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Money D, Migicovsky Z, Gardner K, Myles S. LinkImputeR: user-guided genotype calling and imputation for non-model organisms. BMC Genomics 2017; 18:523. [PMID: 28693460 PMCID: PMC5504746 DOI: 10.1186/s12864-017-3873-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 06/20/2017] [Indexed: 11/24/2022] Open
Abstract
Background Genomic studies such as genome-wide association and genomic selection require genome-wide genotype data. All existing technologies used to create these data result in missing genotypes, which are often then inferred using genotype imputation software. However, existing imputation methods most often make use only of genotypes that are successfully inferred after having passed a certain read depth threshold. Because of this, any read information for genotypes that did not pass the threshold, and were thus set to missing, is ignored. Most genomic studies also choose read depth thresholds and quality filters without investigating their effects on the size and quality of the resulting genotype data. Moreover, almost all genotype imputation methods require ordered markers and are therefore of limited utility in non-model organisms. Results Here we introduce LinkImputeR, a software program that exploits the read count information that is normally ignored, and makes use of all available DNA sequence information for the purposes of genotype calling and imputation. It is specifically designed for non-model organisms since it requires neither ordered markers nor a reference panel of genotypes. Using next-generation DNA sequence (NGS) data from apple, cannabis and grape, we quantify the effect of varying read count and missingness thresholds on the quantity and quality of genotypes generated from LinkImputeR. We demonstrate that LinkImputeR can increase the number of genotype calls by more than an order of magnitude, can improve genotyping accuracy by several percent and can thus improve the power of downstream analyses. Moreover, we show that the effects of quality and read depth filters can differ substantially between data sets and should therefore be investigated on a per-study basis. Conclusions By exploiting DNA sequence data that is normally ignored during genotype calling and imputation, LinkImputeR can significantly improve both the quantity and quality of genotype data generated from NGS technologies. It enables the user to quickly and easily examine the effects of varying thresholds and filters on the number and quality of the resulting genotype calls. In this manner, users can decide on thresholds that are most suitable for their purposes. We show that LinkImputeR can significantly augment the value and utility of NGS data sets, especially in non-model organisms with poor genomic resources. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3873-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Daniel Money
- Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, Nova Scotia, Canada.
| | - Zoë Migicovsky
- Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, Nova Scotia, Canada
| | - Kyle Gardner
- Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, Nova Scotia, Canada
| | - Sean Myles
- Department of Plant and Animal Sciences, Faculty of Agriculture, Dalhousie University, Truro, Nova Scotia, Canada
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Liu N, Irvin MR, Zhi D, Patki A, Beasley TM, Nickerson DA, Hill CE, Chen J, Kimmel SE, Limdi NA. Influence of common and rare genetic variation on warfarin dose among African-Americans and European-Americans using the exome array. Pharmacogenomics 2017; 18:1059-1073. [PMID: 28686080 DOI: 10.2217/pgs-2017-0046] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
AIM We conducted a genome-wide association study using the Illumina Exome Array to identify coding SNPs that may explain additional warfarin dose variability. PATIENTS & METHODS Analysis was performed after adjustment for clinical variables and genetic factors known to influence warfarin dose among 1680 warfarin users (838 European-Americans and 842 African-Americans). Replication was performed in an independent sample. RESULTS We confirmed the influence of known genetic variants on warfarin dose variability. Our study is the first to show the association between rs12772169 and warfarin dose in African-Americans. In addition, genes COX15 and FGF5 showed significant association in European-Americans. CONCLUSION We identified some novel genes/SNPs that underpin warfarin dose response. Further replication is needed to confirm our findings.
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Affiliation(s)
- Nianjun Liu
- Department of Epidemiology & Biostatistics, School of Public Health - Bloomington, Indiana University, Bloomington, IN 47405, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Degui Zhi
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - T Mark Beasley
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, School of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Charles E Hill
- Department of Pathology & Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jinbo Chen
- Department of Biostatistics & Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephen E Kimmel
- Department of Biostatistics & Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nita A Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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203
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Zhang D, Zhao L, Li B, He Z, Wang GT, Liu DJ, Leal SM. SEQSpark: A Complete Analysis Tool for Large-Scale Rare Variant Association Studies Using Whole-Genome and Exome Sequence Data. Am J Hum Genet 2017; 101:115-122. [PMID: 28669402 DOI: 10.1016/j.ajhg.2017.05.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Accepted: 05/23/2017] [Indexed: 01/25/2023] Open
Abstract
Massively parallel sequencing technologies provide great opportunities for discovering rare susceptibility variants involved in complex disease etiology via large-scale imputation and exome and whole-genome sequence-based association studies. Due to modest effect sizes, large sample sizes of tens to hundreds of thousands of individuals are required for adequately powered studies. Current analytical tools are obsolete when it comes to handling these large datasets. To facilitate the analysis of large-scale sequence-based studies, we developed SEQSpark which implements parallel processing based on Spark to increase the speed and efficiency of performing data quality control, annotation, and association analysis. To demonstrate the versatility and speed of SEQSpark, we analyzed whole-genome sequence data from the UK10K, testing for associations with waist-to-hip ratios. The analysis, which was completed in 1.5 hr, included loading data, annotation, principal component analysis, and single variant and rare variant aggregate association analysis of >9 million variants. For rare variant aggregate analysis, an exome-wide significant association (p < 2.5 × 10-6) was observed with CCDC62 (SKAT-O [p = 6.89 × 10-7], combined multivariate collapsing [p = 1.48 × 10-6], and burden of rare variants [p = 1.48 × 10-6]). SEQSpark was also used to analyze 50,000 simulated exomes and it required 1.75 hr for the analysis of a quantitative trait using several rare variant aggregate association methods. Additionally, the performance of SEQSpark was compared to Variant Association Tools and PLINK/SEQ. SEQSpark was always faster and in some situations computation was reduced to a hundredth of the time. SEQSpark will empower large sequence-based epidemiological studies to quickly elucidate genetic variation involved in the etiology of complex traits.
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Roetker NS, Armasu SM, Pankow JS, Lutsey PL, Tang W, Rosenberg MA, Palmer TM, MacLehose RF, Heckbert SR, Cushman M, de Andrade M, Folsom AR. Taller height as a risk factor for venous thromboembolism: a Mendelian randomization meta-analysis. J Thromb Haemost 2017; 15:1334-1343. [PMID: 28445597 PMCID: PMC5504700 DOI: 10.1111/jth.13719] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Indexed: 12/22/2022]
Abstract
Essentials Observational data suggest taller people have a higher risk of venous thromboembolism (VTE). We used Mendelian randomization techniques to further explore this association in three studies. Risk of VTE increased by 30-40% for each 10 cm increment in height. Height was more strongly associated with deep vein thrombosis than with pulmonary embolism. SUMMARY Background Taller height is associated with a greater risk of venous thromboembolism (VTE). Objectives To use instrumental variable (IV) techniques (Mendelian randomization) to further explore this relationship. Methods Participants of European ancestry were included from two cohort studies (Atherosclerosis Risk in Communities [ARIC] study and Cardiovascular Health Study [CHS]) and one case-control study (Mayo Clinic VTE Study [Mayo]). We created two weighted genetic risk scores (GRSs) for height; the full GRS included 668 single-nucleotide polymorphisms (SNPs) from a previously published meta-analysis, and the restricted GRS included a subset of 362 SNPs not associated with weight independently of height. Standard logistic regression and IV models were used to estimate odds ratios (ORs) for VTE per 10-cm increment in height. ORs were pooled across the three studies by the use of inverse variance-weighted random effects meta-analysis. Results Among 9143 ARIC and 3180 CHS participants free of VTE at baseline, there were 367 and 109 incident VTE events. There were 1143 VTE cases and 1292 controls included from Mayo. The pooled ORs from non-IV models and models using the full and restricted GRSs as IVs were 1.27 (95% confidence interval [CI] 1.11-1.46), 1.34 (95% CI 1.04-1.73) and 1.45 (95% CI 1.04-2.01) per 10-cm greater height, respectively. Conclusions Taller height is associated with an increased risk of VTE in adults of European ancestry. Possible explanations for this association, including taller people having a greater venous surface area, a higher number of venous valves, or greater hydrostatic pressure, need to be explored further.
