOI: 10.1038/s41588-023-01534-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 09/15/2023] [Indexed: 11/12/2023]
Abstract
The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.
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Affiliation(s)
- Anqi Wang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jiayi Shen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | | | - Fei Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Rohini Janivara
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Burcu F. Darst
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Xin Sheng
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yili Xu
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alisha J. Chou
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sara Benlloch
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology,University of Cambridge, Cambridge, UK
| | | | | | - Anna Plym
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Urology Division, Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ali Sahimi
- Department of Population and Public Health Sciences, Keck School of Medicine,University of Southern California, Los Angeles, CA, USA
| | - Thomas J. Hoffman
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Atushi Takahashi
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genomic Medicine, National Cerebral and Cardiovascular Center Research Institute, Suita, Japan
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Laboratory of Clinical Genome Sequencing,Graduate school of Frontier Sciences,The University of Tokyo, Tokyo, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center of Integrative Medical Sciences, Yokohama, Japan
| | - Masashi Fujita
- Laboratory for Cancer Genomics, RIKEN Center of Integrative Medical Sciences, Yokohama, Japan
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jéssica Figuerêdo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Shuji Ito
- Department of Orthopaedics, Shimane University, Izumo, Shimane, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - The Biobank Japan Project
- Corresponding Author: Christopher A. Haiman, Harlyne J. Norris Cancer Research Tower, USC Norris Comprehensive Cancer Center, 1450 Biggy Street, Rm 1504, Los Angeles, CA 90033 or
| | - Yuji Uchio
- Department of Orthopaedics, Shimane University, Izumo, Shimane, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester, UK
| | - Peggy Wan
- Department of Population and Public Health Sciences, Keck School of Medicine,University of Southern California, Los Angeles, CA, USA
| | - Caroline Andrews
- Harvard TH Chan School of Public Health and Division of Population Sciences,Dana Farber Cancer Institute, Boston, MA, USA
| | - Adriana Lori
- Department of Population Science, American Cancer Society, Kennesaw, GA, USA
| | | | - Johanna Schleutker
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, Turku, Finland
| | | | - Csilla Sipeky
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Anssi Auvinen
- Unit of Health Sciences, Faculty of Social Sciences, Tampere University, Tampere, Finland
| | - Graham G. Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health,The University of Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Robert J. MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health,The University of Melbourne, Victoria, Australia
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Dominika Wokolorczyk
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Jan Lubinski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Christopher T. Rentsch
- Yale School of Medicine, New Haven, CT, USA
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Kelly Cho
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | | | - David E. Neal
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK
- University of Cambridge, Department of Oncology, Addenbrooke’s Hospital, Cambridge, UK
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK
| | - Jenny L. Donovan
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Freddie C. Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Richard M. Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Borge G. Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Copenhagen, Denmark
| | - Sune F. Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Copenhagen, Denmark
| | - Maren Weischer
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Copenhagen, Denmark
| | - Stig E. Bojesen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Copenhagen, Denmark
| | - Andreas Røder
- Copenhagen Prostate Cancer Center, Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Hein V. Stroomberg
- Copenhagen Prostate Cancer Center, Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Queensland, Australia
| | | | - Lisa Horvath
- Chris O’Brien Lifehouse (COBLH), Camperdown, Sydney, NSW, Australia, Sydney, Australia
- Garvan Institute of Medical Research, Sydney, Australia
| | - Judith A. Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Queensland, Australia
| | - Wayne Tilly
- Dame Roma Mitchell Cancer Research Laboratories, University of Adelaide, Adelaide, Australia
| | - Gail P. Risbridger
- Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
- Prostate Cancer Translational Research Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, and Department of Urology, Karolinska University Hospital, Solna, Stockholm, Sweden
- Department of Urology, Karolinska University Hospital, Stockholm, Sweden
| | - Robert Szulkin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- SDS Life Sciences, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Tobias Nordstrom
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Clinical Sciences at Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Nora Pashayan
- University College London, Department of Applied Health Research, London, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Cambridge, UK
- Department of Applied Health Research, University College London, London, UK
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Cambridge, UK
| | - Maya Ghoussaini
- Open Targets, Wellcome Sanger Institute, Hinxton, Saffron Walden, Hinxton, UK
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tim J. Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Thomas A. Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Hui-Yi Lin
- School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephanie Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Michael B. Cook
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH,, Bethesda, MD, USA
| | - Lorelei A. Mucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Edward Giovannucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sara Lindstrom
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - David J. Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kathryn L. Penney
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital/Harvard Medical School, Boston, MA, USA
| | - Constance Turman
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Catherine M. Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Phyllis J. Goodman
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ian M. Thompson
- CHRISTUS Santa Rosa Hospital – Medical Center, San Antonio, TX, USA
| | - Robert J. Hamilton
- Dept. of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, Canada
- Dept. of Surgery (Urology), University of Toronto, Toronto, Canada
| | - Neil E. Fleshner
- Dept. of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Antonio Finelli
- Division of Urology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Marie-Élise Parent
- Epidemiology and Biostatistics Unit, Centre Armand-Frappier Santé Biotechnologie, Laval, QC, Canada
| | - Janet L. Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Elaine A. Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Laura E. Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Meir Stampfer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital/Harvard Medical School, Boston, MA, USA
| | - Alicja Wolk
- Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Niclas Håkansson
- Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Gerald L. Andriole
- Brady Urological Institute in National Capital Region, Johns Hopkins University, Baltimore, MD, USA
| | - Robert N. Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Mitchell J. Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Karina Dalsgaard Sørensen
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Michael Borre
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Urology, Aarhus University Hospital, Aarhus, Denmark
| | - William J. Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- International Epidemiology Institute, Rockville, MD, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - James E. Mensah
- University of Ghana Medical School, Accra, Ghana
- Korle Bu Teaching Hospital, Accra, Ghana
| | - Yong-Jie Lu
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
| | | | - Ninghan Feng
- Wuxi Second Hospital, Nanjing Medical University, Wuxi, Jiangzhu Province, China
| | - Xueying Mao
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
| | - Yudong Wu
- Department of Urology, First Affiliated Hospital, The Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Shan-Chao Zhao
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zan Sun
- The People’s Hospital of Liaoning Proviouce, The People’s Hospital of China Medical University, Shenyang, China, Shenyang, China
| | - Stephen N. Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | - Daniel J. Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Catharine M.L. West
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Gill Barnett
- University of Cambridge Department of Oncology, Oncology Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | | | | | - Adam S. Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, Boston, MA, USA
| | | | - Olivier Cussenot
- GRC 5 Predictive Onco-Urology, Sorbonne Université, Paris, France
- CeRePP, Paris, France
| | | | - Florence Menegaux
- Exposome and Heredity, CESP (UMR 1018), Paris-Saclay Medical School, Paris-Saclay University, Inserm, Gustave Roussy, Villejuif, France
| | - Thérèse Truong
- Exposome and Heredity, CESP (UMR 1018), Paris-Saclay Medical School, Paris-Saclay University, Inserm, Gustave Roussy, Villejuif, France
| | - Yves Akoli Koudou
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif Cédex, France
| | - Esther M. John
- Department of Medicine, Stanford Cancer Institute,Stanford University School of Medicine, Stanford, CA, USA
| | | | - Lovise Maehle
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
| | - Sue A. Ingles
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Mariana C Stern
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Ana Vega
- Fundación Pública Galega Medicina Xenómica, Santiago De Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, Spain
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Spain
| | - Antonio Gómez-Caamaño
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
| | - Laura Fachal
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, Spain
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Spain
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Fundación Pública Galega Medicina Xenómica, Santiago de Compostela, Spain
| | - Barry S. Rosenstein
- Department of Radiation Oncology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah L. Kerns
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Harry Ostrer
- Professor of Pathology and Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Manuel R. Teixeira
- Department of Laboratory Genetics, Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center, Porto, Portugal
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center, Porto, Portugal
- School of Medicine and Biomedical Sciences (ICBAS), University of Porto, Porto, Portugal
| | - Paula Paulo
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center, Porto, Portugal
| | - Andreia Brandão
- Cancer Genetics Group, IPO Porto Research Center (CI-IPOP) / RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center, Porto, Portugal
| | | | | | - Jeannette T. Bensen
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ebonee N. Butler
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - James L. Mohler
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Jack A. Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
- Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Manolis Kogevinas
- ISGlobal, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Trinidad Dierssen-Sotos
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- University of Cantabria-IDIVAL, Santander, Spain
| | - Gemma Castaño-Vinyals
- ISGlobal, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Lisa Cannon-Albright
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Craig C. Teerlink
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Chad D. Huff
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Patrick Pilie
- Department of Genitourinary Medical Oncology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Yao Yu
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Ryan J. Bohlender
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Jian Gu
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Sara S. Strom
- The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Luc Multigner
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail), Rennes, France
| | - Pascal Blanchet
- CHU de Pointe-à-Pitre, Univ Antilles, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail), Pointe-à-Pitre, France
| | - Laurent Brureau
- CHU de Pointe-à-Pitre, Univ Antilles, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail), Pointe-à-Pitre, France
| | - Radka Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Chavdar Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University of Sofia, Sofia, Bulgaria
| | - Vanio Mitev
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Robin J. Leach
- Department of Cell Systems and Anatomy and Mays Cancer Center, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Xuechen Chen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eric A. Klein
- Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA
- Glickman Urological & Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ann W. Hsing
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Adam B. Murphy
- Department of Urology, Northwestern University, Chicago, IL, USA
| | - Christopher J. Logothetis
- The University of Texas M. D. Anderson Cancer Center, Department of Genitourinary Medical Oncology, Houston, TX, USA
| | - Jeri Kim
- The University of Texas M. D. Anderson Cancer Center, Department of Genitourinary Medical Oncology, Houston, TX, USA
| | - Susan L. Neuhausen
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - Linda Steele
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - Yuan Chun Ding
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - William B. Isaacs
- James Buchanan Brady Urological Institute, Johns Hopkins Hospital and Medical Institution, Baltimore, MD, USA
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Anselm J.M. Hennis
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
- Chronic Disease Research Centre and Faculty of Medical Sciences, University of the West Indies, Bridgetown, Barbados
| | - John Carpten
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | | | - Kim De Ruyck
- Ghent University, Faculty of Medicine and Health Sciences, Basic Medical Sciences, Ghent, Belgium
| | - Gert De Meerleer
- Ghent University Hospital, Department of Radiotherapy, Ghent, Belgium
| | - Piet Ost
- Ghent University Hospital, Department of Radiotherapy, Ghent, Belgium
| | - Jianfeng Xu
- Program for Personalized Cancer Care and Department of Surgery, NorthShore University HealthSystem, Evanston, IL, USA
| | - Azad Razack
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Jasmine Lim
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Soo-Hwang Teo
- Cancer Research Malaysia (CRM), Outpatient Centre, Subang Jaya Medical Centre, Subang Jaya, Selangor, Malaysia
| | - Lisa F. Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Daniel W. Lin
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Jay H. Fowke
- Department of Preventive Medicine, Division of Epidemiology,The University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - Benjamin A. Rybicki
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, Detroit, MI, USA
| | - Marija Gamulin
- Division of Medical Oncology, Urogenital Unit, Department of Oncology, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Davor Lessel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tomislav Kulis
- Department of Urology, University Hospital Center Zagreb, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
- Division of Radiation Oncology, Cross Cancer Institute, Edmonton, Alberta, Canada
| | - Aswin Abraham
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
- Division of Radiation Oncology, Cross Cancer Institute, Edmonton, Alberta, Canada
| | - Sandeep Singhal
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Matthew Parliament
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
- Division of Radiation Oncology, Cross Cancer Institute, Edmonton, Alberta, Canada
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, Leuven, Belgium
| | - Steven Joniau
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Thomas Van den Broeck
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, Leuven, Belgium
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, Servicio Galego de Saúde, SERGAS, Santiago de Compostela, Spain
- University of California San Diego, Moores Cancer Center, La Jolla, CA, USA
| | - Jose Esteban Castelao
- Genetic Oncology Unit, CHUVI Hospital, Complexo Hospitalario Universitario de Vigo, Instituto de Investigación Biomédica Galicia Sur (IISGS), Vigo (Pontevedra), Spain
| | - Maria Elena Martinez
- University of California San Diego, Moores Cancer Center, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Samantha Larkin
- Scientific Education Support, Thames Ditton, Surrey, Formerly Cancer Sciences, University of Southampton, Southampton, UK
| | - Paul A. Townsend
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
| | | | - William S. Bush
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Melinda C. Aldrich
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dana C. Crawford
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Shiv Srivastava
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, USA
| | - Jennifer Cullen
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
- Department of Surgery, Center for Prostate Disease Research,Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Gyorgy Petrovics
- Department of Surgery, Center for Prostate Disease Research,Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Graham Casey
- Department of Public Health Science, Center for Public Health Genomics,University of Virginia, Charlottesville, VA, USA
| | - Ying Wang
- Department of Population Science, American Cancer Society, Kennesaw, GA, USA
| | - Yao Tettey
- Korle Bu Teaching Hospital, Accra, Ghana
- Department of Pathology, University of Ghana, Accra, Ghana
| | - Joseph Lachance
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Wei Tang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | | | - Andrew A. Adjei
- Department of Pathology, University of Ghana Medical School, Accra, Ghana
| | - Evelyn Tay
- Korle Bu Teaching Hospital, Accra, Ghana
| | | | | | - Kosj Yamoah
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | - Jacob M. Keaton
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jacklyn N. Hellwege
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Peter E. Clark
- Atrium Health/Levine Cancer Institute, Charlotte, NC, USA
| | | | | | | | - Olufemi Ogunbiyi
- Department of Pathology, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Olayiwola Shittu
- Department of Surgery, College of Medicine, University of Ibadan and Univerity College Hospital, Ibadan, Nigeria
| | - Olukemi Amodu
- Institute of Child Health, College of Medicine, University of Ibadan and University College Hospital, Ibadan, Nigeria
| | - Akindele O. Adebiyi
- Clinical Epidemiology Unit, Department of Community Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oseremen I. Aisuodionoe-Shadrach
- College of Health Sciences, University of Abuja, University of Abuja Teaching Hospital and Cancer Science Center, Abuja, Nigeria
| | - Hafees O. Ajibola
- College of Health Sciences, University of Abuja, University of Abuja Teaching Hospital and Cancer Science Center, Abuja, Nigeria
| | - Mustapha A. Jamda
- College of Health Sciences, University of Abuja, University of Abuja Teaching Hospital and Cancer Science Center, Abuja, Nigeria
| | - Olabode P. Oluwole
- College of Health Sciences, University of Abuja, University of Abuja Teaching Hospital and Cancer Science Center, Abuja, Nigeria
| | - Maxwell Nwegbu
- College of Health Sciences, University of Abuja, University of Abuja Teaching Hospital and Cancer Science Center, Abuja, Nigeria
| | | | | | | | - Halimatou Diop
- Laboratoires Bacteriologie et Virologie, Hôpital Aristide Le Dantec, Dakar, Senegal
| | - Susan M. Gundell
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monique J. Roobol
- Department of Urology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Guido Jenster
- Department of Urology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Ron H.N. van Schaik
- Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Jennifer J. Hu
- The University of Miami School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL, USA
| | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford Cancer Institute, Stanford, CA, USA
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA, USA
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine,University of Southern California, Los Angeles, CA, USA
| | - Mina Torres
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA, USA
| | - Michael H. Preuss
- The Charles Bronfman Institute for Personalized Medicine,Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine,Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew Zawistowski
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine,University of Southern California, Los Angeles, CA, USA
| | | | - Douglas F. Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology,, Cambridge, UK
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Todd L. Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Timothy R. Rebbeck
- Harvard TH Chan School of Public Health and Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA, USA
| | - Lars Fritsche
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Hidewaki Nakagawa
- Laboratory for Cancer Genomics, RIKEN Center of Integrative Medical Sciences, Yokohama, Japan
| | - John S. Witte
- Department of Epidemiology and Population Health, Stanford Cancer Institute, Stanford, CA, USA
- Departments of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - J. Michael Gaziano
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | | | - Nick Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, School of Pharmaceutical Sciences, Shizuoka, Japan
| | - Rosalind A. Eeles
- The Institute of Cancer Research, London, UK
- Royal Marsden NHS Foundation Trust, London, UK
| | | | | | - David V. Conti
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Zhou F, Soremekun O, Chikowore T, Fatumo S, Barroso I, Morris AP, Asimit JL. Leveraging information between multiple population groups and traits improves fine-mapping resolution. Nat Commun 2023; 14:7279. [PMID: 37949886 PMCID: PMC10638399 DOI: 10.1038/s41467-023-43159-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.
