1
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Mandal S, Kim DH, Hua X, Li S, Shi J. Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error. Biostatistics 2024; 25:486-503. [PMID: 36797830 PMCID: PMC11017132 DOI: 10.1093/biostatistics/kxad001] [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: 02/25/2022] [Revised: 01/24/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.
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Affiliation(s)
- Soutrik Mandal
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
| | - Do Hyun Kim
- Department of Biostatistics, Fielding School of Public Health, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Xing Hua
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA
| | - Shilan Li
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA
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2
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Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. CELL GENOMICS 2024; 4:100523. [PMID: 38508198 PMCID: PMC11019356 DOI: 10.1016/j.xgen.2024.100523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/15/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
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Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Leland E Hull
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia R Martin
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
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3
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Xu C, Ganesh SK, Zhou X. mtPGS: Leverage multiple correlated traits for accurate polygenic score construction. Am J Hum Genet 2023; 110:1673-1689. [PMID: 37716346 PMCID: PMC10577082 DOI: 10.1016/j.ajhg.2023.08.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/18/2023] [Accepted: 08/27/2023] [Indexed: 09/18/2023] Open
Abstract
Accurate polygenic scores (PGSs) facilitate the genetic prediction of complex traits and aid in the development of personalized medicine. Here, we develop a statistical method called multi-trait assisted PGS (mtPGS), which can construct accurate PGSs for a target trait of interest by leveraging multiple traits relevant to the target trait. Specifically, mtPGS borrows SNP effect size similarity information between the target trait and its relevant traits to improve the effect size estimation on the target trait, thus achieving accurate PGSs. In the process, mtPGS flexibly models the shared genetic architecture between the target and the relevant traits to achieve robust performance, while explicitly accounting for the environmental covariance among them to accommodate different study designs with various sample overlap patterns. In addition, mtPGS uses only summary statistics as input and relies on a deterministic algorithm with several algebraic techniques for scalable computation. We evaluate the performance of mtPGS through comprehensive simulations and applications to 25 traits in the UK Biobank, where in the real data mtPGS achieves an average of 0.90%-52.91% accuracy gain compared to the state-of-the-art PGS methods. Overall, mtPGS represents an accurate, fast, and robust solution for PGS construction in biobank-scale datasets.
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Affiliation(s)
- Chang Xu
- 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
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- 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.
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4
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Deng Q, Gupta A, Jeon H, Nam JH, Yilmaz AS, Chang W, Pietrzak M, Li L, Kim HJ, Chung D. graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data. Front Genet 2023; 14:1079198. [PMID: 37501720 PMCID: PMC10370274 DOI: 10.3389/fgene.2023.1079198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 06/21/2023] [Indexed: 07/29/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.
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Affiliation(s)
- Qiaolan Deng
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Arkobrato Gupta
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Hyeongseon Jeon
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| | - Jin Hyun Nam
- Division of Big Data Science, Korea University Sejong Campus, Sejong, Republic of Korea
| | - Ayse Selen Yilmaz
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Hang J. Kim
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
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5
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Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.21.23286110. [PMID: 36865265 PMCID: PMC9980241 DOI: 10.1101/2023.02.21.23286110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
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Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Leland E. Hull
- Division of General Internal Medicine, 100 Cambridge Street,
Massachusetts General Hospital, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - David A. van Heel
- Blizard Institute, Barts and the London School of Medicine and
Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - Alicia R. Martin
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - S. Hong Lee
- Australian Centre for Precision Health, University of South
Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000,
Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
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6
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Xiao J, Cai M, Hu X, Wan X, Chen G, Yang C. XPXP: improving polygenic prediction by cross-population and cross-phenotype analysis. Bioinformatics 2022; 38:1947-1955. [PMID: 35040939 DOI: 10.1093/bioinformatics/btac029] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 11/16/2021] [Accepted: 01/12/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION As increasing sample sizes from genome-wide association studies (GWASs), polygenic risk scores (PRSs) have shown great potential in personalized medicine with disease risk prediction, prevention and treatment. However, the PRS constructed using European samples becomes less accurate when it is applied to individuals from non-European populations. It is an urgent task to improve the accuracy of PRSs in under-represented populations, such as African populations and East Asian populations. RESULTS In this article, we propose a cross-population and cross-phenotype (XPXP) method for construction of PRSs in under-represented populations. XPXP can construct accurate PRSs by leveraging biobank-scale datasets in European populations and multiple GWASs of genetically correlated phenotypes. XPXP also allows to incorporate population-specific and phenotype-specific effects, and thus further improves the accuracy of PRS. Through comprehensive simulation studies and real data analysis, we demonstrated that our XPXP outperformed existing PRS approaches. We showed that the height PRSs constructed by XPXP achieved 9% and 18% improvement over the runner-up method in terms of predicted R2 in East Asian and African populations, respectively. We also showed that XPXP substantially improved the stratification ability in identifying individuals at high genetic risk of type 2 diabetes. AVAILABILITY AND IMPLEMENTATION The XPXP software and all analysis code are available at github.com/YangLabHKUST/XPXP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiashun Xiao
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China.,Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mingxuan Cai
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China.,Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xianghong Hu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China.,Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China.,Pazhou Lab, Guangzhou 510330, China
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China.,Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
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7
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Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022; 135:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/11/2022] [Accepted: 01/30/2022] [Indexed: 01/10/2023]
Abstract
Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11,336 participants, 3,903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p=0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies.
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8
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Chung W. Statistical models and computational tools for predicting complex traits and diseases. Genomics Inform 2022; 19:e36. [PMID: 35012283 PMCID: PMC8752975 DOI: 10.5808/gi.21053] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/01/2021] [Indexed: 12/30/2022] Open
Abstract
Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.
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Affiliation(s)
- Wonil Chung
- Department of Statistics and Actuarial Science, Soongsil University, Seoul 06978, Korea.,Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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9
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Xie Y, Li M, Dong W, Jiang W, Zhao H. M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits. PLoS Genet 2021; 17:e1009849. [PMID: 34735430 PMCID: PMC8568192 DOI: 10.1371/journal.pgen.1009849] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 09/29/2021] [Indexed: 11/22/2022] Open
Abstract
Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.
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Affiliation(s)
- Yuhan Xie
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Mo Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Weilai Dong
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
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10
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Ma Y, Zhou X. Genetic prediction of complex traits with polygenic scores: a statistical review. Trends Genet 2021; 37:995-1011. [PMID: 34243982 PMCID: PMC8511058 DOI: 10.1016/j.tig.2021.06.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 01/03/2023]
Abstract
Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.
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Affiliation(s)
- Ying Ma
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
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11
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Zhao Z, Yi Y, Song J, Wu Y, Zhong X, Lin Y, Hohman TJ, Fletcher J, Lu Q. PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics. Genome Biol 2021; 22:257. [PMID: 34488838 PMCID: PMC8419981 DOI: 10.1186/s13059-021-02479-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/25/2021] [Indexed: 12/20/2022] Open
Abstract
Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis.
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Affiliation(s)
- Zijie Zhao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53703 USA
| | - Yanyao Yi
- Department of Statistics, University of Wisconsin-Madison, Madison, WI USA
| | - Jie Song
- Department of Statistics, University of Wisconsin-Madison, Madison, WI USA
| | - Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53703 USA
| | | | - Yupei Lin
- University of Wisconsin-Madison, Madison, WI USA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Jason Fletcher
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI USA
- Department of Sociology, University of Wisconsin-Madison, Madison, WI USA
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53703 USA
- Department of Statistics, University of Wisconsin-Madison, Madison, WI USA
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI USA
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12
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Tang H, He Z. Advances and challenges in quantitative delineation of the genetic architecture of complex traits. QUANTITATIVE BIOLOGY 2021; 9:168-184. [PMID: 35492964 PMCID: PMC9053444 DOI: 10.15302/j-qb-021-0249] [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] [Indexed: 11/16/2022]
Abstract
Background Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases. Results This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted. Conclusion GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.