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Affiliation(s)
- N S Roetker
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - S M Armasu
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - J S Pankow
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - P L Lutsey
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - W Tang
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - M A Rosenberg
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
- Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, USA
| | - T M Palmer
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - R F MacLehose
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - S R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - M Cushman
- Department of Medicine, University of Vermont, Burlington, VT, USA
- Department of Pathology, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
| | - M de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - A R Folsom
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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205
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Polymorphisms in Renal Ammonia Metabolism Genes Correlate With 24-Hour Urine pH. Kidney Int Rep 2017; 2:1111-1121. [PMID: 29270519 PMCID: PMC5733879 DOI: 10.1016/j.ekir.2017.06.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 05/23/2017] [Accepted: 06/14/2017] [Indexed: 11/24/2022] Open
Abstract
Introduction Urine pH is critical for net acid and solute excretion, but the genetic factors that contribute to its regulation are incompletely understood. Methods We tested the association of single nucleotide polymorphisms (SNPs) from 16 genes related to ammonia (NH3) metabolism (15 biological candidates selected a priori, 1 selected from a previous genome-wide association study analysis) to that of 24-hour urine pH in 2493 individuals of European descent across 2 different cohorts using linear regression, adjusting for age, sex, and body mass index. Results Of 2871 total SNPs in these genes, 13 SNPs in ATP6V0A4 (a4 subunit of hydrogen− adenosine triphosphatase), SLC9A3 (sodium/hydrogen exchanger, isoform 3), and RHCG (Rhesus C glycoprotein), and 12 SNPs from insulin-like growth factor binding protein 7 (IGFBP7) had a meta-analysis P value <0.01 in the joint analysis plus a consistent direction of effect and at a least suggestive association (P < 0.1) in both cohorts. The maximal effect size (in pH units) for each additional minor allele of the identified SNPs was −0.13 for IGFBP7, −0.08 for ATP6V0A4, 0.06 for RHCG, and −0.06 for SLC9A3; SNP rs34447434 in IGFBP7 had the lowest meta-analysis P value (P = 7.1 × 10−8). After adjusting for net alkali absorption, urine pH remained suggestively associated with multiple SNPs in IGFBP, 1 SNP in ATP6V0A4, and a new SNP in GLS (phosphate-dependent glutaminase). Discussion Overall, these findings suggest that variants in common genes involved in ammonia metabolism may substantively contribute to basal urine pH regulation. These variations might influence the likelihood of developing disease conditions associated with altered urine pH, such as uric acid or calcium phosphate kidney stones.
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206
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Hayete B, Wuest D, Laramie J, McDonagh P, Church B, Eberly S, Lang A, Marek K, Runge K, Shoulson I, Singleton A, Tanner C, Khalil I, Verma A, Ravina B. A Bayesian mathematical model of motor and cognitive outcomes in Parkinson's disease. PLoS One 2017; 12:e0178982. [PMID: 28604798 PMCID: PMC5467836 DOI: 10.1371/journal.pone.0178982] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Accepted: 05/22/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND There are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research. OBJECTIVE To identify predictors of long-term motor and cognitive outcomes and rate of progression in PD. METHODS Newly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data. Machine learning dynamic Bayesian graphical models were used to identify and simulate predictors and outcomes. The outcomes rate of cognition changes are assessed by the Montreal Cognitive Assessment scores, and rate of motor changes are assessed by UPDRS part-III. RESULTS The most robust and consistent longitudinal predictors of cognitive function included older age, baseline Unified Parkinson's Disease Rating Scale (UPDRS) parts I and II, Schwab and England activities of daily living scale, striatal dopamine transporter binding, and SNP rs11724635 in the gene BST1. The most consistent predictor of UPDRS part III was baseline level of activities of daily living (part II). Key findings were replicated using long-term data from an independent cohort study. CONCLUSIONS Baseline function near the time of Parkinson's disease diagnosis, as measured by activities of daily living, is a consistent predictor of long-term motor and cognitive outcomes. Additional predictors identified may further characterize the expected course of Parkinson's disease and suggest mechanisms underlying disease progression. The prognostic model developed in this study can be used to simulate the effects of the prognostic variables on motor and cognitive outcomes, and can be replicated and refined with data from independent longitudinal studies.
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Affiliation(s)
- Boris Hayete
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Diane Wuest
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Jason Laramie
- Novartis, Cambridge, Massachusetts, United States of America
| | - Paul McDonagh
- Alexion Pharmaceuticals, Cambridge, Massachusetts, United States of America
| | - Bruce Church
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Shirley Eberly
- University of Rochester, Rochester, New York, United States of America
| | - Anthony Lang
- Morton and Gloria Movement Disorders Clinic and the Edmond J. Safra Program in Parkinson’s Disease, Toronto Western Hospital and the University of Toronto, Toronto, Ontario, Canada
| | - Kenneth Marek
- Institute for Neurodegenerative Disorders, New Haven, Connecticut, United States of America
| | - Karl Runge
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Ira Shoulson
- Georgetown University, Washington, DC, United States of America
| | - Andrew Singleton
- National Institute on Aging, NIH, Bethesda, Maryland, United States of America
| | - Caroline Tanner
- University of San Francisco & San Francisco Veterans Affairs Medical Center, San Francisco, California, United States of America
| | - Iya Khalil
- GNS Healthcare, Cambridge, Massachusetts, United States of America
| | - Ajay Verma
- Biogen Idec, Cambridge, Massachusetts, United States of America
| | - Bernard Ravina
- Voyager Therapeutics, Cambridge, Massachusetts, United States of America
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207
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Iglesias AI, der Lee SJ, Bonnemaijer PW, Höhn R, Nag A, Gharahkhani P, Khawaja AP, Broer L, Foster PJ, Hammond CJ, Hysi PG, Leeuwen EM, MacGregor S, Mackey DA, Mazur J, Nickels S, Uitterlinden AG, Klaver CC, Amin N, Duijn CM. Haplotype reference consortium panel: Practical implications of imputations with large reference panels. Hum Mutat 2017; 38:1025-1032. [DOI: 10.1002/humu.23247] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 04/18/2017] [Accepted: 05/01/2017] [Indexed: 12/28/2022]
Affiliation(s)
- Adriana I. Iglesias
- Department of Epidemiology Erasmus Medical Center Rotterdam the Netherlands
- Department of Ophthalmology Erasmus Medical Center Rotterdam the Netherlands
| | - Sven J. der Lee
- Department of Epidemiology Erasmus Medical Center Rotterdam the Netherlands
| | - Pieter W.M. Bonnemaijer
- Department of Epidemiology Erasmus Medical Center Rotterdam the Netherlands
- Department of Ophthalmology Erasmus Medical Center Rotterdam the Netherlands
| | - René Höhn
- Department of Ophthalmology Inselspital University Hospital Bern University of Bern Bern Switzerland
- Department of Ophthalmology University Medical Center Mainz Mainz Germany
| | - Abhishek Nag
- Department of Twin Research and Genetic Epidemiology King's College London London UK
| | - Puya Gharahkhani
- Statistical Genetics QIMR Berghofer Medical Research Institute Royal Brisbane Hospital Brisbane Australia
| | - Anthony P. Khawaja
- Department of Public Health and Primary Care University of Cambridge Cambridge UK
- NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology London UK
| | - Linda Broer
- Department of Internal Medicine Erasmus Medical Center Rotterdam the Netherlands
| | - Paul J. Foster
- NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology London UK
| | | | - Pirro G. Hysi
- Department of Twin Research and Genetic Epidemiology King's College London London UK
| | | | - Stuart MacGregor
- Statistical Genetics QIMR Berghofer Medical Research Institute Royal Brisbane Hospital Brisbane Australia
| | - David A. Mackey
- Centre for Ophthalmology and Visual Science Lions Eye Institute University of Western Australia Perth Australia
- School of Medicine Menzies Research Institute Tasmania University of Tasmania Hobart Australia
| | - Johanna Mazur
- Institute of Medical Biostatistics Epidemiology and Informatics (IMBEI) University Medical Center Mainz Germany
| | - Stefan Nickels
- Department of Ophthalmology University Medical Center Mainz Mainz Germany
| | - André G. Uitterlinden
- Department of Epidemiology Erasmus Medical Center Rotterdam the Netherlands
- Department of Internal Medicine Erasmus Medical Center Rotterdam the Netherlands
- Netherlands Consortium for Healthy Ageing Netherlands Genomics Initiative the Hague the Netherlands
| | - Caroline C.W. Klaver
- Department of Epidemiology Erasmus Medical Center Rotterdam the Netherlands
- Department of Ophthalmology Erasmus Medical Center Rotterdam the Netherlands
- Department of Ophthalmology Radboud University Medical Center Nijmegen the Netherlands
| | - Najaf Amin
- Department of Epidemiology Erasmus Medical Center Rotterdam the Netherlands
| | - Cornelia M. Duijn
- Department of Epidemiology Erasmus Medical Center Rotterdam the Netherlands
- Leiden Academic Centre for Drug Research (LACDR) Leiden University the Netherlands
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208
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Oreper D, Cai Y, Tarantino LM, de Villena FPM, Valdar W. Inbred Strain Variant Database (ISVdb): A Repository for Probabilistically Informed Sequence Differences Among the Collaborative Cross Strains and Their Founders. G3 (BETHESDA, MD.) 2017; 7:1623-1630. [PMID: 28592645 PMCID: PMC5473744 DOI: 10.1534/g3.117.041491] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 03/20/2017] [Indexed: 02/07/2023]
Abstract
The Collaborative Cross (CC) is a panel of recently established multiparental recombinant inbred mouse strains. For the CC, as for any multiparental population (MPP), effective experimental design and analysis benefit from detailed knowledge of the genetic differences between strains. Such differences can be directly determined by sequencing, but until now whole-genome sequencing was not publicly available for individual CC strains. An alternative and complementary approach is to infer genetic differences by combining two pieces of information: probabilistic estimates of the CC haplotype mosaic from a custom genotyping array, and probabilistic variant calls from sequencing of the CC founders. The computation for this inference, especially when performed genome-wide, can be intricate and time-consuming, requiring the researcher to generate nontrivial and potentially error-prone scripts. To provide standardized, easy-to-access CC sequence information, we have developed the Inbred Strain Variant Database (ISVdb). The ISVdb provides, for all the exonic variants from the Sanger Institute mouse sequencing dataset, direct sequence information for CC founders and, critically, the imputed sequence information for CC strains. Notably, the ISVdb also: (1) provides predicted variant consequence metadata; (2) allows rapid simulation of F1 populations; and (3) preserves imputation uncertainty, which will allow imputed data to be refined in the future as additional sequencing and genotyping data are collected. The ISVdb information is housed in an SQL database and is easily accessible through a custom online interface (http://isvdb.unc.edu), reducing the analytic burden on any researcher using the CC.