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Affiliation(s)
- Feng Zhou
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Opeyemi Soremekun
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Segun Fatumo
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
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11
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Kamiza AB, Touré SM, Zhou F, Soremekun O, Cissé C, Wélé M, Touré AM, Nashiru O, Corpas M, Nyirenda M, Crampin A, Shaffer J, Doumbia S, Zeggini E, Morris AP, Asimit JL, Chikowore T, Fatumo S. Multi-trait discovery and fine-mapping of lipid loci in 125,000 individuals of African ancestry. Nat Commun 2023; 14:5403. [PMID: 37669986 PMCID: PMC10480211 DOI: 10.1038/s41467-023-41271-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/29/2023] [Indexed: 09/07/2023] Open
Abstract
Most genome-wide association studies (GWAS) for lipid traits focus on the separate analysis of lipid traits. Moreover, there are limited GWASs evaluating the genetic variants associated with multiple lipid traits in African ancestry. To further identify and localize loci with pleiotropic effects on lipid traits, we conducted a genome-wide meta-analysis, multi-trait analysis of GWAS (MTAG), and multi-trait fine-mapping (flashfm) in 125,000 individuals of African ancestry. Our meta-analysis and MTAG identified four and 14 novel loci associated with lipid traits, respectively. flashfm yielded an 18% mean reduction in the 99% credible set size compared to single-trait fine-mapping with JAM. Moreover, we identified more genetic variants with a posterior probability of causality >0.9 with flashfm than with JAM. In conclusion, we identified additional novel loci associated with lipid traits, and flashfm reduced the 99% credible set size to identify causal genetic variants associated with multiple lipid traits in African ancestry.
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Affiliation(s)
- Abram Bunya Kamiza
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Sounkou M Touré
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- African Center of Excellence in Bioinformatics, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Feng Zhou
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Opeyemi Soremekun
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Cheickna Cissé
- African Center of Excellence in Bioinformatics, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
- Faculty of Sciences and Techniques, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Mamadou Wélé
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
- Faculty of Sciences and Techniques, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Aboubacrine M Touré
- Faculty of Sciences and Techniques, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Oyekanmi Nashiru
- H3Africa Bioinformatics Network (H3ABioNet) Node, Center for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | - Manuel Corpas
- School of Life sciences, University of Westminster, London, UK
| | - Moffat Nyirenda
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe, Uganda
| | - Amelia Crampin
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
| | - Jeffrey Shaffer
- Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Seydou Doumbia
- African Center of Excellence in Bioinformatics, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
- Faculty of Medicine and Odonto-stomatology, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine, Translational Genomics, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
| | | | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Pediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Segun Fatumo
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda.
- H3Africa Bioinformatics Network (H3ABioNet) Node, Center for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria.
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
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Karhunen V, Launonen I, Järvelin MR, Sebert S, Sillanpää MJ. Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants. Bioinformatics 2023; 39:btad396. [PMID: 37348543 PMCID: PMC10326304 DOI: 10.1093/bioinformatics/btad396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 06/09/2023] [Accepted: 06/20/2023] [Indexed: 06/24/2023] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium patterns. RESULTS We present "FiniMOM" (fine-mapping using a product inverse-moment prior), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a nonlocal inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the linkage disequilibrium reference. The results of simulations studies aimed to mimic a typical GWAS on circulating protein levels show improved credible set coverage and power of the proposed method over current state-of-the-art fine-mapping method SuSiE, especially in the case of multiple causal variants within a locus. AVAILABILITY AND IMPLEMENTATION https://vkarhune.github.io/finimom/.
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Affiliation(s)
- Ville Karhunen
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, P.O.Box 8000, FI-90014, Finland
- Research Unit of Population Health, University of Oulu, Oulu, Finland
| | - Ilkka Launonen
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, P.O.Box 8000, FI-90014, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Department of Life Sciences, College of Health and Life Sciences, Brunel University, London, United Kingdom
| | - Sylvain Sebert
- Research Unit of Population Health, University of Oulu, Oulu, Finland
| | - Mikko J Sillanpää
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, P.O.Box 8000, FI-90014, Finland
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Guga S, Wang Y, Graham DC, Vyse TJ. A review of genetic risk in systemic lupus erythematosus. Expert Rev Clin Immunol 2023; 19:1247-1258. [PMID: 37496418 DOI: 10.1080/1744666x.2023.2240959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 05/10/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION Systemic Lupus Erythematosus (SLE) is a complex multisystem autoimmune disease with a wide range of signs and symptoms in affected individuals. The utilization of genome-wide association study (GWAS) technology has led to an explosion in the number of genetic risk factors mapped for autoimmune diseases, including SLE. AREAS COVERED In this review, we summarize the more recent genetic risk loci mapped in SLE, which bring the total number of loci mapped to approximately 200. We review prioritization analyses of the associated variants and experimental validation of the putative causal variants. This includes the implementation of new bioinformatic techniques to align genomic and functional data and the use of transcriptomics with single-cell RNA-sequencing, CRISPR genome editing, and Massive Parallel Reporter Assays to analyze non-coding regulatory genetics. EXPERT OPINION Despite progress in identifying more genetic risk loci and variant-gene pairs for SLE, understanding its pathogenesis and applying findings clinically remains challenging. The polygenic risk score (PRS) has been used as an application of SLE genetics, but with limited performance in non-EUR populations. In the next few years, advancements in proteomics, post-translational modification estimation, and whole-genome sequencing will enhance disease understanding.
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Affiliation(s)
- Suri Guga
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | - Yuxuan Wang
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | | | - Timothy J Vyse
- Department of Medical & Molecular Genetics, King's College London, London, UK
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14
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Yang Z, Wang C, Liu L, Khan A, Lee A, Vardarajan B, Mayeux R, Kiryluk K, Ionita-Laza I. CARMA is a new Bayesian model for fine-mapping in genome-wide association meta-analyses. Nat Genet 2023; 55:1057-1065. [PMID: 37169873 DOI: 10.1038/s41588-023-01392-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/11/2023] [Indexed: 05/13/2023]
Abstract
Fine-mapping is commonly used to identify putative causal variants at genome-wide significant loci. Here we propose a Bayesian model for fine-mapping that has several advantages over existing methods, including flexible specification of the prior distribution of effect sizes, joint modeling of summary statistics and functional annotations and accounting for discrepancies between summary statistics and external linkage disequilibrium in meta-analyses. Using simulations, we compare performance with commonly used fine-mapping methods and show that the proposed model has higher power and lower false discovery rate (FDR) when including functional annotations, and higher power, lower FDR and higher coverage for credible sets in meta-analyses. We further illustrate our approach by applying it to a meta-analysis of Alzheimer's disease genome-wide association studies where we prioritize putatively causal variants and genes.
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Affiliation(s)
- Zikun Yang
- Department of Biostatistics, Columbia University, New York City, NY, USA
| | - Chen Wang
- Department of Biostatistics, Columbia University, New York City, NY, USA
- Division of Nephrology Department of Medicine College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Linxi Liu
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Atlas Khan
- Division of Nephrology Department of Medicine College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Annie Lee
- Department of Neurology College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Badri Vardarajan
- Department of Neurology College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Richard Mayeux
- Department of Neurology College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology Department of Medicine College of Physicians and Surgeons, Columbia University, New York City, NY, USA
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15
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Bourgault J, Abner E, Manikpurage HD, Pujol-Gualdo N, Laisk T, Gobeil É, Gagnon E, Girard A, Mitchell PL, Thériault S, Esko T, Mathieu P, Arsenault BJ. Proteome-Wide Mendelian Randomization Identifies Causal Links Between Blood Proteins and Acute Pancreatitis. Gastroenterology 2023; 164:953-965.e3. [PMID: 36736436 DOI: 10.1053/j.gastro.2023.01.028] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/13/2023] [Accepted: 01/20/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND & AIMS Acute pancreatitis (AP) is a complex disease and the leading cause of gastrointestinal disease-related hospital admissions. Few therapeutic options exist for AP prevention. Blood proteins with causal evidence may represent promising drug targets, but few have been causally linked with AP. Our objective was to identify blood proteins linked with AP by combining genome-wide association meta-analysis and proteome-wide Mendelian randomization (MR) studies. METHODS We performed a genome-wide association meta-analysis totalling 10,630 patients with AP and 844,679 controls and a series of inverse-variance weighted MR analyses using cis-acting variants on 4719 blood proteins from the deCODE study (n = 35,559) and 4979 blood proteins from the Fenland study (n = 10,708). RESULTS The meta-analysis identified genome-wide significant variants (P <5 × 10-8) at 5 loci (ABCG5/8, TWIST2, SPINK1, PRSS2 and MORC4). The proteome-wide MR analyses identified 68 unique blood proteins that may causally be associated with AP, including 29 proteins validated in both data sets. Functional annotation of these proteins confirmed expression of many proteins in metabolic tissues responsible for digestion and energy metabolism, such as the esophagus, adipose tissue, and liver as well as acinar cells of the pancreas. Genetic colocalization and investigations into the druggable genome also identified potential drug targets for AP. CONCLUSIONS This large genome-wide association study meta-analysis for AP identified new variants linked with AP as well as several blood proteins that may be causally associated with AP. This study provides new information on the genetic architecture of this disease and identified pathways related to AP, which may be further explored as possible therapeutic targets for AP.
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Affiliation(s)
- Jérôme Bourgault
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada
| | - Erik Abner
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hasanga D Manikpurage
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada
| | - Natàlia Pujol-Gualdo
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Triin Laisk
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Émilie Gobeil
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada
| | - Eloi Gagnon
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada
| | - Arnaud Girard
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada
| | - Patricia L Mitchell
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada
| | - Sébastien Thériault
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada; Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Université Laval, Québec, Québec, Canada
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Patrick Mathieu
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada; Department of Surgery, Faculty of Medicine, Université Laval, Québec, Québec, Canada
| | - Benoit J Arsenault
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, Québec, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec, Québec, Canada.
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16
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Goodrich JA, Walker DI, He J, Lin X, Baumert BO, Hu X, Alderete TL, Chen Z, Valvi D, Fuentes ZC, Rock S, Wang H, Berhane K, Gilliland FD, Goran MI, Jones DP, Conti DV, Chatzi L. Metabolic Signatures of Youth Exposure to Mixtures of Per- and Polyfluoroalkyl Substances: A Multi-Cohort Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:27005. [PMID: 36821578 PMCID: PMC9945578 DOI: 10.1289/ehp11372] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 12/12/2022] [Accepted: 01/09/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous and has been associated with an increased risk of several cardiometabolic diseases. However, the metabolic pathways linking PFAS exposure and human disease are unclear. OBJECTIVE We examined associations of PFAS mixtures with alterations in metabolic pathways in independent cohorts of adolescents and young adults. METHODS Three hundred twelve overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) and 137 young adults from the Southern California Children's Health Study (CHS) were included in the analysis. Plasma PFAS and the metabolome were determined using liquid-chromatography/high-resolution mass spectrometry. A metabolome-wide association study was performed on log-transformed metabolites using Bayesian regression with a g-prior specification and g-computation for modeling exposure mixtures to estimate the impact of exposure to a mixture of six ubiquitous PFAS (PFOS, PFHxS, PFHpS, PFOA, PFNA, and PFDA). Pathway enrichment analysis was performed using Mummichog and Gene Set Enrichment Analysis. Significance across cohorts was determined using weighted Z -tests. RESULTS In the SOLAR and CHS cohorts, PFAS exposure was associated with alterations in tyrosine metabolism (meta-analysis p = 0.00002 ) and de novo fatty acid biosynthesis (p = 0.03 ), among others. For example, when increasing all PFAS in the mixture from low (∼ 30 th percentile) to high (∼ 70 th percentile), thyroxine (T4), a thyroid hormone related to tyrosine metabolism, increased by 0.72 standard deviations (SDs; equivalent to a standardized mean difference) in the SOLAR cohort (95% Bayesian credible interval (BCI): 0.00, 1.20) and 1.60 SD in the CHS cohort (95% BCI: 0.39, 2.80). Similarly, when going from low to high PFAS exposure, arachidonic acid increased by 0.81 SD in the SOLAR cohort (95% BCI: 0.37, 1.30) and 0.67 SD in the CHS cohort (95% BCI: 0.00, 1.50). In general, no individual PFAS appeared to drive the observed associations. DISCUSSION Exposure to PFAS is associated with alterations in amino acid metabolism and lipid metabolism in adolescents and young adults. https://doi.org/10.1289/EHP11372.