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Affiliation(s)
- Hua Tang
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305, USA
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13
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Di Narzo AF, Hart A, Kosoy R, Peters L, Stojmirovic A, Cheng H, Zhang Z, Shan M, Cho J, Kasarskis A, Argmann C, Peter I, Schadt EE, Hao K. Polygenic risk score for alcohol drinking behavior improves prediction of inflammatory bowel disease risk. Hum Mol Genet 2021; 30:514-523. [PMID: 33601420 PMCID: PMC8599895 DOI: 10.1093/hmg/ddab045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 01/23/2021] [Accepted: 02/03/2021] [Indexed: 02/05/2023] Open
Abstract
Epidemiological studies have long recognized risky behaviors as potentially modifiable factors for the onset and flares of inflammatory bowel disease (IBD); yet, the underlying mechanisms are largely unknown. Recently, the genetic susceptibilities to cigarette smoking, alcohol and cannabis use [i.e. substance use (SU)] have been characterized by well-powered genome-wide association studies (GWASs). We aimed to assess the impact of genetic determinants of SU on IBD risk. Using Mount Sinai Crohn's and Colitis Registry (MSCCR) cohort of 1058 IBD cases and 188 healthy controls, we computed the polygenic risk score (PRS) for SU and correlated them with the observed IBD diagnoses, while adjusting for genetic ancestry, PRS for IBD and SU behavior at enrollment. The results were validated in a pediatric cohort with no SU exposure. PRS of alcohol consumption (DrnkWk), smoking cessation and age of smoking initiation, were associated with IBD risk in MSCCR even after adjustment for PRSIBD and actual smoking status. One interquartile range decrease in PRSDrnkWk was significantly associated to higher IBD risk (i.e. inverse association) (with odds ratio = 1.65 and 95% confidence interval: 1.32, 2.06). The association was replicated in a pediatric Crohn's disease cohort. Colocalization analysis identified a locus on chromosome 16 with polymorphisms in IL27, SULT1A2 and SH2B1, which reached genome-wide statistical significance in GWAS (P < 7.7e-9) for both alcohol consumption and IBD risk. This study demonstrated that the genetic predisposition to SU was associated with IBD risk, independent of PRSIBD and in the absence of SU behaviors. Our study may help further stratify individuals at risk of IBD.
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Affiliation(s)
- Antonio F Di Narzo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amy Hart
- Immunology Translational Sciences, Janssen R&D, LLC, Spring House, PA 19477, USA
| | - Roman Kosoy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lauren Peters
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Statistical Genomics, Sema4, Stamford, CT 06902, USA
| | | | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zhongyang Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mingxu Shan
- Statistical Genomics, Sema4, Stamford, CT 06902, USA
| | - Judy Cho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carmen Argmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Statistical Genomics, Sema4, Stamford, CT 06902, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Statistical Genomics, Sema4, Stamford, CT 06902, USA
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14
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Cai M, Xiao J, Zhang S, Wan X, Zhao H, Chen G, Yang C. A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. Am J Hum Genet 2021; 108:632-655. [PMID: 33770506 PMCID: PMC8059341 DOI: 10.1016/j.ajhg.2021.03.002] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 03/01/2021] [Indexed: 12/29/2022] Open
Abstract
The development of polygenic risk scores (PRSs) has proved useful to stratify the general European population into different risk groups. However, PRSs are less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ancestry genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. Our framework can also incorporate population-specific effects to further improve construction of PRS. With innovations in data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient, and robust PRS construction across a range of genetic architectures. In a Chinese cohort, our methods achieved 7.3%-198.0% accuracy gain for height and 19.5%-313.3% accuracy gain for body mass index (BMI) in terms of predictive R2 compared to existing PRS approaches. We also show that XPA and XPASS can achieve substantial improvement for construction of height PRSs in the African population, suggesting the generality of our framework across global populations.
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Affiliation(s)
- Mingxuan Cai
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Jiashun Xiao
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Shunkang Zhang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China
| | - Hongyu Zhao
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 201111, China; Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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15
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Akbarzadeh M, Dehkordi SR, Roudbar MA, Sargolzaei M, Guity K, Sedaghati-Khayat B, Riahi P, Azizi F, Daneshpour MS. GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study. Sci Rep 2021; 11:5780. [PMID: 33707626 PMCID: PMC7952573 DOI: 10.1038/s41598-021-85203-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 02/26/2021] [Indexed: 12/15/2022] Open
Abstract
In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs' subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.
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Affiliation(s)
- Mahdi Akbarzadeh
- Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, POBox: 19195-4763, Tehran, Iran
| | - Saeid Rasekhi Dehkordi
- Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, POBox: 19195-4763, Tehran, Iran
| | - Mahmoud Amiri Roudbar
- Department of Animal Science, Safiabad-Dezful Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization (AREEO), Dezful, Iran
| | - Mehdi Sargolzaei
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Canada
- Select Sires Inc., Plain City, USA
| | - Kamran Guity
- Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, POBox: 19195-4763, Tehran, Iran
| | - Bahareh Sedaghati-Khayat
- Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, POBox: 19195-4763, Tehran, Iran
| | - Parisa Riahi
- Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, POBox: 19195-4763, Tehran, Iran
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam S Daneshpour
- Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, POBox: 19195-4763, Tehran, Iran.
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16
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Di Narzo A, Frades I, Crane HM, Crane PK, Hulot JS, Kasarskis A, Hart A, Argmann C, Dubinsky M, Peter I, Hao K. Meta-analysis of sample-level dbGaP data reveals novel shared genetic link between body height and Crohn's disease. Hum Genet 2021; 140:865-877. [PMID: 33452914 DOI: 10.1007/s00439-020-02250-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 12/19/2020] [Indexed: 12/29/2022]
Abstract
To further explore genetic links between complex traits, we developed a comprehensive framework to harmonize and integrate extensive genotype and phenotype data from the four well-characterized cohorts with the focus on cardiometabolic diseases deposited to the database of Genotypes and Phenotypes (dbGaP). We generated a series of polygenic risk scores (PRS) to investigate pleiotropic effects of loci that confer genetic risk for 19 common diseases and traits on body height, type 2 diabetes (T2D), and myocardial infarction (MI). In a meta-analysis of 20,021 subjects, we identified shared genetic determinants of Crohn's Disease (CD), a type of inflammatory bowel disease, and body height (p = 5.5 × 10-5). The association of PRS-CD with height was replicated in UK Biobank (p = 1.1 × 10-5) and an independent cohort of 510 CD cases and controls (1.57 cm shorter height per PRS-CD interquartile increase, p = 5.0 × 10-3 and a 28% reduction in CD risk per interquartile increase in PRS-height, p = 1.1 × 10-3, with the effect independent of CD diagnosis). A pathway analysis of the variants overlapping between PRS-height and PRS-CD detected significant enrichment of genes from the inflammatory, immune-mediated and growth factor regulation pathways. This finding supports the clinical observation of growth failure in patients with childhood-onset CD and demonstrates the value of using individual-level data from dbGaP in searching for shared genetic determinants. This information can help provide a refined insight into disease pathogenesis and may have major implications for novel therapies and drug repurposing.
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Affiliation(s)
- Antonio Di Narzo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA.,Icahn School of Medicine At Mount Sinai, Icahn Institute for Data Science and Genomic Technology, New York, NY, USA
| | - Itziar Frades
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA.,Computational Biology and Systems Biomedicine Research Group, Biodonostia Health Research Institute, San Sebastián, Spain
| | - Heidi M Crane
- Department of Medicine, University of Washington, Seattle, WA, USA.,Center for AIDS Research, University of Washington, Seattle, WA, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jean-Sebastian Hulot
- Université de Paris, INSERM, PARCC, CIC1418, F-75015, Paris, France.,Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA.,Icahn School of Medicine At Mount Sinai, Icahn Institute for Data Science and Genomic Technology, New York, NY, USA.,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amy Hart
- Janssen R&D, LLC, 1400 McKean Road, Spring House, PA, USA
| | - Carmen Argmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA.,Icahn School of Medicine At Mount Sinai, Icahn Institute for Data Science and Genomic Technology, New York, NY, USA
| | - Marla Dubinsky
- Department of Pediatric Gastroenterology and Nutrition, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA.,Icahn School of Medicine At Mount Sinai, Icahn Institute for Data Science and Genomic Technology, New York, NY, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Ave, New York, NY, 10029, USA. .,Icahn School of Medicine At Mount Sinai, Icahn Institute for Data Science and Genomic Technology, New York, NY, USA.
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17
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Li R, Duan R, Zhang X, Lumley T, Pendergrass S, Bauer C, Hakonarson H, Carrell DS, Smoller JW, Wei WQ, Carroll R, Velez Edwards DR, Wiesner G, Sleiman P, Denny JC, Mosley JD, Ritchie MD, Chen Y, Moore JH. Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics. Nat Commun 2021; 12:168. [PMID: 33420026 PMCID: PMC7794298 DOI: 10.1038/s41467-020-20211-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 11/13/2020] [Indexed: 11/22/2022] Open
Abstract
Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients' data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.