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Affiliation(s)
- Daniel Oreper
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
| | - Yanwei Cai
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
| | - Lisa M Tarantino
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy University of North Carolina, Chapel Hill, North Carolina 27599-7265
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Lineberger Comprehensive Cancer Center
| | - William Valdar
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7265
- Lineberger Comprehensive Cancer Center
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209
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Identification of the functional variant driving ORMDL3 and GSDMB expression in human chromosome 17q12-21 in primary biliary cholangitis. Sci Rep 2017; 7:2904. [PMID: 28588209 PMCID: PMC5460198 DOI: 10.1038/s41598-017-03067-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 04/21/2017] [Indexed: 12/20/2022] Open
Abstract
Numerous genome-wide association studies (GWAS) have been performed to identify susceptibility genes to various human complex diseases. However, in many cases, neither a functional variant nor a disease susceptibility gene have been clarified. Here, we show an efficient approach for identification of a functional variant in a primary biliary cholangitis (PBC)-susceptible region, chromosome 17q12-21 (ORMDL3-GSDMB-ZPBP2-IKZF3). High-density association mapping was carried out based on SNP imputation analysis by using the whole-genome sequence data from a reference panel of 1,070 Japanese individuals (1KJPN), together with genotype data from our previous GWAS (PBC patients: n = 1,389; healthy controls: n = 1,508). Among 23 single nucleotide polymorphisms (SNPs) with P < 1.0 × 10-8, rs12946510 was identified as the functional variant that influences gene expression via alteration of Forkhead box protein O1 (FOXO1) binding affinity in vitro. Moreover, expression-quantitative trait locus (e-QTL) analyses showed that the PBC susceptibility allele of rs12946510 was significantly associated with lower endogenous expression of ORMDL3 and GSDMB in whole blood and spleen. This study not only identified the functional variant in chr.17q12-21 and its molecular mechanism through which it conferred susceptibility to PBC, but it also illustrated an efficient systematic approach for post-GWAS analysis that is applicable to other complex diseases.
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210
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Wang Y, Freedman JA, Liu H, Moorman PG, Hyslop T, George DJ, Lee NH, Patierno SR, Wei Q. Associations between RNA splicing regulatory variants of stemness-related genes and racial disparities in susceptibility to prostate cancer. Int J Cancer 2017; 141:731-743. [PMID: 28510291 DOI: 10.1002/ijc.30787] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 04/24/2017] [Accepted: 05/02/2017] [Indexed: 01/01/2023]
Abstract
Evidence suggests that cells with a stemness phenotype play a pivotal role in oncogenesis, and prostate cells exhibiting this phenotype have been identified. We used two genome-wide association study (GWAS) datasets of African descendants, from the Multiethnic/Minority Cohort Study of Diet and Cancer (MEC) and the Ghana Prostate Study, and two GWAS datasets of non-Hispanic whites, from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and the Breast and Prostate Cancer Cohort Consortium (BPC3), to analyze the associations between genetic variants of stemness-related genes and racial disparities in susceptibility to prostate cancer. We evaluated associations of single-nucleotide polymorphisms (SNPs) in 25 stemness-related genes with prostate cancer risk in 1,609 cases and 2,550 controls of non-Hispanic whites (4,934 SNPs) and 1,144 cases and 1,116 controls of African descendants (5,448 SNPs) with correction by false discovery rate ≤0.2. We identified 32 SNPs in five genes (TP63, ALDH1A1, WNT1, MET and EGFR) that were significantly associated with prostate cancer risk, of which six SNPs in three genes (TP63, ALDH1A1 and WNT1) and eight EGFR SNPs showed heterogeneity in susceptibility between these two racial groups. In addition, 13 SNPs in MET and one in ALDH1A1 were found only in African descendants. The in silico bioinformatics analyses revealed that EGFR rs2072454 and SNPs in linkage with the identified SNPs in MET and ALDH1A1 (r2 > 0.6) were predicted to regulate RNA splicing. These variants may serve as novel biomarkers for racial disparities in prostate cancer risk.
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Affiliation(s)
- Yanru Wang
- Duke Cancer Institute, Duke University Medical Center, Durham, NC.,Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, NC
| | - Jennifer A Freedman
- Duke Cancer Institute, Duke University Medical Center, Durham, NC.,Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, NC
| | - Hongliang Liu
- Duke Cancer Institute, Duke University Medical Center, Durham, NC.,Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, NC
| | - Patricia G Moorman
- Duke Cancer Institute, Duke University Medical Center, Durham, NC.,Department of Community and Family Medicine, Duke University Medical Center, Durham, NC
| | - Terry Hyslop
- Duke Cancer Institute, Duke University Medical Center, Durham, NC.,Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC
| | - Daniel J George
- Duke Cancer Institute, Duke University Medical Center, Durham, NC.,Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, NC
| | - Norman H Lee
- Department of Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Steven R Patierno
- Duke Cancer Institute, Duke University Medical Center, Durham, NC.,Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, NC.,Department of Community and Family Medicine, Duke University Medical Center, Durham, NC.,Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC
| | - Qingyi Wei
- Duke Cancer Institute, Duke University Medical Center, Durham, NC.,Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, NC
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211
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Sapkota Y, Steinthorsdottir V, Morris AP, Fassbender A, Rahmioglu N, De Vivo I, Buring JE, Zhang F, Edwards TL, Jones S, O D, Peterse D, Rexrode KM, Ridker PM, Schork AJ, MacGregor S, Martin NG, Becker CM, Adachi S, Yoshihara K, Enomoto T, Takahashi A, Kamatani Y, Matsuda K, Kubo M, Thorleifsson G, Geirsson RT, Thorsteinsdottir U, Wallace LM, Yang J, Velez Edwards DR, Nyegaard M, Low SK, Zondervan KT, Missmer SA, D'Hooghe T, Montgomery GW, Chasman DI, Stefansson K, Tung JY, Nyholt DR. Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism. Nat Commun 2017; 8:15539. [PMID: 28537267 PMCID: PMC5458088 DOI: 10.1038/ncomms15539] [Citation(s) in RCA: 194] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 04/07/2017] [Indexed: 12/27/2022] Open
Abstract
Endometriosis is a heritable hormone-dependent gynecological disorder, associated with severe pelvic pain and reduced fertility; however, its molecular mechanisms remain largely unknown. Here we perform a meta-analysis of 11 genome-wide association case-control data sets, totalling 17,045 endometriosis cases and 191,596 controls. In addition to replicating previously reported loci, we identify five novel loci significantly associated with endometriosis risk (P<5 × 10−8), implicating genes involved in sex steroid hormone pathways (FN1, CCDC170, ESR1, SYNE1 and FSHB). Conditional analysis identified five secondary association signals, including two at the ESR1 locus, resulting in 19 independent single nucleotide polymorphisms (SNPs) robustly associated with endometriosis, which together explain up to 5.19% of variance in endometriosis. These results highlight novel variants in or near specific genes with important roles in sex steroid hormone signalling and function, and offer unique opportunities for more targeted functional research efforts. Endometriosis is a major cause of infertility. Molecular mechanisms underlying the disease involve genetic and environmental risk factors. In a meta-analysis of eleven GWA studies, Sapkota and colleagues identify five novel risk loci, implicating steroid sex hormone pathways in the pathogenesis.