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Affiliation(s)
- Jesse A. Goodrich
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Douglas I. Walker
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Jingxuan He
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Xiangping Lin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Brittney O. Baumert
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Xin Hu
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, Georgia, USA
| | - Tanya L. Alderete
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Damaskini Valvi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zoe C. Fuentes
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sarah Rock
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Hongxu Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Kiros Berhane
- Department of Biostatistics, Columbia University, New York, New York, USA
| | - Frank D. Gilliland
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Michael I. Goran
- Department of Pediatrics, Children’s Hospital Los Angeles, Saban Research Institute, Los Angeles, California, USA
| | - Dean P. Jones
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, Georgia, USA
| | - David V. Conti
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Leda Chatzi
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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17
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Zhou F, Butterworth AS, Asimit JL. Flashfm-ivis: interactive visualization for fine-mapping of multiple quantitative traits. Bioinformatics 2022; 38:4238-4242. [PMID: 35792838 PMCID: PMC9438951 DOI: 10.1093/bioinformatics/btac453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/31/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY flashfm-ivis provides a suite of interactive visualization plots to view potential causal genetic variants that underlie associations that are shared or distinct between multiple quantitative traits and compares results between single- and multi-trait fine-mapping. Unique features include network diagrams that show joint effects between variants for each trait and regional association plots that integrate fine-mapping results, all with user-controlled zoom features for an interactive exploration of potential causal variants across traits. AVAILABILITY AND IMPLEMENTATION flashfm-ivis is an open-source software under the MIT license. It is available as an interactive web-based tool (http://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/) and as an R package. Code and documentation are available at https://github.com/fz-cambridge/flashfm-ivis and https://zenodo.org/record/6376244#.YjnarC-l2X0. Additional features can be downloaded as standalone R libraries to encourage reuse. SUPPLEMENTARY INFORMATION Supplementary information are available at Bioinformatics online.
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Affiliation(s)
- Feng Zhou
- To whom correspondence should be addressed. or
| | - Adam S Butterworth
- British Heart Foundation, Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK,National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics University of Cambridge, Cambridge CB1 8RN, UK,British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge CB1 8RN, UK,Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
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18
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Fine-mapping from summary data with the "Sum of Single Effects" model. PLoS Genet 2022; 18:e1010299. [PMID: 35853082 PMCID: PMC9337707 DOI: 10.1371/journal.pgen.1010299] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 07/29/2022] [Accepted: 06/17/2022] [Indexed: 11/19/2022] Open
Abstract
In recent work, Wang et al introduced the "Sum of Single Effects" (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.
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19
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Kocheril PA, Moore SC, Lenz KD, Mukundan H, Lilley LM. Progress Toward a Multiomic Understanding of Traumatic Brain Injury: A Review. Biomark Insights 2022; 17:11772719221105145. [PMID: 35719705 PMCID: PMC9201320 DOI: 10.1177/11772719221105145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/17/2022] [Indexed: 12/11/2022] Open
Abstract
Traumatic brain injury (TBI) is not a single disease state but describes an array
of conditions associated with insult or injury to the brain. While some
individuals with TBI recover within a few days or months, others present with
persistent symptoms that can cause disability, neuropsychological trauma, and
even death. Understanding, diagnosing, and treating TBI is extremely complex for
many reasons, including the variable biomechanics of head impact, differences in
severity and location of injury, and individual patient characteristics. Because
of these confounding factors, the development of reliable diagnostics and
targeted treatments for brain injury remains elusive. We argue that the
development of effective diagnostic and therapeutic strategies for TBI requires
a deep understanding of human neurophysiology at the molecular level and that
the framework of multiomics may provide some effective solutions for the
diagnosis and treatment of this challenging condition. To this end, we present
here a comprehensive review of TBI biomarker candidates from across the
multiomic disciplines and compare them with known signatures associated with
other neuropsychological conditions, including Alzheimer’s disease and
Parkinson’s disease. We believe that this integrated view will facilitate a
deeper understanding of the pathophysiology of TBI and its potential links to
other neurological diseases.
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Affiliation(s)
- Philip A Kocheril
- Physical Chemistry and Applied Spectroscopy Group, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Shepard C Moore
- Physical Chemistry and Applied Spectroscopy Group, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Kiersten D Lenz
- Physical Chemistry and Applied Spectroscopy Group, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Harshini Mukundan
- Physical Chemistry and Applied Spectroscopy Group, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Laura M Lilley
- Physical Chemistry and Applied Spectroscopy Group, Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM, USA
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20
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Schaid DJ, Sinnwell JP, Batzler A, McDonnell SK. Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk. Am J Hum Genet 2022; 109:900-908. [PMID: 35353984 PMCID: PMC9118111 DOI: 10.1016/j.ajhg.2022.03.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/09/2022] [Indexed: 12/14/2022] Open
Abstract
Polygenic risk scores (PRSs) for a variety of diseases have recently been shown to have relative risks that depend on age, and genetic relative risks decrease with increasing age. A refined understanding of the age dependency of PRSs for a disease is important for personalized risk predictions and risk stratification. To further evaluate how the PRS relative risk for prostate cancer depends on age, we refined analyses for a validated PRS for prostate cancer by using 64,274 prostate cancer cases and 46,432 controls of diverse ancestry (82.8% European, 9.8% African American, 3.8% Latino, 2.8% Asian, and 0.8% Ghanaian). Our strategy applied a novel weighted proportional hazards model to case-control data to fully utilize age to refine how the relative risk decreased with age. We found significantly greater relative risks for younger men (age 30-55 years) compared with older men (70-88 years) for both relative risk per standard deviation of the PRS and dichotomized according to the upper 90th percentile of the PRS distribution. For the largest European ancestral group that could provide reliable resolution, the log-relative risk decreased approximately linearly from age 50 to age 75. Despite strong evidence of age-dependent genetic relative risk, our results suggest that absolute risk predictions differed little from predictions that assumed a constant relative risk over ages, from short-term to long-term predictions, simplifying implementation of risk discussions into clinical practice.
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Affiliation(s)
- Daniel J. Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA,Corresponding author
| | - Jason P. Sinnwell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
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21
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Wang QS, Huang H. Methods for statistical fine-mapping and their applications to auto-immune diseases. Semin Immunopathol 2022; 44:101-113. [PMID: 35041074 PMCID: PMC8837575 DOI: 10.1007/s00281-021-00902-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.
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Affiliation(s)
- Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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22
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Ken-Dror G, Cotlarciuc I, Martinelli I, Grandone E, Hiltunen S, Lindgren E, Margaglione M, Duchez VLC, Triquenot AB, Zedde M, Mancuso M, Ruigrok YM, Marjot T, Worrall B, Majersik JJ, Metso TM, Putaala J, Haapaniemi E, Zuurbier SM, Brouwer MC, Passamonti SM, Abbattista M, Bucciarelli P, Mitchell BD, Kittner SJ, Lemmens R, Jern C, Pappalardo E, Costa P, Colombi M, Aguiar de Sousa D, Rodrigues S, Canhão P, Tkach A, Santacroce R, Favuzzi G, Arauz A, Colaizzo D, Spengos K, Hodge A, Ditta R, Pezzini A, Debette S, Coutinho JM, Thijs V, Jood K, Pare G, Tatlisumak T, Ferro JM, Sharma P. Genome-Wide Association Study Identifies First Locus Associated with Susceptibility to Cerebral Venous Thrombosis. Ann Neurol 2021; 90:777-788. [PMID: 34459509 PMCID: PMC8666091 DOI: 10.1002/ana.26205] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/23/2021] [Accepted: 08/23/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Cerebral venous thrombosis (CVT) is an uncommon form of stroke affecting mostly young individuals. Although genetic factors are thought to play a role in this cerebrovascular condition, its genetic etiology is not well understood. METHODS A genome-wide association study was performed to identify genetic variants influencing susceptibility to CVT. A 2-stage genome-wide study was undertaken in 882 Europeans diagnosed with CVT and 1,205 ethnicity-matched control subjects divided into discovery and independent replication datasets. RESULTS In the overall case-control cohort, we identified highly significant associations with 37 single nucleotide polymorphisms (SNPs) within the 9q34.2 region. The strongest association was with rs8176645 (combined p = 9.15 × 10-24 ; odds ratio [OR] = 2.01, 95% confidence interval [CI] = 1.76-2.31). The discovery set findings were validated across an independent European cohort. Genetic risk score for this 9q34.2 region increases CVT risk by a pooled estimate OR = 2.65 (95% CI = 2.21-3.20, p = 2.00 × 10-16 ). SNPs within this region were in strong linkage disequilibrium (LD) with coding regions of the ABO gene. The ABO blood group was determined using allele combination of SNPs rs8176746 and rs8176645. Blood groups A, B, or AB, were at 2.85 times (95% CI = 2.32-3.52, p = 2.00 × 10-16 ) increased risk of CVT compared with individuals with blood group O. INTERPRETATION We present the first chromosomal region to robustly associate with a genetic susceptibility to CVT. This region more than doubles the likelihood of CVT, a risk greater than any previously identified thrombophilia genetic risk marker. That the identified variant is in strong LD with the coding region of the ABO gene with differences in blood group prevalence provides important new insights into the pathophysiology of CVT. ANN NEUROL 2021;90:777-788.
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Affiliation(s)
- Gie Ken-Dror
- Institute of Cardiovascular Research Royal Holloway, University of London (ICR2UL), London, UK
| | - Ioana Cotlarciuc
- Institute of Cardiovascular Research Royal Holloway, University of London (ICR2UL), London, UK
| | - Ida Martinelli
- A. Bianchi Bonomi Hemophilia and Thrombosis Center, Fondazione IRCCS Ca’Granda – Ospedale Maggiore Policlinico, Milan, Italy
| | - Elvira Grandone
- Atherosclerosis and Thrombosis Unit, I.R.C.C.S. Casa Sollievo della Sofferenza, S. Giovanni Rotondo, Foggia, Italy
- Ob/Gyn Dept. The First I.M. Sechenov Moscow State Medical University
| | - Sini Hiltunen
- Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Erik Lindgren
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Maurizio Margaglione
- Medical Genetics, Dept. of Clinical and Experimental Medicine, University of Foggia, Italy
| | - Veronique Le Cam Duchez
- Normandie University, UNIROUEN, INSERM U1096, Rouen University Hospital, Vascular Hemostasis Unit and Inserm CIC-CRB 1404, F 76000 Rouen, France
| | | | - Marialuisa Zedde
- Neurology Unit, Stroke Unit, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Italy
| | - Michelangelo Mancuso
- Department of Molecular and Translational Medicine, Division of Biology and Genetics, University of Brescia, Italy
| | - Ynte M Ruigrok
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, the Netherlands
| | - Thomas Marjot
- Oxford Liver Unit, Translational Gastroenterology Unit, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Brad Worrall
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
| | | | - Tiina M. Metso
- Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Jukka Putaala
- Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Elena Haapaniemi
- Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Susanna M. Zuurbier
- Department of Neurology, Amsterdam University Medical Centers, location AMC, Amsterdam Neuroscience, University of Amsterdam, the Netherlands
| | - Matthijs C. Brouwer
- Department of Neurology, Amsterdam University Medical Centers, location AMC, Amsterdam Neuroscience, University of Amsterdam, the Netherlands
| | - Serena M. Passamonti
- A. Bianchi Bonomi Hemophilia and Thrombosis Center, Fondazione IRCCS Ca’Granda – Ospedale Maggiore Policlinico, Milan, Italy
| | - Maria Abbattista
- A. Bianchi Bonomi Hemophilia and Thrombosis Center, Fondazione IRCCS Ca’Granda – Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Bucciarelli
- A. Bianchi Bonomi Hemophilia and Thrombosis Center, Fondazione IRCCS Ca’Granda – Ospedale Maggiore Policlinico, Milan, Italy
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Steven J. Kittner
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Neurology, Veterans Affairs Medical Center, Baltimore, MD, USA
| | - Robin Lemmens
- KU Leuven – University of Leuven, Department of Neurosciences, Experimental Neurology; VIB Center for Brain & Disease Research; University Hospitals Leuven, Department of Neurology, Leuven, Belgium
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Emanuela Pappalardo
- A. Bianchi Bonomi Hemophilia and Thrombosis Center, Fondazione IRCCS Ca’Granda – Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Costa
- Department of Clinical and Experimental Sciences, Neurology Clinic, University of Brescia, Italy
| | - Marina Colombi
- Department of Molecular and Translational Medicine, Division of Biology and Genetics, University of Brescia, Italy
| | - Diana Aguiar de Sousa
- Department of Neurosciences, Hospital de Santa Maria, University of Lisbon, Lisbon, Portugal
| | - Sofia Rodrigues
- Department of Neurosciences, Hospital de Santa Maria, University of Lisbon, Lisbon, Portugal
| | - Patrícia Canhão
- Department of Neurosciences, Hospital de Santa Maria, University of Lisbon, Lisbon, Portugal
| | - Aleksander Tkach
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Rosa Santacroce
- Medical Genetics, Dept. of Clinical and Experimental Medicine, University of Foggia, Italy
| | - Giovanni Favuzzi
- Atherosclerosis and Thrombosis Unit, I.R.C.C.S. Casa Sollievo della Sofferenza, S. Giovanni Rotondo, Foggia, Italy
| | - Antonio Arauz
- Stroke Clinic, National Institute of Neurology and Neurosurgery Manuel Velasco Suarez, Mexico City, Mexico
| | - Donatella Colaizzo
- Atherosclerosis and Thrombosis Unit, I.R.C.C.S. Casa Sollievo della Sofferenza, S. Giovanni Rotondo, Foggia, Italy
| | - Kostas Spengos
- Department of Neurology, University of Athens School of Medicine, Eginition Hospital, Athens, Greece
| | - Amanda Hodge
- McMaster University, Pathology and Molecular Medicine. Population Health Research Institute and Thrombosis and Atherosclerosis Research Institute, Hamilton Health Sciences, Ontario, Canada
| | - Reina Ditta
- McMaster University, Pathology and Molecular Medicine. Population Health Research Institute and Thrombosis and Atherosclerosis Research Institute, Hamilton Health Sciences, Ontario, Canada
| | - Alessandro Pezzini
- Department of Molecular and Translational Medicine, Division of Biology and Genetics, University of Brescia, Italy
| | - Stephanie Debette
- Department of Neurology, Bordeaux University Hospital, Bordeaux University, France
| | - Jonathan M. Coutinho
- Department of Neurology, Amsterdam University Medical Centers, location AMC, Amsterdam Neuroscience, University of Amsterdam, the Netherlands
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria, Australia
| | - Katarina Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Guillaume Pare
- McMaster University, Pathology and Molecular Medicine. Population Health Research Institute and Thrombosis and Atherosclerosis Research Institute, Hamilton Health Sciences, Ontario, Canada
| | - Turgut Tatlisumak
- Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - José M. Ferro
- Department of Neurosciences, Hospital de Santa Maria, University of Lisbon, Lisbon, Portugal
| | - Pankaj Sharma
- Institute of Cardiovascular Research Royal Holloway, University of London (ICR2UL), London, UK
- Department of Clinical Neuroscience, Imperial College Healthcare NHS Trust, London
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23
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Hernández N, Soenksen J, Newcombe P, Sandhu M, Barroso I, Wallace C, Asimit JL. The flashfm approach for fine-mapping multiple quantitative traits. Nat Commun 2021; 12:6147. [PMID: 34686674 PMCID: PMC8536717 DOI: 10.1038/s41467-021-26364-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 10/04/2021] [Indexed: 11/08/2022] Open
Abstract
Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.