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Affiliation(s)
- Ruowang Li
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xinyuan Zhang
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Sarah Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Christopher Bauer
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Robert Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Digna R Velez Edwards
- Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Georgia Wiesner
- Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Patrick Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Josh C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Jonathan D Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason H Moore
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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18
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Bracher-Smith M, Crawford K, Escott-Price V. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Mol Psychiatry 2021; 26:70-79. [PMID: 32591634 PMCID: PMC7610853 DOI: 10.1038/s41380-020-0825-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/09/2020] [Accepted: 06/16/2020] [Indexed: 12/25/2022]
Abstract
Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48-0.95 AUC) and differed between schizophrenia (0.54-0.95 AUC), bipolar (0.48-0.65 AUC), autism (0.52-0.81 AUC) and anorexia (0.62-0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies.
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Affiliation(s)
- Matthew Bracher-Smith
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Karen Crawford
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
- Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | - Valentina Escott-Price
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
- Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, UK.
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19
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Chen TH, Chatterjee N, Landi MT, Shi J. A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. J Am Stat Assoc 2020; 116:133-143. [PMID: 34483403 DOI: 10.1080/01621459.2020.1764849] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Large-scale genome-wide association (GWAS) studies provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance for a given disease, which typically requires a large training data set for selecting truly associated single nucleotide polymorphisms (SNPs) and estimating effect sizes accurately. Here, we develop a comprehensive penalized regression for fitting l 1 regularized regression models to GWAS summary statistics. We propose incorporating Pleiotropy and ANnotation information into PRS (PANPRS) development through suitable formulation of penalty functions and associated tuning parameters. Extensive simulations show that PANPRS performs equally well or better than existing PRS methods when no functional annotation or pleiotropy is incorporated. When functional annotation data and pleiotropy are informative, PANPRS substantially outperforms existing PRS methods in simulations. Finally, we applied our methods to build PRS for type 2 diabetes and melanoma and found that incorporating relevant functional annotations and GWAS of genetically related traits improved prediction of these two complex diseases.
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Affiliation(s)
- Ting-Huei Chen
- Department of Mathematics and Statistics, Regular member, Cervo Brain Research Centre, University of Laval, 1045, av. of Medicine, Suite 1056, Quebec G1V 0A6, Canada
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University Baltimore, Maryland, United States of America, 615 N Wolfe Street Baltimore, MD 21205
| | - Maria Teresa Landi
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Maryland, United States of America, 9609 Medical Center Drive, RM 7E106, Bethesda, MD, 20892
| | - Jianxin Shi
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Maryland, United States of America, 9609 Medical Center Drive, RM 7E122, Bethesda, MD, 20892
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20
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Abstract
Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.
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Affiliation(s)
- Ning Sun
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA
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21
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Dai M, Wan X, Peng H, Wang Y, Liu Y, Liu J, Xu Z, Yang C. Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy. Bioinformatics 2020; 35:1729-1736. [PMID: 30307540 DOI: 10.1093/bioinformatics/bty870] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 09/06/2018] [Accepted: 10/09/2018] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area. RESULTS In this study, we propose a novel statistical approach, LEP, which provides a novel way of modeling the connection between the individual-level data and summary-level data. LEP integrates both types of data by LEveraging Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction. The algorithm for parameter estimation is developed to handle genome-wide-scale data. Through comprehensive simulation studies, we demonstrated the advantages of LEP over the existing methods. We further applied LEP to perform integrative analysis of Crohn's disease from WTCCC and summary statistics from GWAS of some other diseases, such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.39% (±0.58%) to 68.33% (±0.32%) using about 195 000 variants. AVAILABILITY AND IMPLEMENTATION The LEP software is available at https://github.com/daviddaigithub/LEP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mingwei Dai
- Department of Applied Mathematics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.,Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China
| | - Xiang Wan
- ShenZhen Research Institute of Big Data, ShenZhen, China
| | - Hao Peng
- School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China
| | - Yao Wang
- Department of Applied Mathematics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
| | - Yue Liu
- Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Jin Liu
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Zongben Xu
- Department of Applied Mathematics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
| | - Can Yang
- Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China
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22
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Abstract
The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.
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23
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Boulesteix AL, Wright MN, Hoffmann S, König IR. Statistical learning approaches in the genetic epidemiology of complex diseases. Hum Genet 2019; 139:73-84. [PMID: 31049651 DOI: 10.1007/s00439-019-01996-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 03/04/2019] [Indexed: 02/07/2023]
Abstract
In this paper, we give an overview of methodological issues related to the use of statistical learning approaches when analyzing high-dimensional genetic data. The focus is set on regression models and machine learning algorithms taking genetic variables as input and returning a classification or a prediction for the target variable of interest; for example, the present or future disease status, or the future course of a disease. After briefly explaining the basic motivation and principle of these methods, we review different procedures that can be used to evaluate the accuracy of the obtained models and discuss common flaws that may lead to over-optimistic conclusions with respect to their prediction performance and usefulness.
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Affiliation(s)
- Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-University, Munich, Germany.
| | - Marvin N Wright
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.,Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Sabine Hoffmann
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-University, Munich, Germany
| | - Inke R König
- Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany
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24
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Wray NR, Kemper KE, Hayes BJ, Goddard ME, Visscher PM. Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans: Genomic Prediction. Genetics 2019; 211:1131-1141. [PMID: 30967442 PMCID: PMC6456317 DOI: 10.1534/genetics.119.301859] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 01/20/2019] [Indexed: 12/20/2022] Open
Abstract
In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship <0.05) to estimate heritability captured by common SNPs. In livestock data, all animals within a breed are to some extent "related," and so it is not possible to select unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.
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Affiliation(s)
- Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4067, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4067, Australia
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4067, Australia
| | - Benjamin J Hayes
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Michael E Goddard
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
- Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4067, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4067, Australia
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25
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Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet 2019; 51:568-576. [PMID: 30804563 PMCID: PMC6788740 DOI: 10.1038/s41588-019-0345-7] [Citation(s) in RCA: 187] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 01/09/2019] [Indexed: 12/12/2022]
Abstract
Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.
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Affiliation(s)
- Yiming Hu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Mo Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Haoyi Weng
- Division of Biostatistics, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, NT, Hong Kong
| | - Jiawei Wang
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Seyedeh M Zekavat
- Yale School of Medicine, New Haven, CT, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zhaolong Yu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Boyang Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Jianlei Gu
- SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China
| | - Sydney Muchnik
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Yu Shi
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Brian W Kunkle
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Adam Naj
- Center for Clinical Epidemiology and Biostatistic, and the Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda Kuzma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yi Zhao
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Hui Lu
- SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
- SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China.
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
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26
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Chung W, Chen J, Turman C, Lindstrom S, Zhu Z, Loh PR, Kraft P, Liang L. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat Commun 2019; 10:569. [PMID: 30718517 PMCID: PMC6361917 DOI: 10.1038/s41467-019-08535-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Accepted: 01/17/2019] [Indexed: 01/15/2023] Open
Abstract
We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank (N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R2 = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data. Information of genetic architectures of complex traits can be leveraged for predicting phenotypes. Here, the authors develop CTPR (Cross-Trait Penalized Regression), a method for multi-trait polygenic risk prediction using individual-level genotypes and/or summary statistics from large cohorts.