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Affiliation(s)
- Yadav Sapkota
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia.,Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | | | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Amelie Fassbender
- KULeuven, Department of Development and Regeneration, Organ systems, 3000 Leuven, Belgium.,Department of Obstetrics and Gynaecology, Leuven University Fertility Centre, University Hospital Leuven, 3000 Leuven, Belgium
| | - Nilufer Rahmioglu
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Immaculata De Vivo
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Julie E Buring
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Todd L Edwards
- Institute of Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Sarah Jones
- Vanderbilt Genetics Institute, Division of Epidemiology, Institute of Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Dorien O
- KULeuven, Department of Development and Regeneration, Organ systems, 3000 Leuven, Belgium.,Department of Obstetrics and Gynaecology, Leuven University Fertility Centre, University Hospital Leuven, 3000 Leuven, Belgium
| | - Daniëlle Peterse
- KULeuven, Department of Development and Regeneration, Organ systems, 3000 Leuven, Belgium.,Department of Obstetrics and Gynaecology, Leuven University Fertility Centre, University Hospital Leuven, 3000 Leuven, Belgium
| | - Kathryn M Rexrode
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - Paul M Ridker
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - Andrew J Schork
- Cognitive Science Department, University of California, San Diego, La Jolla, California 92093, USA.,Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Copenhagen University Hospital, DK-2100 Copenhagen, Denmark
| | - Stuart MacGregor
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Christian M Becker
- Endometriosis CaRe Centre, Nuffield Dept of Obstetrics &Gynaecology, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Sosuke Adachi
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 950-2181, Japan
| | - Kosuke Yoshihara
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 950-2181, Japan
| | - Takayuki Enomoto
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 950-2181, Japan
| | - Atsushi Takahashi
- Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan
| | - Yoichiro Kamatani
- Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan
| | - Koichi Matsuda
- Institute of Medical Sciences, The University of Tokyo, Tokyo 108-8639, Japan
| | - Michiaki Kubo
- Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan
| | | | - Reynir T Geirsson
- Department of Obstetrics and Gynecology, Landspitali University Hospital, 101 Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, 101 Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland
| | - Leanne M Wallace
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | | | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Digna R Velez Edwards
- Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Institute of Medicine and Public Health, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Mette Nyegaard
- Department of Biomedicine, Aarhus University, DK-8000 Aarhus, Denmark.,iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, DK-2100 Copenhagen, Denmark
| | - Siew-Kee Low
- Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan
| | - Krina T Zondervan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.,Endometriosis CaRe Centre, Nuffield Dept of Obstetrics &Gynaecology, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Stacey A Missmer
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Thomas D'Hooghe
- KULeuven, Department of Development and Regeneration, Organ systems, 3000 Leuven, Belgium.,Department of Obstetrics and Gynaecology, Leuven University Fertility Centre, University Hospital Leuven, 3000 Leuven, Belgium.,Global Medical Affairs Fertility, Research and Development, Merck KGaA, Darmstadt, Germany
| | - Grant W Montgomery
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Daniel I Chasman
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - Kari Stefansson
- deCODE Genetics/Amgen, 101 Reykjavik, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, 101 Reykjavik, Iceland
| | - Joyce Y Tung
- 23andMe, Inc., 899 W. Evelyn Avenue, Mountain View, California 94041, USA
| | - Dale R Nyholt
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia.,Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland 4059, Australia
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212
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Linkage disequilibrium matches forensic genetic records to disjoint genomic marker sets. Proc Natl Acad Sci U S A 2017; 114:5671-5676. [PMID: 28507140 PMCID: PMC5465933 DOI: 10.1073/pnas.1619944114] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Combining genotypes across datasets is central in facilitating advances in genetics. Data aggregation efforts often face the challenge of record matching-the identification of dataset entries that represent the same individual. We show that records can be matched across genotype datasets that have no shared markers based on linkage disequilibrium between loci appearing in different datasets. Using two datasets for the same 872 people-one with 642,563 genome-wide SNPs and the other with 13 short tandem repeats (STRs) used in forensic applications-we find that 90-98% of forensic STR records can be connected to corresponding SNP records and vice versa. Accuracy increases to 99-100% when ∼30 STRs are used. Our method expands the potential of data aggregation, but it also suggests privacy risks intrinsic in maintenance of databases containing even small numbers of markers-including databases of forensic significance.
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213
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Kieboom BCT, Ligthart S, Dehghan A, Kurstjens S, de Baaij JHF, Franco OH, Hofman A, Zietse R, Stricker BH, Hoorn EJ. Serum magnesium and the risk of prediabetes: a population-based cohort study. Diabetologia 2017; 60:843-853. [PMID: 28224192 PMCID: PMC6518103 DOI: 10.1007/s00125-017-4224-4] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 01/27/2017] [Indexed: 12/15/2022]
Abstract
AIMS/HYPOTHESIS Previous studies have found an association between serum magnesium and incident diabetes; however, this association may be due to reverse causation, whereby diabetes may induce urinary magnesium loss. In contrast, in prediabetes (defined as impaired fasting glucose), serum glucose levels are below the threshold for urinary magnesium wasting and, hence, unlikely to influence serum magnesium levels. Thus, to study the directionality of the association between serum magnesium levels and diabetes, we investigated its association with prediabetes. We also investigated whether magnesium-regulating genes influence diabetes risk through serum magnesium levels. Additionally, we quantified the effect of insulin resistance in the association between serum magnesium levels and diabetes risk. METHODS Within the population-based Rotterdam Study, we used Cox models, adjusted for age, sex, lifestyle factors, comorbidities, kidney function, serum levels of electrolytes and diuretic use, to study the association between serum magnesium and prediabetes/diabetes. In addition, we performed two mediation analyses: (1) to study if common genetic variation in eight magnesium-regulating genes influence diabetes risk through serum magnesium levels; and (2) to quantify the proportion of the effect of serum magnesium levels on diabetes that is mediated through insulin resistance (quantified by HOMA-IR). RESULTS A total of 8555 participants (mean age, 64.7 years; median follow-up, 5.7 years) with normal glucose levels (mean ± SD: 5.46 ± 0.58 mmol/l) at baseline were included. A 0.1 mmol/l decrease in serum magnesium level was associated with an increase in diabetes risk (HR 1.18 [95% CI 1.04, 1.33]), confirming findings from previous studies. Of interest, a similar association was found between serum magnesium levels and prediabetes risk (HR 1.12 [95% CI 1.01, 1.25]). Genetic variation in CLDN19, CNNM2, FXYD2, SLC41A2, and TRPM6 significantly influenced diabetes risk (p < 0.05), and for CNNM2, FXYD2, SLC41A2 and TRPM6 this risk was completely mediated by serum magnesium levels. We found that 29.1% of the effect of serum magnesium levels on diabetes was mediated through insulin resistance, whereas for prediabetes 13.4% was mediated through insulin resistance. CONCLUSIONS/INTERPRETATION Low serum magnesium levels are associated with an increased risk of prediabetes and this increased risk is similar to that of diabetes. Furthermore, common variants in magnesium-regulating genes modify diabetes risk through serum magnesium levels. Both findings support a potential causal role of magnesium in the development of diabetes, where the hypothesised pathway is partly mediated through insulin resistance.
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Affiliation(s)
- Brenda C T Kieboom
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
- Inspectorate for Health Care, Utrecht, the Netherlands
| | - Symen Ligthart
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Steef Kurstjens
- Department of Physiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeroen H F de Baaij
- Department of Physiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Robert Zietse
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands.
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
- Inspectorate for Health Care, Utrecht, the Netherlands.
| | - Ewout J Hoorn
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
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214
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Dennis J, Medina-Rivera A, Truong V, Antounians L, Zwingerman N, Carrasco G, Strug L, Wells P, Trégouët DA, Morange PE, Wilson MD, Gagnon F. Leveraging cell type specific regulatory regions to detect SNPs associated with tissue factor pathway inhibitor plasma levels. Genet Epidemiol 2017; 41:455-466. [PMID: 28421636 DOI: 10.1002/gepi.22049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Revised: 03/07/2017] [Accepted: 03/14/2017] [Indexed: 11/10/2022]
Abstract
Tissue factor pathway inhibitor (TFPI) regulates the formation of intravascular blood clots, which manifest clinically as ischemic heart disease, ischemic stroke, and venous thromboembolism (VTE). TFPI plasma levels are heritable, but the genetics underlying TFPI plasma level variability are poorly understood. Herein we report the first genome-wide association scan (GWAS) of TFPI plasma levels, conducted in 251 individuals from five extended French-Canadian Families ascertained on VTE. To improve discovery, we also applied a hypothesis-driven (HD) GWAS approach that prioritized single nucleotide polymorphisms (SNPs) in (1) hemostasis pathway genes, and (2) vascular endothelial cell (EC) regulatory regions, which are among the highest expressers of TFPI. Our GWAS identified 131 SNPs with suggestive evidence of association (P-value < 5 × 10-8 ), but no SNPs reached the genome-wide threshold for statistical significance. Hemostasis pathway genes were not enriched for TFPI plasma level associated SNPs (global hypothesis test P-value = 0.147), but EC regulatory regions contained more TFPI plasma level associated SNPs than expected by chance (global hypothesis test P-value = 0.046). We therefore stratified our genome-wide SNPs, prioritizing those in EC regulatory regions via stratified false discovery rate (sFDR) control, and reranked the SNPs by q-value. The minimum q-value was 0.27, and the top-ranked SNPs did not show association evidence in the MARTHA replication sample of 1,033 unrelated VTE cases. Although this study did not result in new loci for TFPI, our work lays out a strategy to utilize epigenomic data in prioritization schemes for future GWAS studies.