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Affiliation(s)
- N Hernández
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J Soenksen
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
- School of Life Sciences, University of Glasgow, Glasgow, UK
| | - P Newcombe
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - M Sandhu
- Dept of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - I Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - C Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, Cambridge, UK
| | - J L Asimit
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
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24
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Gkatzionis A, Burgess S, Conti DV, Newcombe PJ. Bayesian variable selection with a pleiotropic loss function in Mendelian randomization. Stat Med 2021; 40:5025-5045. [PMID: 34155684 PMCID: PMC8446304 DOI: 10.1002/sim.9109] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 04/17/2021] [Accepted: 06/07/2021] [Indexed: 01/04/2023]
Abstract
Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a recently proposed theoretical extension of the traditional Bayesian statistics framework, including a loss function to penalize genetic variants that exhibit pleiotropic effects. The algorithm offers robust inference through the use of model averaging, as we illustrate by running it on a range of simulation scenarios and comparing it against established pleiotropy-robust Mendelian randomization methods. In a real-data application, we study the effect of systolic and diastolic blood pressure on the risk of suffering from coronary heart disease (CHD). Based on a recent large-scale GWAS for blood pressure, we use 395 genetic variants for systolic and 391 variants for diastolic blood pressure. Both traits are shown to have significant risk-increasing effects on CHD risk.
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Affiliation(s)
- Apostolos Gkatzionis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - David V. Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
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25
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LaPierre N, Taraszka K, Huang H, He R, Hormozdiari F, Eskin E. Identifying causal variants by fine mapping across multiple studies. PLoS Genet 2021; 17:e1009733. [PMID: 34543273 PMCID: PMC8491908 DOI: 10.1371/journal.pgen.1009733] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/05/2021] [Accepted: 07/21/2021] [Indexed: 11/18/2022] Open
Abstract
Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of "fine mapping" methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).
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Affiliation(s)
- Nathan LaPierre
- Department of Computer Science, University of California, Los Angeles, California, United States
| | - Kodi Taraszka
- Department of Computer Science, University of California, Los Angeles, California, United States
| | - Helen Huang
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, United States
| | - Rosemary He
- Department of Mathematics, University of California, Los Angeles, California, United States
| | - Farhad Hormozdiari
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, California, United States
- Department of Human Genetics, University of California, Los Angeles, California, United States
- Department of Computational Medicine, University of California, Los Angeles, California, United States
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26
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Wallace C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet 2021; 17:e1009440. [PMID: 34587156 PMCID: PMC8504726 DOI: 10.1371/journal.pgen.1009440] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 10/11/2021] [Accepted: 09/12/2021] [Indexed: 01/05/2023] Open
Abstract
In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.
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Affiliation(s)
- Chris Wallace
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, United Kingdom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
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Horn AL, Jiang L, Washburn F, Hvitfeldt E, de la Haye K, Nicholas W, Simon P, Pentz M, Cozen W, Sood N, Conti DV. An integrated risk and epidemiological model to estimate risk-stratified COVID-19 outcomes for Los Angeles County: March 1, 2020-March 1, 2021. PLoS One 2021; 16:e0253549. [PMID: 34166416 PMCID: PMC8224896 DOI: 10.1371/journal.pone.0253549] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 06/07/2021] [Indexed: 01/08/2023] Open
Abstract
The objective of this study was to use available data on the prevalence of COVID-19 risk factors in subpopulations and epidemic dynamics at the population level to estimate probabilities of severe illness and the case and infection fatality rates (CFR and IFR) stratified across subgroups representing all combinations of the risk factors age, comorbidities, obesity, and smoking status. We focus on the first year of the epidemic in Los Angeles County (LAC) (March 1, 2020-March 1, 2021), spanning three epidemic waves. A relative risk modeling approach was developed to estimate conditional effects from available marginal data. A dynamic stochastic epidemic model was developed to produce time-varying population estimates of epidemic parameters including the transmission and infection observation rate. The epidemic and risk models were integrated to produce estimates of subpopulation-stratified probabilities of disease progression and CFR and IFR for LAC. The probabilities of disease progression and CFR and IFR were found to vary as extensively between age groups as within age categories combined with the presence of absence of other risk factors, suggesting that it is inappropriate to summarize epidemiological parameters for age categories alone, let alone the entire population. The fine-grained subpopulation-stratified estimates of COVID-19 outcomes produced in this study are useful in understanding disparities in the effect of the epidemic on different groups in LAC, and can inform analyses of targeted subpopulation-level policy interventions.
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Affiliation(s)
- Abigail L. Horn
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Lai Jiang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Faith Washburn
- Los Angeles County Department of Public Health, Los Angeles, CA, United States of America
| | - Emil Hvitfeldt
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Kayla de la Haye
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - William Nicholas
- Los Angeles County Department of Public Health, Los Angeles, CA, United States of America
| | - Paul Simon
- Los Angeles County Department of Public Health, Los Angeles, CA, United States of America
| | - Maryann Pentz
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Wendy Cozen
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Neeraj Sood
- Sol Price School of Public Policy, University of Southern California, Los Angeles, CA, United States of America
- Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA, United States of America
| | - David V. Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
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28
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Jiang L, Xu S, Mancuso N, Newcombe PJ, Conti DV. A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis. Am J Epidemiol 2021; 190:1148-1158. [PMID: 33404048 DOI: 10.1093/aje/kwaa287] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 12/23/2022] Open
Abstract
Previous research has demonstrated the usefulness of hierarchical modeling for incorporating a flexible array of prior information in genetic association studies. When this prior information consists of estimates from association analyses of single-nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a 2-stage instrumental or transcriptome-wide association study (TWAS) analysis, respectively. We propose to extend our previous approach for the joint analysis of marginal summary statistics to incorporate prior information via a hierarchical model (hJAM). In this framework, the use of appropriate estimates as prior information yields an analysis similar to Mendelian randomization (MR) and TWAS approaches. hJAM is applicable to multiple correlated SNPs and intermediates to yield conditional estimates for the intermediates on the outcome, thus providing advantages over alternative approaches. We investigated the performance of hJAM in comparison with existing MR and TWAS approaches and demonstrated that hJAM yields an unbiased estimate, maintains correct type-I error, and has increased power across extensive simulations. We applied hJAM to 2 examples: estimating the causal effects of body mass index (GIANT Consortium) and type 2 diabetes (DIAGRAM data set, GERA Cohort, and UK Biobank) on myocardial infarction (UK Biobank) and estimating the causal effects of the expressions of the genes for nuclear casein kinase and cyclin dependent kinase substrate 1 and peptidase M20 domain containing 1 on the risk of prostate cancer (PRACTICAL and GTEx).
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29
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Sobczyk MK, Richardson TG, Zuber V, Min JL, Gaunt TR, Paternoster L. Triangulating molecular evidence to prioritize candidate causal genes at established atopic dermatitis loci. J Invest Dermatol 2021; 141:2620-2629. [PMID: 33901562 PMCID: PMC8592116 DOI: 10.1016/j.jid.2021.03.027] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/12/2021] [Accepted: 03/25/2021] [Indexed: 01/16/2023]
Abstract
Genome-wide association studies for atopic dermatitis (AD) have identified 25 reproducible loci. We attempt to prioritize candidate causal genes at these loci using extensive molecular resources compiled into a bioinformatics pipeline. We identified a list of 103 molecular resources for AD aetiology, including expression, protein and DNA methylation QTL datasets in skin or immune-relevant tissues which were tested for overlap with GWAS signals. This was combined with functional annotation using regulatory variant prediction, and features such as promoter-enhancer interactions, expression studies and variant fine-mapping. For each gene at each locus, we condensed the evidence into a prioritization score. Across the investigated loci, we detected significant enrichment of genes with adaptive immune regulatory function and epidermal barrier formation among the top prioritized genes. At 8 loci, we were able to prioritize a single candidate gene (IL6R, ADO, PRR5L, IL7R, ETS1, INPP5D, MDM1, TRAF3). In addition, at 6 of the 25 loci, our analysis prioritizes less familiar candidates (SLC22A5, IL2RA, MDM1, DEXI, ADO, STMN3). Our analysis provides support for previously implicated genes at several AD GWAS loci, as well as evidence for plausible additional candidates at others, which may represent potential targets for drug discovery.
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Affiliation(s)
- Maria K Sobczyk
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Josine L Min
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
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30
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Conti DV, Darst BF, Moss LC, Saunders EJ, Sheng X, Chou A, Schumacher FR, Olama AAA, Benlloch S, Dadaev T, Brook MN, Sahimi A, Hoffmann TJ, Takahashi A, Matsuda K, Momozawa Y, Fujita M, Muir K, Lophatananon A, Wan P, Le Marchand L, Wilkens LR, Stevens VL, Gapstur SM, Carter BD, Schleutker J, Tammela TLJ, Sipeky C, Auvinen A, Giles GG, Southey MC, MacInnis RJ, Cybulski C, Wokołorczyk D, Lubiński J, Neal DE, Donovan JL, Hamdy FC, Martin RM, Nordestgaard BG, Nielsen SF, Weischer M, Bojesen SE, Røder MA, Iversen P, Batra J, Chambers S, Moya L, Horvath L, Clements JA, Tilley W, Risbridger GP, Gronberg H, Aly M, Szulkin R, Eklund M, Nordström T, Pashayan N, Dunning AM, Ghoussaini M, Travis RC, Key TJ, Riboli E, Park JY, Sellers TA, Lin HY, Albanes D, Weinstein SJ, Mucci LA, Giovannucci E, Lindstrom S, Kraft P, Hunter DJ, Penney KL, Turman C, Tangen CM, Goodman PJ, Thompson IM, Hamilton RJ, Fleshner NE, Finelli A, Parent MÉ, Stanford JL, Ostrander EA, Geybels MS, Koutros S, Freeman LEB, Stampfer M, Wolk A, Håkansson N, Andriole GL, Hoover RN, Machiela MJ, Sørensen KD, Borre M, Blot WJ, Zheng W, Yeboah ED, Mensah JE, Lu YJ, Zhang HW, Feng N, Mao X, Wu Y, Zhao SC, Sun Z, Thibodeau SN, McDonnell SK, Schaid DJ, West CML, Burnet N, Barnett G, Maier C, Schnoeller T, Luedeke M, Kibel AS, Drake BF, Cussenot O, Cancel-Tassin G, Menegaux F, Truong T, Koudou YA, John EM, Grindedal EM, Maehle L, Khaw KT, Ingles SA, Stern MC, Vega A, Gómez-Caamaño A, Fachal L, Rosenstein BS, Kerns SL, Ostrer H, Teixeira MR, Paulo P, Brandão A, Watya S, Lubwama A, Bensen JT, Fontham ETH, Mohler J, Taylor JA, Kogevinas M, Llorca J, Castaño-Vinyals G, Cannon-Albright L, Teerlink CC, Huff CD, Strom SS, Multigner L, Blanchet P, Brureau L, Kaneva R, Slavov C, Mitev V, Leach RJ, Weaver B, Brenner H, Cuk K, Holleczek B, Saum KU, Klein EA, Hsing AW, Kittles RA, Murphy AB, Logothetis CJ, Kim J, Neuhausen SL, Steele L, Ding YC, Isaacs WB, Nemesure B, Hennis AJM, Carpten J, Pandha H, Michael A, De Ruyck K, De Meerleer G, Ost P, Xu J, Razack A, Lim J, Teo SH, Newcomb LF, Lin DW, Fowke JH, Neslund-Dudas C, Rybicki BA, Gamulin M, Lessel D, Kulis T, Usmani N, Singhal S, Parliament M, Claessens F, Joniau S, Van den Broeck T, Gago-Dominguez M, Castelao JE, Martinez ME, Larkin S, Townsend PA, Aukim-Hastie C, Bush WS, Aldrich MC, Crawford DC, Srivastava S, Cullen JC, Petrovics G, Casey G, Roobol MJ, Jenster G, van Schaik RHN, Hu JJ, Sanderson M, Varma R, McKean-Cowdin R, Torres M, Mancuso N, Berndt SI, Van Den Eeden SK, Easton DF, Chanock SJ, Cook MB, Wiklund F, Nakagawa H, Witte JS, Eeles RA, Kote-Jarai Z, Haiman CA. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat Genet 2021; 53:65-75. [PMID: 33398198 PMCID: PMC8148035 DOI: 10.1038/s41588-020-00748-0] [Citation(s) in RCA: 246] [Impact Index Per Article: 82.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 11/05/2020] [Indexed: 01/28/2023]
Abstract
Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.