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Affiliation(s)
- Wonil Chung
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Jun Chen
- Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Constance Turman
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Sara Lindstrom
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
| | - Zhaozhong Zhu
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Po-Ru Loh
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Liming Liang
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. .,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
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27
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Hong EP, Rhee KH, Kim DH, Park JW. Identification of pleiotropic genetic variants affecting osteoporosis risk in a Korean elderly cohort. J Bone Miner Metab 2019; 37:43-52. [PMID: 29273888 DOI: 10.1007/s00774-017-0892-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 11/29/2017] [Indexed: 11/30/2022]
Abstract
Pleiotropy has important implications for understanding the genetic basis and risk assessment of osteoporosis. Our aim was to identify pleiotropic genetic variants associated with the development of osteoporosis and predict osteoporosis risk by leveraging pleiotropic variants. We evaluated the effects of 21 conventional risk factors and 185 single-nucleotide polymorphisms (SNPs) in 63 inflammation- and metabolism-related genes on osteoporosis risk in a community-based Korean cohort study of 1025 participants, the Hallym Aging Study. Ten nongenetic factors, including sex (female) and hematocrit level, and 12 SNPs across ten genes showed evidence of association with incident osteoporosis in 270 initially osteoporosis-free subjects who completed a 6-year follow up. Three gene variants, rs1801282 (PPARG-Pro12Ala, hazard ratio (HR) = 3.26, P = 0.008), rs1408282 (near EPHA7, HR = 1.87, P = 0.002), and rs2076212 (PNPLA3-Gly115Cys, HR = 2.24, P = 0.024), were associated with significant differences in survival among the three genotype groups (Pdiff = 0.042, 0.003, and 0.048, respectively). Individuals in the highest polygenic risk score tertile were 27.9 fold more likely to develop osteoporosis than those in the lowest tertile (P = 0.004). The PPARG gene in particular was a hub pleiotropic gene in the epistasis network. Our findings highlight pleiotropic modulations of metabolism- and inflammation-related genes in the development of osteoporosis and demonstrate the contribution of pleiotropic genetic variants in prediction of osteoporosis risk.
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Affiliation(s)
- Eun Pyo Hong
- Department of Medical Genetics, College of Medicine, Hallym University, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do, 24252, Republic of Korea
| | - Ka Hyun Rhee
- Department of Medicine, College of Medicine, Hallym University, Chuncheon, Republic of Korea
| | - Dong Hyun Kim
- Department of Social and Preventive Medicine, College of Medicine, Hallym University, Chuncheon, Republic of Korea
- Hallym Research Institute of Clinical Epidemiology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - Ji Wan Park
- Department of Medical Genetics, College of Medicine, Hallym University, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do, 24252, Republic of Korea.
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28
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PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy. Animals (Basel) 2018; 8:ani8120239. [PMID: 30562943 PMCID: PMC6316348 DOI: 10.3390/ani8120239] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 11/26/2018] [Accepted: 11/28/2018] [Indexed: 11/16/2022] Open
Abstract
Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene, MCHR2, which was associated with presoma muscle development in cattle. In summary, PCA-based multiple-trait GWAS is an efficient model for exploring pleiotropic QTNs in quantitative traits.
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29
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Chen J, Wu JS, Mize T, Shui D, Chen X. Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits. J Neuroimmune Pharmacol 2018; 13:532-540. [PMID: 30276764 DOI: 10.1007/s11481-018-9811-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/12/2018] [Indexed: 01/03/2023]
Abstract
Schizophrenia is genetically heterogeneous and comorbid with many conditions. In this study, we explored polygenic scores (PGSs) from genetically related conditions and traits to predict schizophrenia diagnosis using both logistic regression and deep neural network (DNN) models. We used the combined Molecular Genetics of Schizophrenia and Swedish Schizophrenia Case Control Study (MGS + SSCCS) data for training and testing the models, and used the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) data as independent validation. We screened 28 conditions and traits comorbid with schizophrenia to identify traits as potential predictors and used LASSO regression to select predictors for model construction. We investigated how PGS calculation influenced model performance. We found that the inclusion of comorbid traits improved model performance and PGSs calculated from two traits were more generalizable in independent validation. With a DNN model using 19 PGS predictors, we accomplished a prediction accuracy of 0.813 and an AUC of 0.905 in the MGS + SSCCS data. When this model was validated with the CATIE data, it achieved an accuracy of 0.721 and AUC of 0.747. Our results indicate that PGSs alone may not be sufficient to predict schizophrenia accurately and the inclusion of behavioral and clinical data may be necessary for more accurate prediction model.
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Affiliation(s)
- Jingchun Chen
- Nevada institute of personalized medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154-4009, USA
| | - Jian-Shing Wu
- Nevada institute of personalized medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154-4009, USA
| | - Travis Mize
- Department of Psychology, University of Nevada Las Vegas, 4505 S. Maryland Parkway, Las Vegas, NV, 89154-4009, USA
| | - Dandan Shui
- Nevada institute of personalized medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154-4009, USA
| | - Xiangning Chen
- Nevada institute of personalized medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154-4009, USA. .,Department of Psychology, University of Nevada Las Vegas, 4505 S. Maryland Parkway, Las Vegas, NV, 89154-4009, USA.
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30
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New approaches in psychiatric drug development. Eur Neuropsychopharmacol 2018; 28:983-993. [PMID: 30056086 DOI: 10.1016/j.euroneuro.2018.06.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 06/18/2018] [Accepted: 06/25/2018] [Indexed: 02/03/2023]
Abstract
Numerous novel neuroscience-based drug targets have been identified in recent years. However, it remains unclear how these targets relate to the expression of symptoms in central nervous system (CNS) disorders in general and psychiatric disorders in particular. To discuss this issue, a New Frontiers Meetings of European College of Neuropsychopharmacology (ECNP) was organized to address the challenges in translational neuroscience research that are impeding the effective development of new treatments. The main aim of this meeting was to discuss scientific insights, concepts and methodologies in order to improve drug development for psychiatric disorders. The meeting was designed to bring together stakeholders from academia, pharmaceutical industry, and regulatory agencies. Here we provide a synopsis of the proceedings from the meeting entitled 'New approaches to psychiatric drug development'. New views on psychiatric drug development were presented to address the challenges and pitfalls as identified by the different stakeholders. The general conclusion of the meeting was that drug discovery could be stimulated by designing new classification and sensitive assessment tools for psychiatric disorders, which bear closer relationships to neuropharmacological and neuroscientific developments. This is in line with the vision of precision psychiatry in which patients are clustered, not merely on symptoms, but primarily on biological phenotypes that represent pathophysiological relevant and 'drugable' processes. To achieve these goals, a closer collaboration between all stakeholders in early stages of development is essential to define the research criteria together and to reach consensus on new quantitative biological methodologies and etiology-directed treatments.
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31
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Liu J, Wan X, Wang C, Yang C, Zhou X, Yang C. LLR: a latent low-rank approach to colocalizing genetic risk variants in multiple GWAS. Bioinformatics 2018; 33:3878-3886. [PMID: 28961754 DOI: 10.1093/bioinformatics/btx512] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 08/09/2017] [Indexed: 12/30/2022] Open
Abstract
Motivation Genome-wide association studies (GWAS), which genotype millions of single nucleotide polymorphisms (SNPs) in thousands of individuals, are widely used to identify the risk SNPs underlying complex human phenotypes (quantitative traits or diseases). Most conventional statistical methods in GWAS only investigate one phenotype at a time. However, an increasing number of reports suggest the ubiquity of pleiotropy, i.e. many complex phenotypes sharing common genetic bases. This motivated us to leverage pleiotropy to develop new statistical approaches to joint analysis of multiple GWAS. Results In this study, we propose a latent low-rank (LLR) approach to colocalizing genetic risk variants using summary statistics. In the presence of pleiotropy, there exist risk loci that affect multiple phenotypes. To leverage pleiotropy, we introduce a low-rank structure to modulate the probabilities of the latent association statuses between loci and phenotypes. Regarding the computational efficiency of LLR, a novel expectation-maximization-path (EM-path) algorithm has been developed to greatly reduce the computational cost and facilitate model selection and inference. We demonstrate the advantages of LLR over competing approaches through simulation studies and joint analysis of 18 GWAS datasets. Availability and implementation The LLR software is available on https://sites.google.com/site/liujin810822. Contact macyang@ust.hk.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jin Liu
- Center for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Xiang Wan
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Chaolong Wang
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | | | - Xiaowei Zhou
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Can Yang
- Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China.,Department of Mathematics, Hong Kong Baptist University, Hong Kong, China
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32
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Abstract
Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.