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Affiliation(s)
- Jessica Dennis
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Alejandra Medina-Rivera
- Program in Genetics and Genome Biology, the Hospital for Sick Children, Toronto, Canada.,Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Vinh Truong
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Lina Antounians
- Program in Genetics and Genome Biology, the Hospital for Sick Children, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Nora Zwingerman
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Giovana Carrasco
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Lisa Strug
- Program in Genetics and Genome Biology, the Hospital for Sick Children, Toronto, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Phil Wells
- Ottawa Hospital Research Institute, Ottawa, Canada
| | - David-Alexandre Trégouët
- Sorbonne Universités, UPMC Univ Paris 06, Paris, France.,INSERM, UMR_S 1166, Paris, France.,ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - Pierre-Emmanuel Morange
- INSERM, UMR_S 1062, Marseille, France.,Inra, UMR_INRA 1260, Marseille, France.,Aix Marseille Université, Marseille, France
| | - Michael D Wilson
- Program in Genetics and Genome Biology, the Hospital for Sick Children, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada.,Heart & Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, Toronto, Canada
| | - France Gagnon
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
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215
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Mitt M, Kals M, Pärn K, Gabriel SB, Lander ES, Palotie A, Ripatti S, Morris AP, Metspalu A, Esko T, Mägi R, Palta P. Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel. Eur J Hum Genet 2017; 25:869-876. [PMID: 28401899 PMCID: PMC5520064 DOI: 10.1038/ejhg.2017.51] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Revised: 02/14/2017] [Accepted: 02/25/2017] [Indexed: 02/08/2023] Open
Abstract
Genetic imputation is a cost-efficient way to improve the power and resolution of genome-wide association (GWA) studies. Current publicly accessible imputation reference panels accurately predict genotypes for common variants with minor allele frequency (MAF)≥5% and low-frequency variants (0.5≤MAF<5%) across diverse populations, but the imputation of rare variation (MAF<0.5%) is still rather limited. In the current study, we evaluate imputation accuracy achieved with reference panels from diverse populations with a population-specific high-coverage (30 ×) whole-genome sequencing (WGS) based reference panel, comprising of 2244 Estonian individuals (0.25% of adult Estonians). Although the Estonian-specific panel contains fewer haplotypes and variants, the imputation confidence and accuracy of imputed low-frequency and rare variants was significantly higher. The results indicate the utility of population-specific reference panels for human genetic studies.
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Affiliation(s)
- Mario Mitt
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Mart Kals
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Kalle Pärn
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Stacey B Gabriel
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Andrew P Morris
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Priit Palta
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
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216
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Yamazoe K, Meguro A, Takeuchi M, Shibuya E, Ohno S, Mizuki N. Comprehensive analysis of the association between UBAC2 polymorphisms and Behçet's disease in a Japanese population. Sci Rep 2017; 7:742. [PMID: 28389674 PMCID: PMC5429716 DOI: 10.1038/s41598-017-00877-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 03/20/2017] [Indexed: 02/06/2023] Open
Abstract
Behçet’s disease (BD) is reportedly associated with polymorphisms of the ubiquitin-associated domain containing 2 (UBAC2) gene in Turkish, Italian, and Chinese populations. Here we investigated whether UBAC2 polymorphisms were associated with BD in a Japanese population. Using data from 611 Japanese BD patients and 737 Japanese controls who participated in our previous genome-wide association study, we analyzed the 58 genotyped single-nucleotide polymorphisms (SNPs) in the region 100 kb upstream and downstream of UBAC2. We also performed imputation analysis in the region, with 562 imputed SNPs included in the statistical analyses. Association testing revealed that the T allele of rs9517723 in the lncRNA LOC107984558 was significantly associated with ocular and central nervous system (CNS) lesions and showed the strongest association under the recessive model (TT vs. CT+CC: ocular lesion, Pc = 0.0099, OR = 1.56; CNS lesion, Pc = 0.0052, OR = 3.42). Expression analysis revealed that rs9517723 TT homozygotes showed significantly increased UBAC2 expression (P < 0.05). Our findings suggest that enhanced UBAC2 expression associated with the homozygous risk allele (TT) of rs9517723 could induce overactivation of ubiquitination-related pathway, resulting in the development of ocular and CNS lesions in BD.
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Affiliation(s)
- Kyoko Yamazoe
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, 236-0004, Japan
| | - Akira Meguro
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, 236-0004, Japan.
| | - Masaki Takeuchi
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, 236-0004, Japan.,Inflammatory Disease Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
| | - Etsuko Shibuya
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, 236-0004, Japan
| | - Shigeaki Ohno
- Department of Ophthalmology, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, 060-8638, Japan
| | - Nobuhisa Mizuki
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, 236-0004, Japan
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217
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Chen G, Doumatey AP, Zhou J, Lei L, Bentley AR, Tekola-Ayele F, Adebamowo SN, Baker JL, Fasanmade O, Okafor G, Eghan B, Agyenim-Boateng K, Amoah A, Adebamowo C, Acheampong J, Johnson T, Oli J, Shriner D, Adeyemo AA, Rotimi CN. Genome-wide analysis identifies an african-specific variant in SEMA4D associated with body mass index. Obesity (Silver Spring) 2017; 25:794-800. [PMID: 28296344 PMCID: PMC5373947 DOI: 10.1002/oby.21804] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 01/23/2017] [Accepted: 01/24/2017] [Indexed: 12/23/2022]
Abstract
OBJECTIVE The prevalence of obesity varies between ethnic groups. No genome-wide association study (GWAS) for body mass index (BMI) has been conducted in continental Africans. METHODS We performed a GWAS for BMI in 1,570 West Africans (WA). Replication was conducted in independent samples of WA (n = 1,411) and African Americans (AA) (n = 9,020). RESULTS We identified a novel genome-wide significant African-specific locus for BMI (SEMA4D, rs80068415; minor allele frequency = 0.008, P = 2.10 × 10-8 ). This finding was replicated in independent samples of WA (P = 0.013) and AA (P = 0.017). Individuals with obesity had higher serum SEMA4D levels compared to those without obesity (P < 0.0001), and elevated levels of serum SEMA4D were associated with increased obesity risk (OR = 4.2, P < 1 × 10-4 ). The prevalence of obesity was higher in individuals with the CT versus TT genotypes (55.6% vs. 22.9%). CONCLUSIONS A novel variant in SEMA4D was significantly associated with BMI. Carriers of the C allele were 4.6 BMI units heavier than carriers of the T allele (P = 0.0007). This variant is monomorphic in Europeans and Asians, highlighting the importance of studying diverse populations. While there is evidence for the involvement of SEMA4D in inflammatory processes, this study is the first to implicate SEMA4D in obesity pathophysiology.