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Affiliation(s)
- David V Conti
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Burcu F Darst
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Lilit C Moss
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | | | - Xin Sheng
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Alisha Chou
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
- Seidman Cancer Center, University Hospitals, Cleveland, OH, USA
| | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Sara Benlloch
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | | | | | - Ali Sahimi
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Atushi Takahashi
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genomic Medicine, National Cerebral and Cardiovascular Center Research Institute, Suita, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Biobank, Tokyo, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center of Integrative Medical Sciences, Yokohama, Japan
| | - Masashi Fujita
- Laboratory for Cancer Genomics, RIKEN Center of Integrative Medical Sciences, Yokohama, Japan
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Peggy Wan
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Lynne R Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Victoria L Stevens
- Behavioral and Epidemiology Research Group, Research Program, American Cancer Society, Atlanta, GA, USA
| | - Susan M Gapstur
- Behavioral and Epidemiology Research Group, Research Program, American Cancer Society, Atlanta, GA, USA
| | - Brian D Carter
- Behavioral and Epidemiology Research Group, Research Program, American Cancer Society, Atlanta, GA, USA
| | - Johanna Schleutker
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, Turku, Finland
| | - Teuvo L J Tammela
- Department of Urology, Tampere University Hospital and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Csilla Sipeky
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Anssi Auvinen
- Unit of Health Sciences, Faculty of Social Sciences, Tampere University, Tampere, Finland
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Dominika Wokołorczyk
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Jan Lubiński
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - David E Neal
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
- University of Cambridge, Department of Oncology, Addenbrooke's Hospital, Cambridge, UK
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK
| | - Jenny L Donovan
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Richard M Martin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research (NIHR) Biomedical Research Centre, University of Bristol, Bristol, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Sune F Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Maren Weischer
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Stig E Bojesen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Martin Andreas Røder
- Copenhagen Prostate Cancer Center, Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Peter Iversen
- Copenhagen Prostate Cancer Center, Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Queensland, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Translational Research Institute, Brisbane, Queensland, Australia
| | | | - Leire Moya
- Australian Prostate Cancer Research Centre-Queensland, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Translational Research Institute, Brisbane, Queensland, Australia
| | - Lisa Horvath
- Chris O'Brien Lifehouse (COBLH), Camperdown, Sydney, New South Wales, Australia
- Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Judith A Clements
- Australian Prostate Cancer Research Centre-Queensland, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Translational Research Institute, Brisbane, Queensland, Australia
| | - Wayne Tilley
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Gail P Risbridger
- Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
- Prostate Cancer Translational Research Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, and Department of Urology, Karolinska University Hospital, Solna, Stockholm, Sweden
- Department of Urology, Karolinska University Hospital, Stockholm, Sweden
| | - Robert Szulkin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- SDS Life Science, Danderyd, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Tobias Nordström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Clinical Sciences at Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Cambridge, UK
- Department of Applied Health Research, University College London, London, UK
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Cambridge, UK
| | | | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tim J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Thomas A Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Hui-Yi Lin
- School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Edward Giovannucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sara Lindstrom
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - David J Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kathryn L Penney
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA
| | - Constance Turman
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Phyllis J Goodman
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ian M Thompson
- CHRISTUS Santa Rosa Hospital - Medical Center, San Antonio, TX, USA
| | - Robert J Hamilton
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Surgery (Urology), University of Toronto, Toronto, Ontario, Canada
| | - Neil E Fleshner
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Antonio Finelli
- Division of Urology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Marie-Élise Parent
- Epidemiology and Biostatistics Unit, Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique, Laval, Quebec, Canada
- Department of Social and Preventive Medicine, School of Public Health, University of Montreal, Montreal, Quebec, Canada
| | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Milan S Geybels
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Laura E Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Meir Stampfer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA
| | - Alicja Wolk
- Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Niclas Håkansson
- Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Karina Dalsgaard Sørensen
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Michael Borre
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Urology, Aarhus University Hospital, Aarhus, Denmark
| | - William J Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- International Epidemiology Institute, Rockville, MD, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Edward D Yeboah
- University of Ghana Medical School, Accra, Ghana
- Korle Bu Teaching Hospital, Accra, Ghana
| | - James E Mensah
- University of Ghana Medical School, Accra, Ghana
- Korle Bu Teaching Hospital, Accra, Ghana
| | - Yong-Jie Lu
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
| | | | - Ninghan Feng
- Wuxi Second Hospital, Nanjing Medical University, Wuxi, China
| | - Xueying Mao
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
| | - Yudong Wu
- Department of Urology, First Affiliated Hospital, The Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Shan-Chao Zhao
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zan Sun
- The People's Hospital of Liaoning Province, The People's Hospital of China Medical University, Shenyang, China
| | - Stephen N Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Shannon K McDonnell
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Daniel J Schaid
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Catharine M L West
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Neil Burnet
- Division of Cancer Sciences, University of Manchester, Manchester Cancer Research Centre, Manchester Academic Health Science Centre, and The Christie NHS Foundation Trust, Manchester, UK
| | - Gill Barnett
- University of Cambridge Department of Oncology, Oncology Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | | | | | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, Boston, MA, USA
| | | | | | | | - Florence Menegaux
- Exposome and Heredity, CESP (UMR 1018), Paris-Saclay Medical School, Paris-Saclay University, Inserm, Gustave Roussy, Villejuif, France
| | - Thérèse Truong
- Exposome and Heredity, CESP (UMR 1018), Paris-Saclay Medical School, Paris-Saclay University, Inserm, Gustave Roussy, Villejuif, France
| | - Yves Akoli Koudou
- CESP (UMR 1018), Paris-Saclay Medical School, Paris-Saclay University, Inserm, Villejuif, France
| | - Esther M John
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Lovise Maehle
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
| | - Sue A Ingles
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Mariana C Stern
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Ana Vega
- Fundación Pública Galega Medicina Xenómica, Santiago De Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, Spain
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Antonio Gómez-Caamaño
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
| | - Laura Fachal
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- Fundación Pública Galega Medicina Xenómica, Santiago De Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, Spain
- Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Barry S Rosenstein
- Department of Radiation Oncology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah L Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
| | - Harry Ostrer
- Department of Pathology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
| | - Andreia Brandão
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
| | | | | | - Jeannette T Bensen
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elizabeth T H Fontham
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - James Mohler
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Urology, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
- Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Manolis Kogevinas
- ISGlobal, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Javier Llorca
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- University of Cantabria-IDIVAL, Santander, Spain
| | - Gemma Castaño-Vinyals
- ISGlobal, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Lisa Cannon-Albright
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Craig C Teerlink
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Chad D Huff
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Sara S Strom
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Luc Multigner
- University of Rennes, Inserm, EHESP, Irset (Research Institute for Environmental and Occupational Health), Rennes, France
| | - Pascal Blanchet
- CHU de Pointe-à-Pitre, University of the French Antilles, University of Rennes, Inserm, EHESP, Irset (Research Institute for Environmental and Occupational Health), Pointe-à-Pitre, France
| | - Laurent Brureau
- CHU de Pointe-à-Pitre, University of the French Antilles, University of Rennes, Inserm, EHESP, Irset (Research Institute for Environmental and Occupational Health), Pointe-à-Pitre, France
| | - Radka Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Chavdar Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University of Sofia, Sofia, Bulgaria
| | - Vanio Mitev
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Robin J Leach
- Department of Urology, Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Brandi Weaver
- Department of Urology, Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Katarina Cuk
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Kai-Uwe Saum
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eric A Klein
- Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA
- Glickman Urological & Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ann W Hsing
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Rick A Kittles
- Division of Health Equities, Department of Population Sciences, City of Hope, Duarte, CA, USA
| | - Adam B Murphy
- Department of Urology, Northwestern University, Chicago, IL, USA
| | - Christopher J Logothetis
- Department of Genitourinary Medical Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Jeri Kim
- Department of Genitourinary Medical Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - Linda Steele
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - Yuan Chun Ding
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - William B Isaacs
- James Buchanan Brady Urological Institute, Johns Hopkins Hospital and Medical Institution, Baltimore, MD, USA
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Anselm J M Hennis
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
- Chronic Disease Research Centre and Faculty of Medical Sciences, University of the West Indies, Bridgetown, Barbados
| | - John Carpten
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hardev Pandha
- Faculty of Health and Medical Sciences, The University of Surrey, Guildford, UK
| | - Agnieszka Michael
- Faculty of Health and Medical Sciences, The University of Surrey, Guildford, UK
| | - Kim De Ruyck
- Department of Basic Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Gent, Belgium
| | - Gert De Meerleer
- Department of Radiotherapy, Ghent University Hospital, Gent, Belgium
| | - Piet Ost
- Department of Radiotherapy, Ghent University Hospital, Gent, Belgium
| | - Jianfeng Xu
- Program for Personalized Cancer Care and Department of Surgery, NorthShore University HealthSystem, Evanston, IL, USA
| | - Azad Razack
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Jasmine Lim
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Soo-Hwang Teo
- Cancer Research Malaysia (CRM), Outpatient Centre, Subang Jaya Medical Centre, Subang Jaya, Malaysia
| | - Lisa F Newcomb
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Daniel W Lin
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Jay H Fowke
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - Benjamin A Rybicki
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
| | - Marija Gamulin
- Department of Oncology, University Hospital Centre Zagreb, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Davor Lessel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tomislav Kulis
- Department of Urology, University Hospital Center Zagreb, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
- Division of Radiation Oncology, Cross Cancer Institute, Edmonton, Alberta, Canada
| | - Sandeep Singhal
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Matthew Parliament
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
- Division of Radiation Oncology, Cross Cancer Institute, Edmonton, Alberta, Canada
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, Leuven, Belgium
| | - Steven Joniau
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Thomas Van den Broeck
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, Leuven, Belgium
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, Servicio Galego de Saúde, SERGAS, Santiago de Compostela, Spain
- University of California San Diego, Moores Cancer Center, La Jolla, CA, USA
| | - Jose Esteban Castelao
- Genetic Oncology Unit, CHUVI Hospital, Complexo Hospitalario Universitario de Vigo, Instituto de Investigación Biomédica Galicia Sur (IISGS), Vigo, Spain
| | - Maria Elena Martinez
- Moores Cancer Center, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Samantha Larkin
- The University of Southampton, Southampton General Hospital, Southampton, UK
| | - Paul A Townsend
- Faculty of Health and Medical Sciences, The University of Surrey, Guildford, UK
- Division of Cancer Sciences, Manchester Cancer Research Centre, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Health Innovation Manchester, University of Manchester, Manchester, UK
| | - Claire Aukim-Hastie
- Faculty of Health and Medical Sciences, The University of Surrey, Guildford, UK
| | - William S Bush
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Melinda C Aldrich
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dana C Crawford
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Shiv Srivastava
- Center for Prostate Disease Research, Uniformed Services University, Bethesda, MD, USA
| | - Jennifer C Cullen
- Center for Prostate Disease Research, Uniformed Services University, Bethesda, MD, USA
| | - Gyorgy Petrovics
- Center for Prostate Disease Research, Uniformed Services University, Bethesda, MD, USA
| | - Graham Casey
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Monique J Roobol
- Department of Urology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Guido Jenster
- Department of Urology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jennifer J Hu
- The University of Miami School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL, USA
| | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, USA
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA, USA
| | - Roberta McKean-Cowdin
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Mina Torres
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephen K Van Den Eeden
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
- Department of Urology, University of California San Francisco, San Francisco, CA, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Michael B Cook
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Hidewaki Nakagawa
- Laboratory for Cancer Genomics, RIKEN Center of Integrative Medical Sciences, Yokohama, Japan
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Urology, University of California San Francisco, San Francisco, CA, USA
| | - Rosalind A Eeles
- The Institute of Cancer Research, London, UK
- Royal Marsden NHS Foundation Trust, London, UK
| | | | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
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Horn AL, Jiang L, Washburn F, Hvitfeldt E, de la Haye K, Nicholas W, Simon P, Pentz M, Cozen W, Sood N, Conti DV. Estimation of COVID-19 risk-stratified epidemiological parameters and policy implications for Los Angeles County through an integrated risk and stochastic epidemiological model. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.12.11.20209627. [PMID: 33367291 PMCID: PMC7756248 DOI: 10.1101/2020.12.11.20209627] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Health disparities have emerged with the COVID-19 epidemic because the risk of exposure to infection and the prevalence of risk factors for severe outcomes given infection vary within and between populations. However, estimated epidemic quantities such as rates of severe illness and death, the case fatality rate (CFR), and infection fatality rate (IFR), are often expressed in terms of aggregated population-level estimates due to the lack of epidemiological data at the refined subpopulation level. For public health policy makers to better address the pandemic, stratified estimates are necessary to investigate the potential outcomes of policy scenarios targeting specific subpopulations. Methods We develop a framework for using available data on the prevalence of COVID-19 risk factors (age, comorbidities, BMI, smoking status) in subpopulations, and epidemic dynamics at the population level and stratified by age, to estimate subpopulation-stratified probabilities of severe illness and the CFR (as deaths over observed infections) and IFR (as deaths over estimated total infections) across risk profiles representing all combinations of risk factors including age, comorbidities, obesity class, and smoking status. A dynamic epidemic model is integrated with a relative risk model to produce time-varying subpopulation-stratified estimates. The integrated model is used to analyze dynamic outcomes and parameters by population and subpopulation, and to simulate alternate policy scenarios that protect specific at-risk subpopulations or modify the population-wide transmission rate. The model is calibrated to data from the Los Angeles County population during the period March 1 - October 15 2020. Findings We estimate a rate of 0.23 (95% CI: 0.13,0.33) of infections observed before April 15, which increased over the epidemic course to 0.41 (0.11,0.69). Overall population-average IFR( t ) estimates for LAC peaked at 0.77% (0.38%,1.15%) on May 15 and decreased to 0.55% (0.24%,0.90%) by October 15. The population-average IFR( t ) stratified by age group varied extensively across subprofiles representing each combination of the additional risk factors considered (comorbidities, BMI, smoking). We found median IFRs ranging from 0.009%-0.04% in the youngest age group (0-19), from 0.1%-1.8% for those aged 20-44, 0.36%-4.3% for those aged 45-64, and 1.02%-5.42% for those aged 65+. In the group aged 65+ for which the rate of unobserved infections is likely much lower, we find median CFRs in the range 4.4%-23.45%. The initial societal lockdown period avoided overwhelming healthcare capacity and greatly reduced the observed death count. In comparative scenario analysis, alternative policies in which the population-wide transmission rate is reduced to a moderate and sustainable level of non-pharmaceutical interventions (NPIs) would not have been sufficient to avoid overwhelming healthcare capacity, and additionally would have exceeded the observed death count. Combining the moderate NPI policy with stringent protection of the at-risk subpopulation of individuals 65+ would have resulted in a death count similar to observed levels, but hospital counts would have approached capacity limits. Interpretation The risk of severe illness and death of COVID-19 varies tremendously across subpopulations and over time, suggesting that it is inappropriate to summarize epidemiological parameters for the entire population and epidemic time period. This includes variation not only across age groups, but also within age categories combined with other risk factors analyzed in this study (comorbidities, obesity status, smoking). In the policy analysis accounting for differences in IFR across risk groups in comparing the control of infections and protection of higher risk groups, we find that the strict initial lockdown period in LAC was effective because it both reduced overall transmission and protected individuals at greater risk, resulting in preventing both healthcare overload and deaths. While similar numbers of deaths as observed in LAC could have been achieved with a more moderate NPI policy combined with greater protection of individuals 65+, this would have come at the expense of overwhelming the healthcare system. In anticipation of a continued rise in cases in LAC this winter, policy makers need to consider the trade offs of various policy options on the numbers of the overall population that may become infected, severely ill, and that die when considering policies targeted at subpopulations at greatest risk of transmitting infection and at greatest risk for developing severe outcomes.