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33
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Deng Y, Pan W. Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses. Genetics 2017; 207:1285-1299. [PMID: 28971959 PMCID: PMC5714448 DOI: 10.1534/genetics.117.300347] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 09/29/2017] [Indexed: 11/18/2022] Open
Abstract
There is growing interest in testing genetic pleiotropy, which is when a single genetic variant influences multiple traits. Several methods have been proposed; however, these methods have some limitations. First, all the proposed methods are based on the use of individual-level genotype and phenotype data; in contrast, for logistical, and other, reasons, summary statistics of univariate SNP-trait associations are typically only available based on meta- or mega-analyzed large genome-wide association study (GWAS) data. Second, existing tests are based on marginal pleiotropy, which cannot distinguish between direct and indirect associations of a single genetic variant with multiple traits due to correlations among the traits. Hence, it is useful to consider conditional analysis, in which a subset of traits is adjusted for another subset of traits. For example, in spite of substantial lowering of low-density lipoprotein cholesterol (LDL) with statin therapy, some patients still maintain high residual cardiovascular risk, and, for these patients, it might be helpful to reduce their triglyceride (TG) level. For this purpose, in order to identify new therapeutic targets, it would be useful to identify genetic variants with pleiotropic effects on LDL and TG after adjusting the latter for LDL; otherwise, a pleiotropic effect of a genetic variant detected by a marginal model could simply be due to its association with LDL only, given the well-known correlation between the two types of lipids. Here, we develop a new pleiotropy testing procedure based only on GWAS summary statistics that can be applied for both marginal analysis and conditional analysis. Although the main technical development is based on published union-intersection testing methods, care is needed in specifying conditional models to avoid invalid statistical estimation and inference. In addition to the previously used likelihood ratio test, we also propose using generalized estimating equations under the working independence model for robust inference. We provide numerical examples based on both simulated and real data, including two large lipid GWAS summary association datasets based on ∼100,000 and ∼189,000 samples, respectively, to demonstrate the difference between marginal and conditional analyses, as well as the effectiveness of our new approach.
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Affiliation(s)
- Yangqing Deng
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455
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Chesmore K, Bartlett J, Williams SM. The ubiquity of pleiotropy in human disease. Hum Genet 2017; 137:39-44. [DOI: 10.1007/s00439-017-1854-z] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 11/14/2017] [Indexed: 02/07/2023]
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Accumulation of minor alleles and risk prediction in schizophrenia. Sci Rep 2017; 7:11661. [PMID: 28916820 PMCID: PMC5601945 DOI: 10.1038/s41598-017-12104-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 09/01/2017] [Indexed: 12/15/2022] Open
Abstract
Schizophrenia is a common neuropsychiatric disorder with a lifetime risk of 1%. Accumulation of common polygenic variations has been found to be an important risk factor. Recent studies showed a role for the enrichment of minor alleles (MAs) of SNPs in complex diseases such as Parkinson’s disease. Here we similarly studied the role of genome wide MAs in schizophrenia using public datasets. Relative to matched controls, schizophrenia cases showed higher average values in minor allele content (MAC) or the average amount of MAs per subject. By risk prediction analysis based on weighted genetic risk score (wGRS) of MAs, we identified an optimal MA set consisting of 23 238 variants that could be used to predict 3.14% of schizophrenia cases, which is comparable to using 22q11 deletion to detect schizophrenia cases. Pathway enrichment analysis of these SNPs identified 30 pathways with false discovery rate (FDR) <0.02 and of significant P-value, most of which are known to be linked with schizophrenia and other neurological disorders. These results suggest that MAs accumulation may be a risk factor to schizophrenia and provide a method to genetically screen for this disease.
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Coram MA, Fang H, Candille SI, Assimes TL, Tang H. Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations. Am J Hum Genet 2017; 101:218-226. [PMID: 28757202 DOI: 10.1016/j.ajhg.2017.06.015] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 06/29/2017] [Indexed: 11/20/2022] Open
Abstract
An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent. Heterogeneity in genetic architecture underlying complex traits and diseases, while broadly acknowledged, remains poorly characterized. Ignoring such heterogeneity likely reduces predictive accuracy for minority individuals. In this study, we propose an approach, called XP-BLUP, which ameliorates this ethnic disparity by combining trans-ethnic and ethnic-specific information. We build a polygenic model for complex traits that distinguishes candidate trait-relevant variants from the rest of the genome. The set of candidate variants are selected based on studies in any human population, yet the allelic effects are evaluated in a population-specific fashion. Simulation studies and real data analyses demonstrate that XP-BLUP adaptively utilizes trans-ethnic information and can substantially improve predictive accuracy in minority populations. At the same time, our study highlights the importance of the continued expansion of minority cohorts.
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Affiliation(s)
- Marc A Coram
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Huaying Fang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sophie I Candille
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Themistocles L Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
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Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genet 2017; 13:e1006836. [PMID: 28598966 PMCID: PMC5482506 DOI: 10.1371/journal.pgen.1006836] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 06/23/2017] [Accepted: 05/23/2017] [Indexed: 12/25/2022] Open
Abstract
Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS) in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn’s disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases. Genetic risk prediction plays a significant role in precision medicine. Accurate prediction models could have great impact on disease prevention and treatment strategies. However, prediction accuracies for most complex diseases remain moderate mainly due to the challenges in identifying and quantifying the effects of genetic variants from millions of markers, limited access to individual-level genotype data, and lack of efficient computational methods. Up to now, most methods have been focused on predicting disease risk using data from a single trait. With the discovery of genetic correlations among many complex diseases, incorporating data of genetically correlated diseases could have the potential to increase prediction accuracy. Current statistical methods are not able to fully exploit the richness of these kinds of data to take into account the shared genetic architecture. To make use of commonly available GWAS summary statistics, we propose a novel method to address these challenges by jointly modeling genetically correlated diseases and integrating genomic functional annotations. We demonstrate the substantial improvement in accuracy in both extensive simulation studies and real data analysis of Crohn’s disease, celiac disease and type-II diabetes. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.
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Affiliation(s)
- Yiming Hu
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Qiongshi Lu
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Wei Liu
- Peking University, Beijing, China
| | - Yuhua Zhang
- Shanghai Jiao Tong University, Shanghai, China
| | - Mo Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Clinical Epidemiology Research Center (CERC), Veterans Affairs (VA) Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
- * E-mail:
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Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Comput Biol 2017; 13:e1005589. [PMID: 28594818 PMCID: PMC5481142 DOI: 10.1371/journal.pcbi.1005589] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 06/22/2017] [Accepted: 05/19/2017] [Indexed: 12/25/2022] Open
Abstract
Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium. In this paper, we introduce AnnoPred, a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases. AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data. Compared with state-of-the-art risk prediction methods, AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data.
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Zeng P, Zhou X, Huang S. Prediction of gene expression with cis-SNPs using mixed models and regularization methods. BMC Genomics 2017; 18:368. [PMID: 28490319 PMCID: PMC5425981 DOI: 10.1186/s12864-017-3759-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 05/03/2017] [Indexed: 12/25/2022] Open
Abstract
Background It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. Methods We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. Results The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R2 ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R2 ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. Conclusions Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, 209 Tongshan Rd, Xuzhou, Jiangsu, 221004, China. .,Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48104, USA.
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48104, USA
| | - Shuiping Huang
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, 209 Tongshan Rd, Xuzhou, Jiangsu, 221004, China.
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Mak TSH, Porsch RM, Choi SW, Zhou X, Sham PC. Polygenic scores via penalized regression on summary statistics. Genet Epidemiol 2017; 41:469-480. [DOI: 10.1002/gepi.22050] [Citation(s) in RCA: 186] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 02/20/2017] [Accepted: 03/14/2017] [Indexed: 01/01/2023]
Affiliation(s)
| | | | - Shing Wan Choi
- Department of Psychiatry; University of Hong Kong; Hong Kong
| | - Xueya Zhou
- Department of Psychiatry; University of Hong Kong; Hong Kong
| | - Pak Chung Sham
- Centre for Genomic Sciences; University of Hong Kong; Hong Kong
- Department of Psychiatry; University of Hong Kong; Hong Kong
- State Key Laboratory of Brain and Cognitive Sciences; University of Hong Kong; Hong Kong
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Gordon D, Londono D, Patel P, Kim W, Finch SJ, Heiman GA. An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance. Hum Hered 2017; 81:194-209. [PMID: 28315880 DOI: 10.1159/000457135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 01/20/2017] [Indexed: 01/14/2023] Open
Abstract
Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.