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Affiliation(s)
- Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Lin Lei
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Fasil Tekola-Ayele
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Sally N Adebamowo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Jennifer L Baker
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Olufemi Fasanmade
- University of Lagos, College of Medicine, Endocrine and Metabolic Unit, Lagos, Nigeria
| | - Godfrey Okafor
- University of Nigeria Teaching Hospital, Department of Hematology, Enugu, Nigeria
| | - Benjamin Eghan
- University of Science and Technology, Department of Medicine, Kumasi, Ghana
| | | | - Albert Amoah
- University of Ghana Medical School, Department of Medicine and Therapeutics, Accra, Ghana
| | | | - Joseph Acheampong
- University of Science and Technology, Department of Medicine, Kumasi, Ghana
| | - Thomas Johnson
- University of Lagos, College of Medicine, Endocrine and Metabolic Unit, Lagos, Nigeria
| | - Johnnie Oli
- University of Nigeria Teaching Hospital, Department of Hematology, Enugu, Nigeria
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA, 20892
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218
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Monnereau C, Jansen PW, Tiemeier H, Jaddoe VWV, Felix JF. Influence of genetic variants associated with body mass index on eating behavior in childhood. Obesity (Silver Spring) 2017; 25:765-772. [PMID: 28245097 PMCID: PMC5496668 DOI: 10.1002/oby.21778] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 12/23/2016] [Accepted: 12/26/2016] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Childhood eating behaviors are associated with body mass index (BMI). Recent genome-wide association studies have identified many single-nucleotide polymorphisms (SNPs) associated with adult and childhood BMI. This study hypothesized that these SNPs also influence eating behavior. METHODS In a population-based prospective cohort study among 3,031 children (mean age [standard deviation]: 4.0 [0.1] years), two weighted genetic risk scores, based on 15 childhood and 97 adult BMI SNPs, and ten individual appetite- and/or satiety-related SNPs were tested for association with food fussiness, food responsiveness, enjoyment of food, satiety responsiveness, and slowness in eating. RESULTS The 15 SNP-based childhood BMI genetic risk score was not associated with the eating behavior subscales. The 97 SNP-based adult BMI genetic risk score was nominally associated with satiety responsiveness (β: -0.007 standard deviation, 95% confidence interval [CI] -0.013, 0.000). Of the 10 individual SNPs, rs11030104 in BDNF and rs10733682 in LMX1B were nominally associated with satiety responsiveness (β: -0.057 standard deviation, 95% CI -0.112, -0.002). CONCLUSIONS These findings do not strongly support the hypothesis that BMI-associated SNPs also influence eating behavior at this age. A potential role for BMI SNPs in satiety responsiveness during childhood was observed; however, no associations with the other eating behavior subscales were found.
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Affiliation(s)
- Claire Monnereau
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Pauline W Jansen
- Institute of Psychology, Erasmus University, Rotterdam, the Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Psychiatry, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
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219
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Hong EP, Go MJ, Kim HL, Park JW. Risk prediction of pulmonary tuberculosis using genetic and conventional risk factors in adult Korean population. PLoS One 2017; 12:e0174642. [PMID: 28355295 PMCID: PMC5371343 DOI: 10.1371/journal.pone.0174642] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 03/13/2017] [Indexed: 12/16/2022] Open
Abstract
A complex interplay among host, pathogen, and environmental factors is believed to contribute to the risk of developing pulmonary tuberculosis (PTB). The lack of replication of published genome-wide association study (GWAS) findings limits the clinical utility of reported single nucleotide polymorphisms (SNPs). We conducted a GWAS using 467 PTB cases and 1,313 healthy controls obtained from two community-based cohorts in Korea. We evaluated the performance of PTB risk models based on different combinations of genetic and nongenetic factors and validated the results in an independent Korean population comprised of 179 PTB cases and 500 healthy controls. We demonstrated the polygenic nature of PTB and nongenetic factors such as age, sex, and body mass index (BMI) were strongly associated with PTB risk. None of the SNPs achieved genome-wide significance; instead, we were able to replicate the associations between PTB and ten SNPs near or in the genes, CDCA7, GBE1, GADL1, SPATA16, C6orf118, KIAA1432, DMRT2, CTR9, CCDC67, and CDH13, which may play roles in the immune and inflammatory pathways. Among the replicated SNPs, an intergenic SNP, rs9365798, located downstream of the C6orf118 gene showed the most significant association under the dominant model (OR = 1.59, 95% CI 1.32–1.92, P = 2.1×10−6). The performance of a risk model combining the effects of ten replicated SNPs and six nongenetic factors (i.e., age, sex, BMI, cigarette smoking, systolic blood pressure, and hemoglobin) were validated in the replication set (AUC = 0.80, 95% CI 0.76–0.84). The strategy of combining genetic and nongenetic risk factors ultimately resulted in better risk prediction for PTB in the adult Korean population.
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Affiliation(s)
- Eun Pyo Hong
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon-si, Ganwon-do, Republic of Korea
| | - Min Jin Go
- Center for Genome Science, National Institute of Health, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Hyung-Lae Kim
- Department of Biochemistry, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Ji Wan Park
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon-si, Ganwon-do, Republic of Korea
- * E-mail:
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220
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Scanning indels in the 5q22.1 region and identification of the TMEM232 susceptibility gene that is associated with atopic dermatitis in the Chinese Han population. Gene 2017; 617:17-23. [PMID: 28351738 DOI: 10.1016/j.gene.2017.03.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 01/21/2017] [Accepted: 03/24/2017] [Indexed: 11/22/2022]
Abstract
BACKGROUND Atopic dermatitis (AD) is a chronic inflammatory skin disease. The 5q22.1 region was found to have an association with AD in our previous genome-wide association study (GWAS). OBJECTIVE To identify the AD susceptibility gene in 5q22.1 and observe its expression in AD tissues. METHODS Suggestive indels from the GWAS data were genotyped in 3013 AD patients and 5075 controls from the Chinese Han population with the SequenomMassArray system. Association, Bayesian and bioinformatics analyses were used to identify possible causal indels and genes in the 5q22.1 region. Immunohistochemistry (IHC) was performed to observe protein expression in the tissues. PLINK 1.07 software was used for all statistical analyses. RESULTS The genotyping and association analysis showed that six deletions and four SNPs were associated with AD (P<0.005). The rs11357450 (Pcombined=7.79E-04, OR=1.39, logBayes Factor=1.29) deletion located in TMEM232 was identified to be the strongest variant. Analysis of the genetic model revealed that the dominant model best described rs11357450 (P=1.96E-03, OR=1.22; 95% CI=1.07-1.37). IHC showed that the expression of TMEM232 decreased gradually from the granular layer to the basal layer in AD, but in normal tissues, this trend was reversed. Additionally, positive cytoplasm staining was found in lymphocytes around the blood vessels in AD. CONCLUSIONS The study indicates that TMEM232 in the 5q22.1 region is the causal gene for AD in the Chinese Han population.
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221
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Ran S, Zhang L, Liu L, Feng AP, Pei YF, Zhang L, Han YY, Lin Y, Li X, Kong WW, You XY, Zhao W, Tian Q, Shen H, Zhang YH, Deng HW. Gene-based genome-wide association study identified 19p13.3 for lean body mass. Sci Rep 2017; 7:45025. [PMID: 28322352 PMCID: PMC5359571 DOI: 10.1038/srep45025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 02/17/2017] [Indexed: 12/15/2022] Open
Abstract
Lean body mass (LBM) is a complex trait for human health. To identify genomic loci underlying LBM, we performed a gene-based genome-wide association study of lean mass index (LMI) in 1000 unrelated Caucasian subjects, and replicated in 2283 unrelated Caucasians subjects. Gene-based association analyses highlighted the significant associations of three genes UQCR, TCF3 and MBD3 in one single locus 19p13.3 (discovery p = 6.10 × 10-5, 1.65 × 10-4 and 1.10 × 10-4; replication p = 2.21 × 10-3, 1.84 × 10-3 and 6.95 × 10-3; combined p = 2.26 × 10-6, 4.86 × 10-6 and 1.15 × 10-5, respectively). These results, together with the known functional relevance of the three genes to LMI, suggested that the 19p13.3 region containing UQCR, TCF3 and MBD3 genes was a novel locus underlying lean mass variation.
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Affiliation(s)
- Shu Ran
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Lei Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, PR China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
| | - Lu Liu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, PR China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
| | - An-Ping Feng
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, PR China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
| | - Yu-Fang Pei
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
- Department of Epidemiology and Statistics, School of Public Health, Soochow University, Jiangsu, PR China
| | - Lei Zhang
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Ying-Ying Han
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Yong Lin
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, PR China
| | - Xiao Li
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, PR China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
| | - Wei-Wen Kong
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, PR China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
| | - Xin-Yi You
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, PR China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
| | - Wen Zhao
- Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, PR China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
| | - Qing Tian
- Department of Biostatistics, Tulane University, New Orleans, Louisiana, USA
| | - Hui Shen
- Department of Biostatistics, Tulane University, New Orleans, Louisiana, USA
| | - Yong-Hong Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Jiangsu, PR China
- Department of Epidemiology and Statistics, School of Public Health, Soochow University, Jiangsu, PR China
| | - Hong-Wen Deng
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, PR China
- Department of Biostatistics, Tulane University, New Orleans, Louisiana, USA
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Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. I: Multivariate Gaussian priors for marker effects and derivation of the joint probability mass function of genotypes. J Theor Biol 2017; 417:8-19. [PMID: 28043819 DOI: 10.1016/j.jtbi.2016.12.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 11/30/2016] [Accepted: 12/28/2016] [Indexed: 11/23/2022]
Abstract
It is important to consider heterogeneity of marker effects and allelic frequencies in across population genome-wide prediction studies. Moreover, all regression models used in genome-wide prediction overlook randomness of genotypes. In this study, a family of hierarchical Bayesian models to perform across population genome-wide prediction modeling genotypes as random variables and allowing population-specific effects for each marker was developed. Models shared a common structure and differed in the priors used and the assumption about residual variances (homogeneous or heterogeneous). Randomness of genotypes was accounted for by deriving the joint probability mass function of marker genotypes conditional on allelic frequencies and pedigree information. As a consequence, these models incorporated kinship and genotypic information that not only permitted to account for heterogeneity of allelic frequencies, but also to include individuals with missing genotypes at some or all loci without the need for previous imputation. This was possible because the non-observed fraction of the design matrix was treated as an unknown model parameter. For each model, a simpler version ignoring population structure, but still accounting for randomness of genotypes was proposed. Implementation of these models and computation of some criteria for model comparison were illustrated using two simulated datasets. Theoretical and computational issues along with possible applications, extensions and refinements were discussed. Some features of the models developed in this study make them promising for genome-wide prediction, the use of information contained in the probability distribution of genotypes is perhaps the most appealing. Further studies to assess the performance of the models proposed here and also to compare them with conventional models used in genome-wide prediction are needed.