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Hutchinson A, Asimit J, Wallace C. Fine-mapping genetic associations. Hum Mol Genet 2020; 29:R81-R88. [PMID: 32744321 PMCID: PMC7733401 DOI: 10.1093/hmg/ddaa148] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/04/2020] [Accepted: 07/09/2020] [Indexed: 02/07/2023] Open
Abstract
Whilst thousands of genetic variants have been associated with human traits, identifying the subset of those variants that are causal requires a further 'fine-mapping' step. We review the basic fine-mapping approach, which is computationally fast and requires only summary data, but depends on an assumption of a single causal variant per associated region which is recognized as biologically unrealistic. We discuss different ways that the approach has been built upon to accommodate multiple causal variants in a region and to incorporate additional layers of functional annotation data. We further review methods for simultaneous fine-mapping of multiple datasets, either exploiting different linkage disequilibrium (LD) structures across ancestries or borrowing information between distinct but related traits. Finally, we look to the future and the opportunities that will be offered by increasingly accurate maps of causal variants for a multitude of human traits.
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Affiliation(s)
- Anna Hutchinson
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
| | - Jennifer Asimit
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
| | - Chris Wallace
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Cambridge CB2 0SR, UK
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
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Wang G, Sarkar A, Carbonetto P, Stephens M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J R Stat Soc Series B Stat Methodol 2020; 82:1273-1300. [DOI: 10.1111/rssb.12388] [Citation(s) in RCA: 176] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Fortune MD, Wallace C. simGWAS: a fast method for simulation of large scale case-control GWAS summary statistics. Bioinformatics 2020; 35:1901-1906. [PMID: 30371734 PMCID: PMC6546134 DOI: 10.1093/bioinformatics/bty898] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 09/12/2018] [Accepted: 10/26/2018] [Indexed: 11/23/2022] Open
Abstract
Motivation Methods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some ‘truth’ is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study. Results We have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis. Availability and implementation Our method is available under a GPL license as an R package from http://github.com/chr1swallace/simGWAS. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mary D Fortune
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.,Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Chris Wallace
- MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.,Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
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Hutchinson A, Watson H, Wallace C. Improving the coverage of credible sets in Bayesian genetic fine-mapping. PLoS Comput Biol 2020; 16:e1007829. [PMID: 32282791 PMCID: PMC7179948 DOI: 10.1371/journal.pcbi.1007829] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/23/2020] [Accepted: 03/27/2020] [Indexed: 12/19/2022] Open
Abstract
Genome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some "coverage probability". Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to re-estimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the "adjusted coverage estimate". This is extended to find "adjusted credible sets", which are the smallest set of variants such that their adjusted coverage estimate meets the target coverage. We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants-a pattern matched in simulations of well powered GWAS. Crucially, our method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases.
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Affiliation(s)
- Anna Hutchinson
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, United Kingdom
| | - Hope Watson
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, United Kingdom
| | - Chris Wallace
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, United Kingdom
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, United Kingdom
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Abstract
Cerebral autoregulatory dysfunction after traumatic brain injury (TBI) is strongly linked to poor global outcome in patients at 6 months after injury. However, our understanding of the drivers of this dysfunction is limited. Genetic variation among individuals within a population gives rise to single-nucleotide polymorphisms (SNPs) that have the potential to influence a given patient's cerebrovascular response to an injury. Associations have been reported between a variety of genetic polymorphisms and global outcome in patients with TBI, but few studies have explored the association between genetic variants and cerebrovascular function after injury. In this Review, we explore polymorphisms that might play an important part in cerebral autoregulatory capacity after TBI. We outline a variety of SNPs, their biological substrates and their potential role in mediating cerebrovascular reactivity. A number of candidate polymorphisms exist in genes that are involved in myogenic, endothelial, metabolic and neurogenic vascular responses to injury. Furthermore, polymorphisms in genes involved in inflammation, the central autonomic response and cortical spreading depression might drive cerebrovascular reactivity. Identification of candidate genes involved in cerebral autoregulation after TBI provides a platform and rationale for further prospective investigation of the link between genetic polymorphisms and autoregulatory function.
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37
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Newcombe PJ, Nelson CP, Samani NJ, Dudbridge F. A flexible and parallelizable approach to genome-wide polygenic risk scores. Genet Epidemiol 2019; 43:730-741. [PMID: 31328830 PMCID: PMC6764842 DOI: 10.1002/gepi.22245] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 05/03/2019] [Accepted: 05/30/2019] [Indexed: 01/06/2023]
Abstract
The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic risk scores from meta-GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in Step 1, followed by, uniquely, long-range LD in Step 2. Our algorithm is highly parallelizable since block-wise analyses in Step 1 can be distributed across a high-performance computing cluster, and flexible, since sparsity and heritability are estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models. We compared our method to two alternative approaches: LDPred and lassosum using all seven traits in the Welcome Trust Case Control Consortium as well as meta-GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, for which there are multiple heterogeneous signals in regions of both short- and long-range LD. With sufficient compute resources, our method also allows the fastest runtimes.
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Affiliation(s)
- Paul J. Newcombe
- MRC Biostatistics Unit, School of Clinical Medicine, Cambridge Institute of Public HealthCambridge Biomedical CampusCambridgeUK
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, Cardiovascular Research Centre, Glenfield HospitalUniversity of LeicesterLeicesterUK
- NIHR Leicester Biomedical Research CentreGlenfield HospitalLeicesterUK
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, Cardiovascular Research Centre, Glenfield HospitalUniversity of LeicesterLeicesterUK
- NIHR Leicester Biomedical Research CentreGlenfield HospitalLeicesterUK
| | - Frank Dudbridge
- Department of Health Sciences, Centre for MedicineUniversity of LeicesterLeicesterUK
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38
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Asimit JL, Rainbow DB, Fortune MD, Grinberg NF, Wicker LS, Wallace C. Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases. Nat Commun 2019; 10:3216. [PMID: 31324808 PMCID: PMC6642100 DOI: 10.1038/s41467-019-11271-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 07/02/2019] [Indexed: 02/06/2023] Open
Abstract
Thousands of genetic variants are associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. Both lack of power, and joint tagging of two or more distinct causal variants by a single non-causal SNP, lead to inaccuracies in fine-mapping, with stochastic search more robust than stepwise. We develop a computationally efficient multinomial fine-mapping (MFM) approach that borrows information between diseases in a Bayesian framework. We show that MFM has greater accuracy than single disease analysis when shared causal variants exist, and negligible loss of precision otherwise. MFM analysis of six immune-mediated diseases reveals causal variants undetected in individual disease analysis, including in IL2RA where we confirm functional effects of multiple causal variants using allele-specific expression in sorted CD4+ T cells from genotype-selected individuals. MFM has the potential to increase fine-mapping resolution in related diseases enabling the identification of associated cellular and molecular phenotypes.
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Affiliation(s)
- Jennifer L Asimit
- MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
| | - Daniel B Rainbow
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Trust Center for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Mary D Fortune
- MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | - Nastasiya F Grinberg
- Department of Medicine, Cambridge Biomedical Campus, University of Cambridge, Box 157, Level 4, Cambridge, CB2 0QQ, UK
| | - Linda S Wicker
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Trust Center for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK. .,Department of Medicine, Cambridge Biomedical Campus, University of Cambridge, Box 157, Level 4, Cambridge, CB2 0QQ, UK.
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39
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Zeiler FA, McFadyen C, Newcombe VFJ, Synnot A, Donoghue EL, Ripatti S, Steyerberg EW, Gruen RL, McAllister TW, Rosand J, Palotie A, Maas AIR, Menon DK. Genetic Influences on Patient-Oriented Outcomes in Traumatic Brain Injury: A Living Systematic Review of Non-Apolipoprotein E Single-Nucleotide Polymorphisms. J Neurotrauma 2019; 38:1107-1123. [PMID: 29799308 PMCID: PMC8054522 DOI: 10.1089/neu.2017.5583] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
There is a growing literature on the impact of genetic variation on outcome in traumatic brain injury (TBI). Whereas a substantial proportion of these publications have focused on the apolipoprotein E (APOE) gene, several have explored the influence of other polymorphisms. We undertook a systematic review of the impact of single-nucleotide polymorphisms (SNPs) in non–apolipoprotein E (non-APOE) genes associated with patient outcomes in adult TBI). We searched EMBASE, MEDLINE, CINAHL, and gray literature from inception to the beginning of August 2017 for studies of genetic variance in relation to patient outcomes in adult TBI. Sixty-eight articles were deemed eligible for inclusion into the systematic review. The SNPs described were in the following categories: neurotransmitter (NT) in 23, cytokine in nine, brain-derived neurotrophic factor (BDNF) in 12, mitochondrial genes in three, and miscellaneous SNPs in 21. All studies were based on small patient cohorts and suffered from potential bias. A range of SNPs associated with genes coding for monoamine NTs, BDNF, cytokines, and mitochondrial proteins have been reported to be associated with variation in global, neuropsychiatric, and behavioral outcomes. An analysis of the tissue, cellular, and subcellular location of the genes that harbored the SNPs studied showed that they could be clustered into blood–brain barrier associated, neuroprotective/regulatory, and neuropsychiatric/degenerative groups. Several small studies report that various NT, cytokine, and BDNF-related SNPs are associated with variations in global outcome at 6–12 months post-TBI. The association of these SNPs with neuropsychiatric and behavioral outcomes is less clear. A definitive assessment of role and effect size of genetic variation in these genes on outcome remains uncertain, but could be clarified by an adequately powered genome-wide association study with appropriate recording of outcomes.
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Affiliation(s)
- Frederick A Zeiler
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom.,Section of Neurosurgery, Department of Surgery, University of Manitoba, Winnipeg, Manitoba, Canada.,Clinician Investigator Program, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Charles McFadyen
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | | | - Anneliese Synnot
- Centre for Excellence in Traumatic Brain Injury Research, National Trauma Research Institute, Monash University, The Alfred Hospital, Melbourne, Australia and Cochrane Consumers and Communication Review Group, Centre for Health Communication and Participation, School of Psychology and Public Health, La Trobe University, Melbourne, Australia
| | - Emma L Donoghue
- Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine and Cochrane Australia, Monash University, Melbourne, Australia
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM) and Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ewout W Steyerberg
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands and Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
| | - Russel L Gruen
- Central Clinical School, Monash University, Melbourne, Australia and Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Thomas W McAllister
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana
| | - Jonathan Rosand
- Division of Neurocritical Care and Emergency Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Aarno Palotie
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Andrew I R Maas
- Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
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40
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Shapland CY, Thompson JR, Sheehan NA. A Bayesian approach to Mendelian randomisation with dependent instruments. Stat Med 2019; 38:985-1001. [PMID: 30485479 DOI: 10.1002/sim.8029] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 10/12/2018] [Accepted: 10/16/2018] [Indexed: 12/11/2022]
Abstract
Mendelian randomisation (MR) is a method for establishing causality between a risk factor and an outcome by using genetic variants as instrumental variables. In practice, the association between individual genetic variants and the risk factor is often weak, which may lead to a lack of precision in the MR and even biased MR estimates. Usually, the most significant variant within a genetic region is selected to represent the association with the risk factor, but there is no guarantee that this variant will be causal or that it will capture all of the genetic association within the region. It may be advantageous to use extra variants selected from the same region in the MR. The problem is to decide which variants to select. Rather than selecting a specific set of variants, we investigate the use of Bayesian model averaging (BMA) to average the MR over all possible combinations of genetic variants. Our simulations demonstrate that the BMA version of MR outperforms classical estimation with many dependent variants and performs much better than an MR based on variants selected by penalised regression. In further simulations, we investigate robustness to violations in the model assumptions and demonstrate sensitivity to the inclusion of invalid instruments. The method is illustrated by applying it to an MR of the effect of body mass index on blood pressure using SNPs in the FTO gene.
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Affiliation(s)
- Chin Yang Shapland
- Department of Health Sciences and Genetics, University of Leicester, Leicester, UK
| | - John R Thompson
- Department of Health Sciences and Genetics, University of Leicester, Leicester, UK
| | - Nuala A Sheehan
- Department of Health Sciences and Genetics, University of Leicester, Leicester, UK
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41
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Banerjee S, Zeng L, Schunkert H, Söding J. Bayesian multiple logistic regression for case-control GWAS. PLoS Genet 2018; 14:e1007856. [PMID: 30596640 PMCID: PMC6329526 DOI: 10.1371/journal.pgen.1007856] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 01/11/2019] [Accepted: 11/28/2018] [Indexed: 12/21/2022] Open
Abstract
Genetic variants in genome-wide association studies (GWAS) are tested for disease association mostly using simple regression, one variant at a time. Standard approaches to improve power in detecting disease-associated SNPs use multiple regression with Bayesian variable selection in which a sparsity-enforcing prior on effect sizes is used to avoid overtraining and all effect sizes are integrated out for posterior inference. For binary traits, the logistic model has not yielded clear improvements over the linear model. For multi-SNP analysis, the logistic model required costly and technically challenging MCMC sampling to perform the integration. Here, we introduce the quasi-Laplace approximation to solve the integral and avoid MCMC sampling. We expect the logistic model to perform much better than multiple linear regression except when predicted disease risks are spread closely around 0.5, because only close to its inflection point can the logistic function be well approximated by a linear function. Indeed, in extensive benchmarks with simulated phenotypes and real genotypes, our Bayesian multiple LOgistic REgression method (B-LORE) showed considerable improvements (1) when regressing on many variants in multiple loci at heritabilities ≥ 0.4 and (2) for unbalanced case-control ratios. B-LORE also enables meta-analysis by approximating the likelihood functions of individual studies by multivariate normal distributions, using their means and covariance matrices as summary statistics. Our work should make sparse multiple logistic regression attractive also for other applications with binary target variables. B-LORE is freely available from: https://github.com/soedinglab/b-lore.