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Affiliation(s)
- Derek Gordon
- Department of Genetics, The State University of New Jersey, Piscataway, NJ, USA
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Chung D, Kim HJ, Zhao H. graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture. PLoS Comput Biol 2017; 13:e1005388. [PMID: 28212402 PMCID: PMC5347371 DOI: 10.1371/journal.pcbi.1005388] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 03/06/2017] [Accepted: 01/28/2017] [Indexed: 02/06/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/. Recently, there has been accumulating evidence suggesting pleiotropy, i.e., genetic components shared across multiple phenotypes. Incorporation of pleiotropy in genetic analysis might improve statistical power to identify risk associated genetic variants. Several statistical approaches have been proposed to utilize pleiotropy for association mapping but they are currently still limited to a relatively small number of phenotypes, e.g., a pair of phenotypes. This restricts potential gain in statistical power in association mapping and investigation of pleiotropic structure among a large number of phenotypes. In order to address this challenge, in this paper, we propose graph-GPA, a novel statistical framework to integrate a large number of phenotypes using a hidden Markov random field architecture. Application of the proposed statistical method to GWAS datasets for 12 phenotypes showed that graph-GPA does not only provide a parsimonious representation of genetic relationship among these phenotypes, but also identify significantly larger number of novel genetic variants that are potentially functional. We believe that this novel approach might help investigation of common etiology and improvement of diagnosis and therapeutics.
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Affiliation(s)
- Dongjun Chung
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
- * E-mail:
| | - Hang J. Kim
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Cooperative Studies Program Coordinating Center, West Haven, Connecticut, United States of America
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43
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Zhao SD. Integrative genetic risk prediction using non-parametric empirical Bayes classification. Biometrics 2016; 73:582-592. [PMID: 27792843 DOI: 10.1111/biom.12619] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 09/01/2016] [Accepted: 09/01/2016] [Indexed: 12/27/2022]
Abstract
Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa.
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Affiliation(s)
- Sihai Dave Zhao
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, U.S.A
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Kar SP, Beesley J, Amin Al Olama A, Michailidou K, Tyrer J, Kote-Jarai ZS, Lawrenson K, Lindstrom S, Ramus SJ, Thompson DJ, Kibel AS, Dansonka-Mieszkowska A, Michael A, Dieffenbach AK, Gentry-Maharaj A, Whittemore AS, Wolk A, Monteiro A, Peixoto A, Kierzek A, Cox A, Rudolph A, Gonzalez-Neira A, Wu AH, Lindblom A, Swerdlow A, Ziogas A, Ekici AB, Burwinkel B, Karlan BY, Nordestgaard BG, Blomqvist C, Phelan C, McLean C, Pearce CL, Vachon C, Cybulski C, Slavov C, Stegmaier C, Maier C, Ambrosone CB, Høgdall CK, Teerlink CC, Kang D, Tessier DC, Schaid DJ, Stram DO, Cramer DW, Neal DE, Eccles D, Flesch-Janys D, Edwards DRV, Wokozorczyk D, Levine DA, Yannoukakos D, Sawyer EJ, Bandera EV, Poole EM, Goode EL, Khusnutdinova E, Høgdall E, Song F, Bruinsma F, Heitz F, Modugno F, Hamdy FC, Wiklund F, Giles GG, Olsson H, Wildiers H, Ulmer HU, Pandha H, Risch HA, Darabi H, Salvesen HB, Nevanlinna H, Gronberg H, Brenner H, Brauch H, Anton-Culver H, Song H, Lim HY, McNeish I, Campbell I, Vergote I, Gronwald J, Lubiński J, Stanford JL, Benítez J, Doherty JA, Permuth JB, Chang-Claude J, Donovan JL, Dennis J, Schildkraut JM, Schleutker J, Hopper JL, Kupryjanczyk J, Park JY, Figueroa J, Clements JA, Knight JA, Peto J, Cunningham JM, Pow-Sang J, Batra J, Czene K, Lu KH, Herkommer K, Khaw KT, Matsuo K, Muir K, Offitt K, Chen K, Moysich KB, Aittomäki K, Odunsi K, Kiemeney LA, Massuger LFAG, Fitzgerald LM, Cook LS, Cannon-Albright L, Hooning MJ, Pike MC, Bolla MK, Luedeke M, Teixeira MR, Goodman MT, Schmidt MK, Riggan M, Aly M, Rossing MA, Beckmann MW, Moisse M, Sanderson M, Southey MC, Jones M, Lush M, Hildebrandt MAT, Hou MF, Schoemaker MJ, Garcia-Closas M, Bogdanova N, Rahman N, Le ND, Orr N, Wentzensen N, Pashayan N, Peterlongo P, Guénel P, Brennan P, Paulo P, Webb PM, Broberg P, Fasching PA, Devilee P, Wang Q, Cai Q, Li Q, Kaneva R, Butzow R, Kopperud RK, Schmutzler RK, Stephenson RA, MacInnis RJ, Hoover RN, Winqvist R, Ness R, Milne RL, Travis RC, Benlloch S, Olson SH, McDonnell SK, Tworoger SS, Maia S, Berndt S, Lee SC, Teo SH, Thibodeau SN, Bojesen SE, Gapstur SM, Kjær SK, Pejovic T, Tammela TLJ, Dörk T, Brüning T, Wahlfors T, Key TJ, Edwards TL, Menon U, Hamann U, Mitev V, Kosma VM, Setiawan VW, Kristensen V, Arndt V, Vogel W, Zheng W, Sieh W, Blot WJ, Kluzniak W, Shu XO, Gao YT, Schumacher F, Freedman ML, Berchuck A, Dunning AM, Simard J, Haiman CA, Spurdle A, Sellers TA, Hunter DJ, Henderson BE, Kraft P, Chanock SJ, Couch FJ, Hall P, Gayther SA, Easton DF, Chenevix-Trench G, Eeles R, Pharoah PDP, Lambrechts D. Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types. Cancer Discov 2016; 6:1052-67. [PMID: 27432226 PMCID: PMC5010513 DOI: 10.1158/2159-8290.cd-15-1227] [Citation(s) in RCA: 139] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Accepted: 06/07/2016] [Indexed: 02/02/2023]
Abstract
UNLABELLED Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis. SIGNIFICANCE We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.
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Affiliation(s)
- Siddhartha P Kar
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Jonathan Beesley
- Department of Genetics, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | | | - Kate Lawrenson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Sara Lindstrom
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts
| | - Susan J Ramus
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Agnieszka Dansonka-Mieszkowska
- Department of Pathology and Laboratory Diagnostics, the Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
| | | | - Aida K Dieffenbach
- 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
| | | | - Alice S Whittemore
- Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford, California
| | - Alicja Wolk
- Karolinska Institutet, Department of Environmental Medicine, Division of Nutritional Epidemiology, Stockholm, Sweden
| | - Alvaro Monteiro
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Ana Peixoto
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | | | - Angela Cox
- Sheffield Cancer Research, Department of Oncology, University of Sheffield, Sheffield, UK
| | - Anja Rudolph
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anna Gonzalez-Neira
- Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO) and Spanish National Genotyping Center (CEGEN), Madrid, Spain
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Anthony Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK. Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Argyrios Ziogas
- Department of Epidemiology, UCI Center for Cancer Genetics Research and Prevention, School of Medicine, University of California, Irvine, Irvine, California
| | - Arif B Ekici
- University Hospital Erlangen, Institute of Human Genetics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Barbara Burwinkel
- Molecular Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany. Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Beth Y Karlan
- Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Børge G Nordestgaard
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Catherine Phelan
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Catriona McLean
- Anatomical Pathology, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Celine Vachon
- Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Chavdar Slavov
- Department of Urology, Alexandrovska University Hospital, Medical University, Sofia, Bulgaria
| | | | | | | | - Claus K Høgdall
- The Juliane Marie Centre, Department of Gynecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Craig C Teerlink
- Division of Genetic Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
| | - Daehee Kang
- Cancer Research Institute, Seoul National University, Seoul, Korea. Departments of Preventive Medicine and Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Daniel C Tessier
- McGill University and Génome Québec Innovation Centre, Montréal, Canada
| | | | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Daniel W Cramer
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, Massachusetts
| | - David E Neal
- Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK. Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | - Diana Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Dieter Flesch-Janys
- University Medical Center Hamburg-Eppendorf, Institute of Occupational Medicine and Maritime Medicine and Institute for Medical Biometrics and Epidemiology, Hamburg, Germany
| | - Digna R Velez Edwards
- Vanderbilt Epidemiology Center, Vanderbilt Genetics Institute, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dominika Wokozorczyk
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Douglas A Levine
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Elinor J Sawyer
- Research Oncology, Guy's Hospital, King's College London, London, UK
| | - Elisa V Bandera
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, The State University of New Jersey, New Brunswick, New Jersey
| | - Elizabeth M Poole
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Ellen L Goode
- Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
| | - Elza Khusnutdinova
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia. Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa, Russia
| | - Estrid Høgdall
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark. Molecular Unit, Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, National Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, P.R. China
| | - Fiona Bruinsma
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Florian Heitz
- Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte/Evang. Huyssens-Stiftung/Knappschaft GmbH, Essen, Germany. Department of Gynecology and Gynecologic Oncology, Dr. Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany
| | - Francesmary Modugno
- Department of Obstetrics, Gynecology and Reproductive Sciences, Division of Gynecologic Oncology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania. Women's Cancer Research Program, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania
| | - 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
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Håkan Olsson
- Departments of Cancer Epidemiology and Oncology, University Hospital, Lund, Sweden
| | - Hans Wildiers
- Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium
| | | | | | - Harvey A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Hatef Darabi
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Helga B Salvesen
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway. Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - 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, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hiltrud Brauch
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany. University of Tübingen, Tübingen, Germany
| | - Hoda Anton-Culver
- Department of Epidemiology, UCI Center for Cancer Genetics Research and Prevention, School of Medicine, University of California, Irvine, Irvine, California
| | - Honglin Song
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Hui-Yi Lim
- Biostatistics Program, Moffitt Cancer Center, Tampa, Florida
| | - Iain McNeish
- Institute of Cancer Sciences, University of Glasgow, Wolfson Wohl Cancer Research Centre, Beatson Institute for Cancer Research, Glasgow, UK
| | - Ian Campbell
- Cancer Genetics Laboratory, Research Division, Peter MacCallum Cancer Centre, St. Andrews Place, East Melbourne, Victoria, Australia. Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Ignace Vergote