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Luo L, Orlow I, Kanetsky PA, Thomas NE, Fang S, Lee JE, Berwick M, Lee JH. No prognostic value added by vitamin D pathway SNPs to current prognostic system for melanoma survival. PLoS One 2017; 12:e0174234. [PMID: 28323902 PMCID: PMC5360355 DOI: 10.1371/journal.pone.0174234] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 03/06/2017] [Indexed: 12/31/2022] Open
Abstract
The prognostic improvement attributed to genetic markers over current prognostic system has not been well studied for melanoma. The goal of this study is to evaluate the added prognostic value of Vitamin D Pathway (VitD) SNPs to currently known clinical and demographic factors such as age, sex, Breslow thickness, mitosis and ulceration (CDF). We utilized two large independent well-characterized melanoma studies: the Genes, Environment, and Melanoma (GEM) and MD Anderson studies, and performed variable selection of VitD pathway SNPs and CDF using Random Survival Forest (RSF) method in addition to Cox proportional hazards models. The Harrell's C-index was used to compare the performance of model predictability. The population-based GEM study enrolled 3,578 incident cases of cutaneous melanoma (CM), and the hospital-based MD Anderson study consisted of 1,804 CM patients. Including both VitD SNPs and CDF yielded C-index of 0.85, which provided slight but not significant improvement by CDF alone (C-index = 0.83) in the GEM study. Similar results were observed in the independent MD Anderson study (C-index = 0.84 and 0.83, respectively). The Cox model identified no significant associations after adjusting for multiplicity. Our results do not support clinically significant prognostic improvements attributable to VitD pathway SNPs over current prognostic system for melanoma survival.
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Affiliation(s)
- Li Luo
- Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico, United States of America
- University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico, United States of America
| | - Irene Orlow
- Department of Epidemiology and Biostatistics, Epidemiology Service, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Peter A. Kanetsky
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Nancy E. Thomas
- Department of Dermatology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Shenying Fang
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Jeffrey E. Lee
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Marianne Berwick
- Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico, United States of America
- University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico, United States of America
| | - Ji-Hyun Lee
- Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico, United States of America
- University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico, United States of America
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Crandall CJ, Manson JE, Hohensee C, Horvath S, Wactawski-Wende J, LeBlanc ES, Vitolins MZ, Nassir R, Sinsheimer JS. Association of genetic variation in the tachykinin receptor 3 locus with hot flashes and night sweats in the Women's Health Initiative Study. Menopause 2017; 24:252-261. [PMID: 28231077 PMCID: PMC5327841 DOI: 10.1097/gme.0000000000000763] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Vasomotor symptoms (VMS, ie, hot flashes or night sweats) are reported by many, but not all, women. The extent to which VMS are genetically determined is unknown. We evaluated the relationship of genetic variation and VMS. METHODS In this observational study, we accessed data from three genome-wide association studies (GWAS) (SNP Health Association Resource cohort [SHARe], WHI Memory Study cohort [WHIMS+], and Genome-Wide Association Studies of Treatment Response in Randomized Clinical Trials [GARNET] studies, total n = 17,695) of European American, African American, and Hispanic American postmenopausal women aged 50 to 79 years at baseline in the Women's Health Initiative Study. We examined genetic variation in relation to VMS (yes/no) in each study and using trans-ethnic inverse variance fixed-effects meta-analysis. A total of 11,078,977 single-nucleotide polymorphisms (SNPs) met the quality criteria. RESULTS After adjustment for covariates and population structure, three SNPs (on chromosomes 3 and 11) were associated with VMS at the genome-wide threshold of 5 × 10 in the African American SHARe GWAS, but were not associated in the other cohorts. In the meta-analysis, 14 SNPs, all located on chromosome 4 in the tachykinin receptor 3 (TACR3) locus, however, had P < 5 × 10. These SNPs' effect sizes were similar across studies/participants' ancestry (odds ratio ∼1.5). CONCLUSIONS Genetic variation in TACR3 may contribute to the risk of VMS. To our knowledge, this is the first GWAS to examine SNPs associated with VMS. These results support the biological hypothesis of a role for TACR3 in VMS, which was previously hypothesized from animal and human studies. Further study of these variants may lead to new insights into the biological pathways involved in VMS, which are poorly understood.
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Affiliation(s)
- Carolyn J. Crandall
- Dept. of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90024, USA
| | - JoAnn E. Manson
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, 02215, USA
| | - Chancellor Hohensee
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Steve Horvath
- Dept. of Human Genetics and Biostatistics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90024, USA
| | - Jean Wactawski-Wende
- Dept. of Epidemiology and Environmental Health, University at Buffalo, the State University of NY, Buffalo, NY, 14214, USA
| | - Erin S. LeBlanc
- Center for Health Research NW, Kaiser Permanente, Portland, OR, 97239, USA
| | - Mara Z. Vitolins
- Dept of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA
| | - Rami Nassir
- Department of Biochemistry and Molecular Medicine, University of California-Davis Davis, CA, 95616, USA
| | - Janet S. Sinsheimer
- Dept. of Human Genetics and Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90024, USA
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Zhang Y, Wei Y, Jiang B, Chen L, Bai H, Zhu X, Li X, Zhang H, Yang Q, Ma J, Xu Y, Ben J, Christiani DC, Chen Q. Scavenger Receptor A1 Prevents Metastasis of Non-Small Cell Lung Cancer via Suppression of Macrophage Serum Amyloid A1. Cancer Res 2017; 77:1586-1598. [PMID: 28202524 DOI: 10.1158/0008-5472.can-16-1569] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 12/27/2016] [Accepted: 01/03/2017] [Indexed: 11/16/2022]
Abstract
Mechanisms of cross-talk between tumor cells and tumor-associated macrophages (TAM), which drive metastasis, are not fully understood. Scavenger receptor A1 (SR-A1) expressed primarily in macrophages has been associated with lung tumorigenesis. In this study, we used population genetics, transcriptomics, and functional analyses to uncover how SR-A1 is involved in lung cancer and its prognosis. SR-A1 genetic variants were investigated for possible association with survival of advanced stage NSCLC patients in the Harvard Lung Cancer Study cohort. Two SNPs (rs17484273, rs1484751) in SR-A1 were associated significantly with poor overall survival in this cohort. Data from The Cancer Genome Atlas showed considerable downregulation of SR-A1 in lung tumor tissues. The association of SR-A1 with prognosis was validated in animal models in the context of lung cancer metastasis. Macrophages derived from mice genetically deficient for SR-A1 exhibited accelerated metastasis in a model of lung cancer. On the other hand, tumor cell seeding, migration, and invasion, as well as macrophage accumulation in lung cancer tissue, were enhanced in SR-A1-deficient mice. SR-A1 deletion upregulated serum amyloid A1 (SAA1) in macrophages via MAPK/IκB/NFκB signaling. SAA1 promoted tumor cell invasion and macrophage migration in vitro and in vivo, but these effects were blocked by administration of an anti-SAA1 antibody. Overall, our findings show how SR-A1 suppresses lung cancer metastasis by downregulating SAA1 production in TAMs. Cancer Res; 77(7); 1586-98. ©2017 AACR.
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Affiliation(s)
- Yan Zhang
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Yongyue Wei
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Bin Jiang
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Lili Chen
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Hui Bai
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Xudong Zhu
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Xiaoyu Li
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Hanwen Zhang
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Qing Yang
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Junqing Ma
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Yong Xu
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China
| | - Jingjing Ben
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China.
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Qi Chen
- Department of Pathophysiology, Key Laboratory of Cardiovascular Disease and Molecular Intervention, Nanjing Medical University, Nanjing, China.