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Affiliation(s)
- Saikat Banerjee
- Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | | | | | - Johannes Söding
- Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
- * E-mail:
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42
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Matejcic M, Saunders EJ, Dadaev T, Brook MN, Wang K, Sheng X, Olama AAA, Schumacher FR, Ingles SA, Govindasami K, Benlloch S, Berndt SI, Albanes D, Koutros S, Muir K, Stevens VL, Gapstur SM, Tangen CM, Batra J, Clements J, Gronberg H, Pashayan N, Schleutker J, Wolk A, West C, Mucci L, Kraft P, Cancel-Tassin G, Sorensen KD, Maehle L, Grindedal EM, Strom SS, Neal DE, Hamdy FC, Donovan JL, Travis RC, Hamilton RJ, Rosenstein B, Lu YJ, Giles GG, Kibel AS, Vega A, Bensen JT, Kogevinas M, Penney KL, Park JY, Stanford JL, Cybulski C, Nordestgaard BG, Brenner H, Maier C, Kim J, Teixeira MR, Neuhausen SL, De Ruyck K, Razack A, Newcomb LF, Lessel D, Kaneva R, Usmani N, Claessens F, Townsend PA, Gago-Dominguez M, Roobol MJ, Menegaux F, Khaw KT, Cannon-Albright LA, Pandha H, Thibodeau SN, Schaid DJ, Wiklund F, Chanock SJ, Easton DF, Eeles RA, Kote-Jarai Z, Conti DV, Haiman CA. Germline variation at 8q24 and prostate cancer risk in men of European ancestry. Nat Commun 2018; 9:4616. [PMID: 30397198 PMCID: PMC6218483 DOI: 10.1038/s41467-018-06863-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 10/01/2018] [Indexed: 02/07/2023] Open
Abstract
Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10-15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62-4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification.
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Affiliation(s)
- Marco Matejcic
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA
| | | | - Tokhir Dadaev
- The Institute of Cancer Research, London, SW7 3RP, UK
| | - Mark N Brook
- The Institute of Cancer Research, London, SW7 3RP, UK
| | - Kan Wang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA
| | - Xin Sheng
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA
| | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106-7219, USA
- Seidman Cancer Center, University Hospitals, Cleveland, OH, 44106, USA
| | - Sue A Ingles
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA
| | | | - Sara Benlloch
- The Institute of Cancer Research, London, SW7 3RP, UK
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Kenneth Muir
- Institute of Population Health, University of Manchester, Manchester, M13 9PL, UK
- Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - Victoria L Stevens
- Epidemiology Research Program, American Cancer Society, 250 Williams Street, Atlanta, GA, 30303, USA
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, 250 Williams Street, Atlanta, GA, 30303, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia
- Translational Research Institute, Brisbane, QLD, 4102, Australia
| | - Judith Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia
- Translational Research Institute, Brisbane, QLD, 4102, Australia
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Nora Pashayan
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Department of Applied Health Research, University College London, London, WC1E 7HB, UK
| | - Johanna Schleutker
- Department of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, FI-20014, Turku, Finland
- Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, 20521, Turku, Finland
- BioMediTech, University of Tampere, 33520, Tampere, Finland
| | - Alicja Wolk
- Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Catharine West
- Division of Cancer Sciences, Manchester Academic Health Science Centre, Radiotherapy Related Research, Manchester NIHR Biomedical Research Centre, The Christie Hospital NHS Foundation Trust, University of Manchester, Manchester, M13 9PL, UK
| | - Lorelei Mucci
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, 02115, USA
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Géraldine Cancel-Tassin
- GRC N°5 ONCOTYPE-URO, UPMC Univ Paris 06, Tenon Hospital, F-75020, Paris, France
- CeRePP, Tenon Hospital, F-75020, Paris, France
| | - Karina D Sorensen
- Department of Molecular Medicine, Aarhus University Hospital, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, 8200, Aarhus N, Denmark
| | - Lovise Maehle
- Department of Medical Genetics, Oslo University Hospital, 0424, Oslo, Norway
| | - Eli M Grindedal
- Department of Medical Genetics, Oslo University Hospital, 0424, Oslo, Norway
| | - Sara S Strom
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - David E Neal
- Department of Oncology, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX1 2JD, UK
| | - Jenny L Donovan
- School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK
| | - Ruth C Travis
- Cancer Epidemiology, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Robert J Hamilton
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON, M5G 2M9, Canada
| | - Barry Rosenstein
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029-5674, USA
| | - Yong-Jie Lu
- Centre for Molecular Oncology, John Vane Science Centre, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Graham G Giles
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, Boston, MA, 02115, USA
| | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica, CIBERER, IDIS, 15706, Santiago de Compostela, Spain
| | - Jeanette T Bensen
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Columbia, SC, 29208, USA
| | - Manolis Kogevinas
- Centre for Research in Environmental Epidemiology (CREAL), Barcelona Institute for Global Health (ISGlobal), 08003, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- IMIM (Hospital del Mar Research Institute), 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08002, Barcelona, Spain
| | - Kathryn L Penney
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, 02184, USA
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115, Szczecin, Poland
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120, Heidelberg, Germany
| | - Christiane Maier
- Institute for Human Genetics, University Hospital Ulm, 89075, Ulm, Germany
| | - Jeri Kim
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute of Porto, 4200-072, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313, Porto, Portugal
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, 91010, USA
| | - Kim De Ruyck
- Ghent University, Faculty of Medicine and Health Sciences, Basic Medical Sciences, B-9000, Gent, Belgium
| | - Azad Razack
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Lisa F Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA
- Department of Urology, University of Washington, Seattle, WA, 98195, USA
| | - Davor Lessel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, D-20246, Hamburg, Germany
| | - Radka Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, 1431, Sofia, Bulgaria
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB, T6G 1Z2, Canada
- Division of Radiation Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB, T6G 1Z2, Canada
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, BE-3000, Leuven, Belgium
| | - Paul A Townsend
- Manchester Cancer Research Centre, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Health Innovation Manchester, University of Manchester, Manchester, M13 9WL, UK
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, Servicio Galego de Saúde, SERGAS, 15706, Santiago de Compostela, Spain
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92037, USA
| | - Monique J Roobol
- Department of Urology, Erasmus University Medical Center, 3015 CE, Rotterdam, The Netherlands
| | - Florence Menegaux
- Cancer and Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, 94807, Villejuif Cédex, France
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, CB2 2QQ, UK
| | - Lisa A Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, 84112, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, 84148, USA
| | - Hardev Pandha
- The University of Surrey, Guildford, Surrey, GU2 7XH, UK
| | - Stephen N Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Daniel J Schaid
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Rosalind A Eeles
- The Institute of Cancer Research, London, SW7 3RP, UK
- Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | | | - David V Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA.
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43
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Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet 2018; 19:491-504. [PMID: 29844615 PMCID: PMC6050137 DOI: 10.1038/s41576-018-0016-z] [Citation(s) in RCA: 478] [Impact Index Per Article: 79.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues.
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Affiliation(s)
- Daniel J Schaid
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
| | - Wenan Chen
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Nicholas B Larson
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
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44
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Inshaw JRJ, Cutler AJ, Burren OS, Stefana MI, Todd JA. Approaches and advances in the genetic causes of autoimmune disease and their implications. Nat Immunol 2018; 19:674-684. [PMID: 29925982 DOI: 10.1038/s41590-018-0129-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Accepted: 04/04/2018] [Indexed: 12/18/2022]
Abstract
Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology.
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Affiliation(s)
- Jamie R J Inshaw
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Antony J Cutler
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Oliver S Burren
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - M Irina Stefana
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - John A Todd
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
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45
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Dadaev T, Saunders EJ, Newcombe PJ, Anokian E, Leongamornlert DA, Brook MN, Cieza-Borrella C, Mijuskovic M, Wakerell S, Olama AAA, Schumacher FR, Berndt SI, Benlloch S, Ahmed M, Goh C, Sheng X, Zhang Z, Muir K, Govindasami K, Lophatananon A, Stevens VL, Gapstur SM, Carter BD, Tangen CM, Goodman P, Thompson IM, Batra J, Chambers S, Moya L, Clements J, Horvath L, Tilley W, Risbridger G, Gronberg H, Aly M, Nordström T, Pharoah P, Pashayan N, Schleutker J, Tammela TLJ, Sipeky C, Auvinen A, Albanes D, Weinstein S, Wolk A, Hakansson N, West C, Dunning AM, Burnet N, Mucci L, Giovannucci E, Andriole G, Cussenot O, Cancel-Tassin G, Koutros S, Freeman LEB, Sorensen KD, Orntoft TF, Borre M, Maehle L, Grindedal EM, Neal DE, Donovan JL, Hamdy FC, Martin RM, Travis RC, Key TJ, Hamilton RJ, Fleshner NE, Finelli A, Ingles SA, Stern MC, Rosenstein B, Kerns S, Ostrer H, Lu YJ, Zhang HW, Feng N, Mao X, Guo X, Wang G, Sun Z, Giles GG, Southey MC, MacInnis RJ, FitzGerald LM, Kibel AS, Drake BF, Vega A, Gómez-Caamaño A, Fachal L, Szulkin R, Eklund M, Kogevinas M, Llorca J, Castaño-Vinyals G, Penney KL, Stampfer M, Park JY, Sellers TA, Lin HY, Stanford JL, Cybulski C, Wokolorczyk D, Lubinski J, Ostrander EA, Geybels MS, Nordestgaard BG, Nielsen SF, Weisher M, Bisbjerg R, Røder MA, Iversen P, Brenner H, Cuk K, Holleczek B, Maier C, Luedeke M, Schnoeller T, Kim J, Logothetis CJ, John EM, Teixeira MR, Paulo P, Cardoso M, Neuhausen SL, Steele L, Ding YC, De Ruyck K, De Meerleer G, Ost P, Razack A, Lim J, Teo SH, Lin DW, Newcomb LF, Lessel D, Gamulin M, Kulis T, Kaneva R, Usmani N, Slavov C, Mitev V, Parliament M, Singhal S, Claessens F, Joniau S, Van den Broeck T, Larkin S, Townsend PA, Aukim-Hastie C, Gago-Dominguez M, Castelao JE, Martinez ME, Roobol MJ, Jenster G, van Schaik RHN, Menegaux F, Truong T, Koudou YA, Xu J, Khaw KT, Cannon-Albright L, Pandha H, Michael A, Kierzek A, Thibodeau SN, McDonnell SK, Schaid DJ, Lindstrom S, Turman C, Ma J, Hunter DJ, Riboli E, Siddiq A, Canzian F, Kolonel LN, Le Marchand L, Hoover RN, Machiela MJ, Kraft P, Freedman M, Wiklund F, Chanock S, Henderson BE, Easton DF, Haiman CA, Eeles RA, Conti DV, Kote-Jarai Z. Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants. Nat Commun 2018; 9:2256. [PMID: 29892050 PMCID: PMC5995836 DOI: 10.1038/s41467-018-04109-8] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 04/05/2018] [Indexed: 12/16/2022] Open
Abstract
Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.