- Department of Gynaecologic Oncology, Leuven Cancer Institute, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Jacek Gronwald
- 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
| | - Janet L Stanford
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington. Department of Epidemiology, University of Washington, Seattle, Washington
| | - Javier Benítez
- Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO) and Spanish National Genotyping Center (CEGEN), Madrid, Spain
| | - Jennifer A Doherty
- Department of Epidemiology, The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Jennifer B Permuth
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jenny L Donovan
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joellen M Schildkraut
- Department of Community and Family Medicine, Duke University Medical Center, Durham, North Carolina. Cancer Control and Population Sciences, Duke Cancer Institute, Durham, North Carolina
| | - Johanna Schleutker
- Department of Medical Biochemistry and Genetics Institute of Biomedicine, University of Turku, Turku, Finland. BioMediTech, University of Tampere and FimLab Laboratories, Tampere, Finland
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Jolanta Kupryjanczyk
- Department of Pathology and Laboratory Diagnostics, the Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Judith A Clements
- Australian Prostate Cancer Research Centre, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Julia A Knight
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Canada. Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Julio Pow-Sang
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Karen H Lu
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kathleen Herkommer
- Department of Urology, Klinikum rechts der Isar der Technischen Universitaet Muenchen, Munich, Germany
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
| | - Keitaro Matsuo
- Department of Preventive Medicine, Kyushu University Faculty of Medical Science, Nagoya, Aichi, Japan
| | - Kenneth Muir
- Institute of Population Health, University of Manchester, Manchester, UK. Warwick Medical School, University of Warwick, Coventry, UK
| | - Kenneth Offitt
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York. Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, National Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, P.R. China
| | - Kirsten B Moysich
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, New York
| | - Kristiina Aittomäki
- Department of Clinical Genetics, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Kunle Odunsi
- Department of Gynecological Oncology, Roswell Park Cancer Institute, Buffalo, New York
| | - Lambertus A Kiemeney
- Radboud University Medical Centre, Radbond Institute for Health Sciences, Nijmegen, the Netherlands
| | - Leon F A G Massuger
- Department of Gynaecology, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Linda S Cook
- Division of Epidemiology and Biostatistics, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico
| | - Lisa Cannon-Albright
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah
| | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Manuel Luedeke
- Department of Urology, University Hospital Ulm, Ulm, Germany
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal. Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Marc T Goodman
- Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute, and Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Marjanka K Schmidt
- Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Marjorie Riggan
- Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden. Department of Clinical Sciences, Danderyds Hospital, Stockholm, Sweden
| | - Mary Anne Rossing
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington. Department of Epidemiology, University of Washington, Seattle, Washington
| | - Matthias W Beckmann
- Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | | | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee
| | - Melissa C Southey
- Department of Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Michael Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Ming-Feng Hou
- Cancer Center and Department of Surgery, Chung-Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Montserrat Garcia-Closas
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Natalia Bogdanova
- Radiation Oncology Research Unit, Hannover Medical School, Hannover, Germany
| | - Nazneen Rahman
- Section of Cancer Genetics, The Institute of Cancer Research, London, UK
| | - Nhu D Le
- Cancer Control Research, British Columbia Cancer Agency, Vancouver, Canada
| | - Nick Orr
- Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Nora Pashayan
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK. Department of Applied Health Research, University College London, London, UK
| | | | - Pascal Guénel
- Environmental Epidemiology of Cancer, Center for Research in Epidemiology and Population Health, INSERM, Villejuif, France. University Paris-Sud, Villejuif, France
| | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | - Penelope M Webb
- Population Health Department, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Per Broberg
- Department of Cancer Epidemiology, University Hospital, Lund, Sweden
| | - Peter A Fasching
- Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Peter Devilee
- Departments of Human Genetics and of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qiuyin Cai
- Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Qiyuan Li
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts. Medical College of Xiamen University, Xiamen, China
| | - Radka Kaneva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, Bulgaria
| | - Ralf Butzow
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland. Department of Pathology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Reidun Kristin Kopperud
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway. Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Rita K Schmutzler
- Center for Integrated Oncology (CIO) and Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne, Germany. Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Robert A Stephenson
- Department of Surgery, University of Utah School of Medicine, Salt Lake City, Utah
| | - Robert J MacInnis
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Cancer Research and Translational Medicine, Biocenter Oulu, University of Oulu, and Northern Finland Laboratory Centre, Oulu, Finland
| | - Roberta Ness
- The University of Texas School of Public Health, Houston, Texas
| | - Roger L Milne
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sara Benlloch
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Shelley S Tworoger
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | - Sonja Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Soo Chin Lee
- Department of Hematology-Oncology, National University Health System, Singapore. Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Soo-Hwang Teo
- Cancer Research Initiatives Foundation, Sime Darby Medical Centre, Subang Jaya, Malaysia. University of Malaya Cancer Research Institute, University Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Stig E Bojesen
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia
| | - Susanne Krüger Kjær
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark. Department of Gynaecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Tanja Pejovic
- Department of Obstetrics and Gynecology, and Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon
| | - Teuvo L J Tammela
- Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Tampere, Finland
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany
| | - Tiina Wahlfors
- Department of Medical Biochemistry and Genetics Institute of Biomedicine, University of Turku, Turku, Finland
| | - Tim J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Todd L Edwards
- Vanderbilt Epidemiology Center, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee
| | - Usha Menon
- Women's Cancer, Institute for Women's Health, University College London, London, UK
| | - Ute Hamann
- Frauenklinik der Stadtklinik Baden-Baden, Baden-Baden, Germany
| | - Vanio Mitev
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, Bulgaria
| | - Veli-Matti Kosma
- Department of Pathology and Forensic Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland. Department of Pathology, Kuopio University Hospital, Kuopio, Finland
| | - Veronica Wendy Setiawan
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Vessela Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway. K.G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. Department of Clinical Molecular Biology, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Walther Vogel
- Institute of Human Genetics, University Hospital Ulm, Ulm, Germany
| | - Wei Zheng
- Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Weiva Sieh
- Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford, California
| | - William J Blot
- Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Wojciech Kluzniak
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Xiao-Ou Shu
- Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Yu-Tang Gao
- Shanghai Cancer Institute, Shanghai, P.R. China
| | - Fredrick Schumacher
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Matthew L Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts. The Eli and Edythe L. Broad Institute, Cambridge, Massachusetts
| | - Andrew Berchuck
- Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, Canada
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Amanda Spurdle
- Molecular Cancer Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Thomas A Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - David J Hunter
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Simon A Gayther
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Georgia Chenevix-Trench
- Department of Genetics, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Rosalind Eeles
- The Institute of Cancer Research, Sutton, UK. Royal Marsden National Health Service (NHS) Foundation Trust, London and Sutton, UK
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
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Prieto ML, Ryu E, Jenkins GD, Batzler A, Nassan MM, Cuellar-Barboza AB, Pathak J, McElroy SL, Frye MA, Biernacka JM. Leveraging electronic health records to study pleiotropic effects on bipolar disorder and medical comorbidities. Transl Psychiatry 2016; 6:e870. [PMID: 27529678 PMCID: PMC5022084 DOI: 10.1038/tp.2016.138] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 05/13/2016] [Accepted: 06/15/2016] [Indexed: 01/27/2023] Open
Abstract
Patients with bipolar disorder (BD) have a high prevalence of comorbid medical illness. However, the mechanisms underlying these comorbidities with BD are not well known. Certain genetic variants may have pleiotropic effects, increasing the risk of BD and other medical illnesses simultaneously. In this study, we evaluated the association of BD-susceptibility genetic variants with various medical conditions that tend to co-exist with BD, using electronic health records (EHR) data linked to genome-wide single-nucleotide polymorphism (SNP) data. Data from 7316 Caucasian subjects were used to test the association of 19 EHR-derived phenotypes with 34 SNPs that were previously reported to be associated with BD. After Bonferroni multiple testing correction, P<7.7 × 10(-5) was considered statistically significant. The top association findings suggested that the BD risk alleles at SNP rs4765913 in CACNA1C gene and rs7042161 in SVEP1 may be associated with increased risk of 'cardiac dysrhythmias' (odds ratio (OR)=1.1, P=3.4 × 10(-3)) and 'essential hypertension' (OR=1.1, P=3.5 × 10(-3)), respectively. Although these associations are not statistically significant after multiple testing correction, both genes have been previously implicated with cardiovascular phenotypes. Moreover, we present additional evidence supporting these associations, particularly the association of the SVEP1 SNP with hypertension. This study shows the potential for EHR-based analyses of large cohorts to discover pleiotropic effects contributing to complex psychiatric traits and commonly co-occurring medical conditions.
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Affiliation(s)
- M L Prieto
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine, Rochester, MN, USA
- Universidad de los Andes, Facultad de Medicina, Departamento de Psiquiatría, Santiago, Chile
| | - E Ryu
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - G D Jenkins
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - A Batzler
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - M M Nassan
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - A B Cuellar-Barboza
- Department of Psychiatry, Universidad Autónoma de Nuevo León, Nuevo León, Mexico
| | - J Pathak
- Division of Health Informatics, Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - S L McElroy
- Lindner Center of HOPE, Mason, OH, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - M A Frye
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - J M Biernacka
- Department of Psychiatry and Psychology, Mayo Clinic College of Medicine, Rochester, MN, USA
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
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46
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Lu Q, Jin C, Sun J, Bowler R, Kechris K, Kaminski N, Zhao H. Post-GWAS Prioritization Through Data Integration Provides Novel Insights on Chronic Obstructive Pulmonary Disease. STATISTICS IN BIOSCIENCES 2016; 2016:1-17. [PMID: 27812370 PMCID: PMC5087812 DOI: 10.1007/s12561-016-9151-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 05/14/2016] [Accepted: 05/24/2016] [Indexed: 01/24/2023]
Abstract
Rich collections of genomic and epigenomic annotations, availabilities of large population cohorts for genome-wide association studies (GWAS), and advancements in data integration techniques provide the unprecedented opportunity to accelerate discoveries in complex disease studies through integrative analyses. In this paper, we apply a variety of approaches to integrate GWAS summary statistics of chronic obstructive pulmonary disease (COPD) with functional annotations to illustrate how data integration could help researchers understand complex human diseases. We show that incorporating functional annotations can better prioritize GWAS signals at both the global and the local levels. Signal prioritization on severe COPD GWAS reveals multiple potential risk loci that are linked with pulmonary functions. Enrichment analysis provides novel insights on the pathogenesis of COPD and hints the existence of genetic contributions to muscle dysfuncion and chronic lung inflammation, two symptoms that are often co-morbid with COPD. Our results suggest that rich signals for COPD genetics are still buried under the Bonferroni-corrected genome-wide significance threshold. Many more biological findings are expected to emerge as more samples are recruited for COPD studies.
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Affiliation(s)
- Qiongshi Lu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | | | - Jiehuan Sun
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Russell Bowler
- National Jewish Health, Department of Medicine, Denver, CO, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Denver, Denver, CO, USA
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- VA Cooperative Studies Program Coordinating Center, West Haven, CT, USA
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47
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Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet 2016; 17:392-406. [PMID: 27140283 DOI: 10.1038/nrg.2016.27] [Citation(s) in RCA: 449] [Impact Index Per Article: 56.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Knowledge of genetics and its implications for human health is rapidly evolving in accordance with recent events, such as discoveries of large numbers of disease susceptibility loci from genome-wide association studies, the US Supreme Court ruling of the non-patentability of human genes, and the development of a regulatory framework for commercial genetic tests. In anticipation of the increasing relevance of genetic testing for the assessment of disease risks, this Review provides a summary of the methodologies used for building, evaluating and applying risk prediction models that include information from genetic testing and environmental risk factors. Potential applications of models for primary and secondary disease prevention are illustrated through several case studies, and future challenges and opportunities are discussed.
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Affiliation(s)
- Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University.,Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21205, USA.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA
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48
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Li J, Wei Z, Hakonarson H. Application of computational methods in genetic study of inflammatory bowel disease. World J Gastroenterol 2016; 22:949-960. [PMID: 26811639 PMCID: PMC4716047 DOI: 10.3748/wjg.v22.i3.949] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2015] [Revised: 11/04/2015] [Accepted: 11/24/2015] [Indexed: 02/06/2023] Open
Abstract
Genetic factors play an important role in the etiology of inflammatory bowel disease (IBD). The launch of genome-wide association study (GWAS) represents a landmark in the genetic study of human complex disease. Concurrently, computational methods have undergone rapid development during the past a few years, which led to the identification of numerous disease susceptibility loci. IBD is one of the successful examples of GWAS and related analyses. A total of 163 genetic loci and multiple signaling pathways have been identified to be associated with IBD. Pleiotropic effects were found for many of these loci; and risk prediction models were built based on a broad spectrum of genetic variants. Important gene-gene, gene-environment interactions and key contributions of gut microbiome are being discovered. Here we will review the different types of analyses that have been applied to IBD genetic study, discuss the computational methods for each type of analysis, and summarize the discoveries made in IBD research with the application of these methods.
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49
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Guo Y, Wei Z, Keating BJ, Hakonarson H. Machine learning derived risk prediction of anorexia nervosa. BMC Med Genomics 2016; 9:4. [PMID: 26792494 PMCID: PMC4721143 DOI: 10.1186/s12920-016-0165-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2015] [Accepted: 01/15/2016] [Indexed: 12/25/2022] Open
Abstract
Background Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. Methods In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children’s Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Results Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC’s of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. Conclusions To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting. Electronic supplementary material The online version of this article (doi:10.1186/s12920-016-0165-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yiran Guo
- The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Brendan J Keating
- The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.,Department of Pediatrics, School of Medicine University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | | | - Hakon Hakonarson
- The Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. .,Department of Pediatrics, School of Medicine University of Pennsylvania, Philadelphia, PA, 19104, USA.
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50
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Collective effects of common SNPs in foraging decisions in Caenorhabditis elegans and an integrative method of identification of candidate genes. Sci Rep 2015; 5:16904. [PMID: 26581252 PMCID: PMC4652280 DOI: 10.1038/srep16904] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 10/22/2015] [Indexed: 01/27/2023] Open
Abstract
Optimal foraging decision is a quantitative flexible behavior, which describes the time at which animals choose to abandon a depleting food supply. The total minor allele content (MAC) in an individual has been shown to correlate with quantitative variations in complex traits. We have studied the role of MAC in the decision to leave a food lawn in recombinant inbred advanced intercross lines (RIAILs) of Caenorhabditis elegans. We found a strong link between MAC and the food lawn leaving rates (Spearman r = 0.4, P = 0.005). We identified 28 genes of unknown functions whose expression levels correlated with both MAC and leaving rates. When examined by RNAi experiments, 8 of 10 tested among the 28 affected leaving rates, whereas only 2 of 9 did among genes that were only associated with leaving rates but not MAC (8/10 vs 2/9, P < 0.05). The results establish a link between MAC and the foraging behavior and identify 8 genes that may play a role in linking MAC with the quantitative nature of the trait. The method of correlations with both MAC and traits may find broad applications in high efficiency identification of target genes for other complex traits in model organisms and humans.
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