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Affiliation(s)
- Tokhir Dadaev
- The Institute of Cancer Research, London, SW7 3RP, UK
| | | | - Paul J Newcombe
- MRC Biostatistics Unit, University of Cambridge, Robinson Way, Cambridge, CB2 0SR, UK
| | | | - Daniel A Leongamornlert
- The Institute of Cancer Research, London, SW7 3RP, UK
- Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
| | - Mark N Brook
- The Institute of Cancer Research, London, SW7 3RP, UK
| | | | | | | | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106-7219, USA
- Seidman Cancer Center, University Hospitals, Cleveland, OH, 44106, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Sara Benlloch
- The Institute of Cancer Research, London, SW7 3RP, UK
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Mahbubl Ahmed
- The Institute of Cancer Research, London, SW7 3RP, UK
| | - Chee Goh
- The Institute of Cancer Research, London, SW7 3RP, UK
| | - Xin Sheng
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
| | - Zhuo Zhang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
| | - Kenneth Muir
- Institute of Population Health, University of Manchester, Manchester, M13 9PL, UK
- Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | | | - Artitaya Lophatananon
- Institute of Population Health, University of Manchester, Manchester, M13 9PL, UK
- Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - Victoria L Stevens
- Epidemiology Research Program, American Cancer Society, 250 Williams Street, Atlanta, GA, 30303, USA
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, 250 Williams Street, Atlanta, GA, 30303, USA
| | - Brian D Carter
- Epidemiology Research Program, American Cancer Society, 250 Williams Street, Atlanta, GA, 30303, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Phyllis Goodman
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Ian M Thompson
- CHRISTUS Santa Rosa Hospital - Medical Center, San Antonio, TX, 78229, USA
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia
- Translational Research Institute, Brisbane, QLD, 4102, Australia
| | - Suzanne Chambers
- Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, 4222, Australia
- Cancer Council Queensland, Fortitude Valley, QLD, 4006, Australia
| | - Leire Moya
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia
- Translational Research Institute, Brisbane, QLD, 4102, Australia
| | - Judith Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia
- Translational Research Institute, Brisbane, QLD, 4102, Australia
| | - Lisa Horvath
- Chris O'Brien Lifehouse (COBLH), Camperdown, Sydney, NSW, 2010, Australia
- Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Wayne Tilley
- Dame Roma Mitchell Cancer Research Centre, University of Adelaide, Adelaide, SA, 5005, Australia
| | - Gail Risbridger
- Department of Anatomy and Developmental Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC, 3800, Australia
- Prostate Cancer Translational Research Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, and Department of Urology, Karolinska University Hospital, 171 76, Stockholm, Sweden
| | - Tobias Nordström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
- Department of Clinical Sciences at Danderyd Hospital, Karolinska Institutet, 182 88, Stockholm, Sweden
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Nora Pashayan
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Department of Applied Health Research, University College London, London, WC1E 7HB, UK
| | - Johanna Schleutker
- Institute of Biomedicine, University of Turku, FI-20014, Turku, Finland
- Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, 20521, Turku, Finland
| | - Teuvo L J Tammela
- Department of Urology, Tampere University Hospital, University of Tampere, Kalevantie 4, FI-33014, Tampere, Finland
| | - Csilla Sipeky
- Institute of Biomedicine, University of Turku, FI-20014, Turku, Finland
| | - Anssi Auvinen
- Department of Epidemiology, School of Health Sciences, University of Tampere, FI-33014, Tampere, Finland
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Stephanie Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Alicja Wolk
- Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Niclas Hakansson
- Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Catharine West
- Division of Cancer Sciences, Manchester Academic Health Science Centre, Radiotherapy Related Research, Manchester NIHR Biomedical Research Centre, The Christie Hospital NHS Foundation Trust, University of Manchester, Manchester, M13 9PL, UK
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Neil Burnet
- University of Cambridge Department of Oncology, Oncology Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB1 8RN, UK
| | - Lorelei Mucci
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, 02115, USA
| | - Edward Giovannucci
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, 02115, USA
| | - Gerald Andriole
- Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Olivier Cussenot
- GRC N°5 ONCOTYPE-URO, UPMC Univ Paris 06, Tenon Hospital, F-75020, Paris, France
- CeRePP, Tenon Hospital, F-75020, Paris, France
| | - Géraldine Cancel-Tassin
- GRC N°5 ONCOTYPE-URO, UPMC Univ Paris 06, Tenon Hospital, F-75020, Paris, France
- CeRePP, Tenon Hospital, F-75020, Paris, France
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Laura E Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Karina Dalsgaard Sorensen
- Department of Molecular Medicine, Aarhus University Hospital, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, 8200, Aarhus N, Denmark
| | - Torben Falck Orntoft
- Department of Molecular Medicine, Aarhus University Hospital, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, 8200, Aarhus N, Denmark
| | - Michael Borre
- Department of Clinical Medicine, Aarhus University, 8200, Aarhus N, Denmark
- Department of Urology, Aarhus University Hospital, 8200, Aarhus N, Denmark
| | - Lovise Maehle
- Department of Medical Genetics, Oslo University Hospital, 0424, Oslo, Norway
| | - Eli Marie Grindedal
- Department of Medical Genetics, Oslo University Hospital, 0424, Oslo, Norway
| | - David E Neal
- Department of Oncology, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, CB2 0RE, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX1 2JD, UK
| | - Jenny L Donovan
- School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX1 2JD, UK
- Faculty of Medical Science, John Radcliffe Hospital, University of Oxford, Oxford, OX1 2JD, UK
| | - Richard M Martin
- School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- National Institute for Health Research (NIHR) Biomedical Research Centre, University of Bristol, Bristol, BS8 1TH, UK
| | - Ruth C Travis
- Cancer Epidemiology, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Tim J Key
- Cancer Epidemiology, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Robert J Hamilton
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON, M5G 2M9, Canada
| | - Neil E Fleshner
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON, M5G 2M9, Canada
| | - Antonio Finelli
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON, M5G 2M9, Canada
| | - Sue Ann Ingles
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
| | - Mariana C Stern
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
| | - Barry Rosenstein
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029-5674, USA
| | - Sarah Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, 14620, USA
| | - Harry Ostrer
- Professor of Pathology and Pediatrics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Yong-Jie Lu
- Centre for Molecular Oncology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Hong-Wei Zhang
- Second Military Medical University, Shanghai, 200433, P. R. China
| | - Ninghan Feng
- Wuxi Second Hospital, Nanjing Medical University, Wuxi, Jiangzhu, 214003, China
| | - Xueying Mao
- Centre for Molecular Oncology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Xin Guo
- Department of Urology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, 200032, China
- The People's Hospital of Liaoning Province and The People's Hospital of China Medical University, Shenyang, 110001, China
| | - Guomin Wang
- Department of Urology, Zhongshan Hospital, Fudan University Medical College, Shanghai, 200032, China
| | - Zan Sun
- The People's Hospital of Liaoning Province and The People's Hospital of China Medical University, Shenyang, 110001, China
| | - Graham G Giles
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Melissa C Southey
- Precision Medicine, School and Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia
| | - Robert J MacInnis
- Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Liesel M FitzGerald
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia
| | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, Boston, MA, 02115, USA
| | - Bettina F Drake
- Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica, CIBERER, IDIS, Santiago de Compostela, 15706, Spain
| | - Antonio Gómez-Caamaño
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, SERGAS, 15706, Santiago de Compostela, Spain
| | - Laura Fachal
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica, CIBERER, IDIS, Santiago de Compostela, 15706, Spain
| | - Robert Szulkin
- Division of Family Medicine, Department of Neurobiology, Care Science and Society, Karolinska Institutet, Huddinge, SE-171 77, Stockholm, Sweden
- Scandinavian Development Services, 182 33, Danderyd, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Manolis Kogevinas
- Centre for Research in Environmental Epidemiology (CREAL), Barcelona Institute for Global Health (ISGlobal), 08003, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- IMIM (Hospital del Mar Research Institute), 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08002, Barcelona, Spain
| | - Javier Llorca
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- University of Cantabria-IDIVAL, 39005, Santander, Spain
| | - Gemma Castaño-Vinyals
- Centre for Research in Environmental Epidemiology (CREAL), Barcelona Institute for Global Health (ISGlobal), 08003, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- IMIM (Hospital del Mar Research Institute), 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08002, Barcelona, Spain
| | - Kathryn L Penney
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, 02184, USA
| | - Meir Stampfer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, 02184, USA
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Thomas A Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Hui-Yi Lin
- School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA
| | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, 98195, USA
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115, Szczecin, Poland
| | - Dominika Wokolorczyk
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115, Szczecin, Poland
| | - Jan Lubinski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, 70-115, Szczecin, Poland
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Milan S Geybels
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
| | - Sune F Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
| | - Maren Weisher
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
| | - Rasmus Bisbjerg
- Department of Urology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
| | - Martin Andreas Røder
- Copenhagen Prostate Cancer Center, Department of Urology, Rigshospitalet, Copenhagen University Hospital, DK-2730, Herlev, Denmark
| | - Peter Iversen
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
- Copenhagen Prostate Cancer Center, Department of Urology, Rigshospitalet, Copenhagen University Hospital, DK-2730, Herlev, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120, Heidelberg, Germany
| | - Katarina Cuk
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
| | | | - Christiane Maier
- Institute for Human Genetics, University Hospital Ulm, 89075, Ulm, Germany
| | - Manuel Luedeke
- Institute for Human Genetics, University Hospital Ulm, 89075, Ulm, Germany
| | | | - Jeri Kim
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Christopher J Logothetis
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Esther M John
- Cancer Prevention Institute of California, Fremont, CA, 94538, USA
- Department of Health Research & Policy (Epidemiology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, 94305-5101, USA
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute of Porto, 4200-072, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313, Porto, Portugal
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute of Porto, 4200-072, Porto, Portugal
| | - Marta Cardoso
- Department of Genetics, Portuguese Oncology Institute of Porto, 4200-072, Porto, Portugal
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, 91010, USA
| | - Linda Steele
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, 91010, USA
| | - Yuan Chun Ding
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, 91010, USA
| | - Kim De Ruyck
- Ghent University, Faculty of Medicine and Health Sciences, Basic Medical Sciences, B-9000, Gent, Belgium
| | - Gert De Meerleer
- Ghent University, Faculty of Medicine and Health Sciences, Basic Medical Sciences, B-9000, Gent, Belgium
| | - Piet Ost
- Department of Radiotherapy, Ghent University Hospital, B-9000, Gent, Belgium
| | - Azad Razack
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Jasmine Lim
- Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Soo-Hwang Teo
- Cancer Research Malaysia (CRM), Outpatient Centre, Subang Jaya Medical Centre, 47500, Subang Jaya, Selangor, Malaysia
| | - Daniel W Lin
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA
- Department of Urology, University of Washington, Seattle, WA, 98195, USA
| | - Lisa F Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109-1024, USA
- Department of Urology, University of Washington, Seattle, WA, 98195, USA
| | - Davor Lessel
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, D-20246, Hamburg, Germany
| | - Marija Gamulin
- Division of Medical Oncology, Urogenital Unit, Department of Oncology at the University Hospital Centre Zagreb, Šalata 2, 10000, Zagreb, Croatia
| | - Tomislav Kulis
- Department of Urology, University Hospital Center Zagreb, University of Zagreb School of Medicine, Šalata 2, 10000, Zagreb, Croatia
| | - Radka Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, 1431, Sofia, Bulgaria
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB, T6G 1Z2, Canada
- Division of Radiation Oncology, Cross Cancer Institute, Edmonton, AB, T6G 1Z2, Canada
| | - Chavdar Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University of Sofia, 1431, Sofia, Bulgaria
| | - Vanio Mitev
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, 1431, Sofia, Bulgaria
| | - Matthew Parliament
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB, T6G 1Z2, Canada
- Division of Radiation Oncology, Cross Cancer Institute, Edmonton, AB, T6G 1Z2, Canada
| | - Sandeep Singhal
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB, T6G 1Z2, Canada
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, BE-3000, Leuven, Belgium
| | - Steven Joniau
- Department of Urology, University Hospitals Leuven, BE-3000, Leuven, Belgium
| | - Thomas Van den Broeck
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, BE-3000, Leuven, Belgium
- Department of Urology, University Hospitals Leuven, BE-3000, Leuven, Belgium
| | - Samantha Larkin
- Southampton General Hospital, The University of Southampton, Southampton, SO16 6YD, UK
| | - Paul A Townsend
- Manchester Cancer Research Centre, Faculty of Biology Medicine & Health, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Health Innovation Manchester, University of Manchester, Manchester, M13 9WL, UK
| | | | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, Servicio Galego de Saúde, SERGAS, 15706, Santiago de Compostela, Spain
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92037, USA
| | - Jose Esteban Castelao
- Genetic Oncology Unit, CHUVI Hospital, Complexo Hospitalario Universitario de Vigo, Instituto de Investigación Biomédica Galicia Sur (IISGS), 36204, Vigo (Pontevedra), Spain
| | - Maria Elena Martinez
- Moores Cancer Center, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, 92093-0012, USA
| | - Monique J Roobol
- Department of Urology, Erasmus University Medical Center, 3015 CE, Rotterdam, The Netherlands
| | - Guido Jenster
- Department of Urology, Erasmus University Medical Center, 3015 CE, Rotterdam, The Netherlands
| | - Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus University Medical Center, 3015 CE, Rotterdam, The Netherlands
| | - Florence Menegaux
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, 94807, Villejuif Cédex, France
| | - Thérèse Truong
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, 94807, Villejuif Cédex, France
| | - Yves Akoli Koudou
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, 94807, Villejuif Cédex, France
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, 60201, USA
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, CB2 2QQ, UK
| | - Lisa Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, 84112, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, 84148, USA
| | - Hardev Pandha
- The University of Surrey, Guildford, Surrey, GU2 7XH, UK
| | | | | | - Stephen N Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Shannon K McDonnell
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Daniel J Schaid
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Sara Lindstrom
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
| | - Constance Turman
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Jing Ma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, 02184, USA
| | - David J Hunter
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, SW7 2AZ, UK
| | - Afshan Siddiq
- Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London, EC1M 6BQ, UK
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
| | - Laurence N Kolonel
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | | | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, Strangeways Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
| | - Rosalind A Eeles
- The Institute of Cancer Research, London, SW7 3RP, UK
- Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | - David V Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90015, USA
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Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018. [PMID: 29846171 DOI: 10.7554/elife.34408s] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (<ext-link ext-link-type="uri" xlink:href="http://www.mrbase.org">http://www.mrbase.org</ext-link>): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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Affiliation(s)
- Gibran Hemani
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Jie Zheng
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Benjamin Elsworth
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Kaitlin H Wade
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Valeriia Haberland
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Denis Baird
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Charles Laurin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Jack Bowden
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Ryan Langdon
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Vanessa Y Tan
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - James Yarmolinsky
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Hashem A Shihab
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - David M Evans
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.,University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia
| | - Caroline Relton
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Richard M Martin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom R Gaunt
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Philip C Haycock
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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47
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Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018; 7:e34408. [PMID: 29846171 PMCID: PMC5976434 DOI: 10.7554/elife.34408] [Citation(s) in RCA: 3413] [Impact Index Per Article: 568.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 03/28/2018] [Indexed: 12/21/2022] Open
Abstract
Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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Affiliation(s)
- Gibran Hemani
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Jie Zheng
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Benjamin Elsworth
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Kaitlin H Wade
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Valeriia Haberland
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Denis Baird
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Charles Laurin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Stephen Burgess
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUnited Kingdom
| | - Jack Bowden
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Ryan Langdon
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Vanessa Y Tan
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - James Yarmolinsky
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Hashem A Shihab
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - David M Evans
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
- University of Queensland Diamantina InstituteTranslational Research InstituteBrisbaneAustralia
| | - Caroline Relton
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Richard M Martin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Tom R Gaunt
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
| | - Philip C Haycock
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
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48
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Sundar VS, Fan CC, Holland D, Dale AM. Determining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty. Front Genet 2018; 9:77. [PMID: 29556250 PMCID: PMC5844985 DOI: 10.3389/fgene.2018.00077] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 02/19/2018] [Indexed: 01/16/2023] Open
Abstract
With the availability of high-throughput sequencing data, identification of genetic causal variants accurately requires the efficient incorporation of function annotation data into the optimization routine. This motivates the need for development of novel methods for genome wide association studies with special focus on fine-mapping capabilities. A penalty function method that is simple to implement and capable of integrating functional annotation information into the estimation procedure, is proposed in this work. The idea is to use the prior distribution of the effect sizes explicitly as a penalty function. The estimates obtained are shown to be better correlated with the true effect sizes (in comparison with a few existing techniques). An increase in the positive and negative predictive value is demonstrated using Hapgen2 simulated data.
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Affiliation(s)
- V. S. Sundar
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, United States
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- *Correspondence: V. S. Sundar
| | - Chun-Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, United States
- Department of Cognitive Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Dominic Holland
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, United States
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, United States
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, United States
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
- Anders M. Dale
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49
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Benner C, Havulinna AS, Järvelin MR, Salomaa V, Ripatti S, Pirinen M. Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies. Am J Hum Genet 2017; 101:539-551. [PMID: 28942963 DOI: 10.1016/j.ajhg.2017.08.012] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 08/17/2017] [Indexed: 01/15/2023] Open
Abstract
During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research.
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Affiliation(s)
- Christian Benner
- Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; Department of Public Health, University of Helsinki, 00014 Helsinki, Finland.
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; National Institute for Health and Welfare, 00271 Helsinki, Finland
| | - Marjo-Riitta Järvelin
- Center for Life-Course Health Research and Northern Finland Cohort Center, Biocenter Oulu, University of Oulu, 90014 Oulu, Finland; Faculty of Medicine, University of Oulu, 90014 Oulu, Finland; Unit of Primary Care, Oulu University Hospital, 90220 Oulu, Finland; Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, W2 1PG, UK
| | - Veikko Salomaa
- National Institute for Health and Welfare, 00271 Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; Department of Public Health, University of Helsinki, 00014 Helsinki, Finland; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, Cambridge, UK
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland; Department of Public Health, University of Helsinki, 00014 Helsinki, Finland; Helsinki Institute for Information Technology and Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland.
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50
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Zhu X, Stephens M. BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES. Ann Appl Stat 2017; 11:1561-1592. [PMID: 29399241 PMCID: PMC5796536 DOI: 10.1214/17-aoas1046] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a "Regression with Summary Statistics" (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.
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