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Humbert M, Ayday E, Hubaux JP, Telenti A. Quantifying Interdependent Risks in Genomic Privacy. ACM TRANSACTIONS ON PRIVACY AND SECURITY 2017. [DOI: 10.1145/3035538] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The rapid progress in human-genome sequencing is leading to a high availability of genomic data. These data is notoriously very sensitive and stable in time, and highly correlated among relatives. In this article, we study the implications of these familial correlations on kin genomic privacy. We formalize the problem and detail efficient reconstruction attacks based on graphical models and belief propagation. With our approach, an attacker can infer the genomes of the relatives of an individual whose genome or phenotype are observed by notably relying on Mendel’s Laws, statistical relationships between the genomic variants, and between the genome and the phenotype. We evaluate the effect of these dependencies on privacy with respect to the amount of observed variants and the relatives sharing them. We also study how the algorithmic performance evolves when we take these various relationships into account. Furthermore, to quantify the level of genomic privacy as a result of the proposed inference attack, we discuss possible definitions of
genomic privacy
metrics, and compare their values and evolution. Genomic data reveals Mendelian disorders and the likelihood of developing severe diseases, such as Alzheimer’s. We also introduce the quantification of
health privacy
, specifically, the measure of how well the predisposition to a disease is concealed from an attacker. We evaluate our approach on actual genomic data from a pedigree and show the threat extent by combining data gathered from a genome-sharing website as well as an online social network.
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He Z, Zhang D, Renton AE, Li B, Zhao L, Wang GT, Goate AM, Mayeux R, Leal SM. The Rare-Variant Generalized Disequilibrium Test for Association Analysis of Nuclear and Extended Pedigrees with Application to Alzheimer Disease WGS Data. Am J Hum Genet 2017; 100:193-204. [PMID: 28065470 PMCID: PMC5294711 DOI: 10.1016/j.ajhg.2016.12.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 12/06/2016] [Indexed: 01/10/2023] Open
Abstract
Whole-genome and exome sequence data can be cost-effectively generated for the detection of rare-variant (RV) associations in families. Causal variants that aggregate in families usually have larger effect sizes than those found in sporadic cases, so family-based designs can be a more powerful approach than population-based designs. Moreover, some family-based designs are robust to confounding due to population admixture or substructure. We developed a RV extension of the generalized disequilibrium test (GDT) to analyze sequence data obtained from nuclear and extended families. The GDT utilizes genotype differences of all discordant relative pairs to assess associations within a family, and the RV extension combines the single-variant GDT statistic over a genomic region of interest. The RV-GDT has increased power by efficiently incorporating information beyond first-degree relatives and allows for the inclusion of covariates. Using simulated genetic data, we demonstrated that the RV-GDT method has well-controlled type I error rates, even when applied to admixed populations and populations with substructure. It is more powerful than existing family-based RV association methods, particularly for the analysis of extended pedigrees and pedigrees with missing data. We analyzed whole-genome sequence data from families affected by Alzheimer disease to illustrate the application of the RV-GDT. Given the capability of the RV-GDT to adequately control for population admixture or substructure and analyze pedigrees with missing genotype data and its superior power over other family-based methods, it is an effective tool for elucidating the involvement of RVs in the etiology of complex traits.
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Affiliation(s)
- Zongxiao He
- Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Di Zhang
- Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alan E. Renton
- Department of Neuroscience and Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029, USA
| | - Biao Li
- Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Linhai Zhao
- Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Gao T. Wang
- Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alison M. Goate
- Department of Neuroscience and Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029, USA
| | - Richard Mayeux
- Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain and Gertrude H. Sergievsky Center, Columbia University, New York, NY 10027, USA
| | - Suzanne M. Leal
- Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA,Corresponding author
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228
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Securing the use of existing sample collections for future human genetic research. Eur J Hum Genet 2017; 25:522-529. [PMID: 28145429 DOI: 10.1038/ejhg.2017.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 11/11/2016] [Accepted: 12/24/2016] [Indexed: 11/08/2022] Open
Abstract
Hundreds of thousands of individuals have been genotyped in the past decades using genotyping arrays, representing both a valuable data resource for future biomedical research and a substantial investment in human genetic research. However, novel chip designs and their altered sets of single-nucleotide polymorphisms (SNPs) pose the question of how well established data resources, such as large samples of healthy controls genotyped on legacy arrays, can be combined with newer samples genotyped on those novel arrays using genotype imputation. We exemplarily investigated this question based on genotype data of 30 European and 30 African unrelated samples from the 1000 Genomes project and on markers present on two legacy SNP arrays, namely Affymetrix's Human SNP 6.0 and Illumina's 550k array, and three newer arrays, namely two Axiom arrays from Affymetrix and an OmniExpress array from Illumina. We cross-compared the imputation accuracy as well as efficacy and assessed genotype concordance among these arrays. Although the accuracy of genotype prediction was uniformly high across all arrays, the imputation efficacy, that is, the proportion of successfully imputed markers, differed considerably between array combinations in both sample sets, with legacy arrays showing a trend towards lower efficacy values compared with newer arrays when serving as imputation basis. We conclude that, given the substantial losses of markers covered by the legacy arrays, the re-genotyping of existing samples sets, in particular those of healthy population controls, would be a worthwhile endeavor to secure their continued use in the future.
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Miar Y, Sargolzaei M, Schenkel FS. A comparison of different algorithms for phasing haplotypes using Holstein cattle genotypes and pedigree data. J Dairy Sci 2017; 100:2837-2849. [PMID: 28161175 DOI: 10.3168/jds.2016-11590] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 12/09/2016] [Indexed: 01/25/2023]
Abstract
Phasing genotypes to haplotypes is becoming increasingly important due to its applications in the study of diseases, population and evolutionary genetics, imputation, and so on. Several studies have focused on the development of computational methods that infer haplotype phase from population genotype data. The aim of this study was to compare phasing algorithms implemented in Beagle, Findhap, FImpute, Impute2, and ShapeIt2 software using 50k and 777k (HD) genotyping data. Six scenarios were considered: no-parents, sire-progeny pairs, sire-dam-progeny trios, each with and without pedigree information in Holstein cattle. Algorithms were compared with respect to their phasing accuracy and computational efficiency. In the studied population, Beagle and FImpute were more accurate than other phasing algorithms. Across scenarios, phasing accuracies for Beagle and FImpute were 99.49-99.90% and 99.44-99.99% for 50k, respectively, and 99.90-99.99% and 99.87-99.99% for HD, respectively. Generally, FImpute resulted in higher accuracy when genotypic information of at least one parent was available. In the absence of parental genotypes and pedigree information, Beagle and Impute2 (with double the default number of states) were slightly more accurate than FImpute. Findhap gave high phasing accuracy when parents' genotypes and pedigree information were available. In terms of computing time, Findhap was the fastest algorithm followed by FImpute. FImpute was 30 to 131, 87 to 786, and 353 to 1,400 times faster across scenarios than Beagle, ShapeIt2, and Impute2, respectively. In summary, FImpute and Beagle were the most accurate phasing algorithms. Moreover, the low computational requirement of FImpute makes it an attractive algorithm for phasing genotypes of large livestock populations.
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Affiliation(s)
- Younes Miar
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, Nova Scotia, Canada B2N 5E3; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada N1G 2W1.
| | - Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada N1G 2W1; The Semex Alliance, Guelph, Ontario, Canada N1H 6J2
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada N1G 2W1
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230
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Kalmbach DA, Schneider LD, Cheung J, Bertrand SJ, Kariharan T, Pack AI, Gehrman PR. Genetic Basis of Chronotype in Humans: Insights From Three Landmark GWAS. Sleep 2017; 40:2662182. [PMID: 28364486 PMCID: PMC6084759 DOI: 10.1093/sleep/zsw048] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2016] [Indexed: 01/22/2023] Open
Abstract
Study Objectives Chronotype, or diurnal preference, refers to behavioral manifestations of the endogenous circadian system that governs preferred timing of sleep and wake. As variations in circadian timing and system perturbations are linked to disease development, the fundamental biology of chronotype has received attention for its role in the regulation and dysregulation of sleep and related illnesses. Family studies indicate that chronotype is a heritable trait, thus directing attention toward its genetic basis. Although discoveries from molecular studies of candidate genes have shed light onto its genetic architecture, the contribution of genetic variation to chronotype has remained unclear with few related variants identified. In the advent of large-scale genome-wide association studies (GWAS), scientists now have the ability to discover novel common genetic variants associated with complex phenotypes. Three recent large-scale GWASs of chronotype were conducted on subjects of European ancestry from the 23andMe cohort and the UK Biobank. This review discusses the findings of these landmark GWASs in the context of prior research. Methods We systematically reviewed and compared methodological and analytical approaches and results across the three GWASs of chronotype. Results A good deal of consistency was observed across studies with 9 genes identified in 2 of the 3 GWASs. Several genes previously unknown to influence chronotype were identified. Conclusions GWAS is an important tool in identifying common variants associated with the complex chronotype phenotype, the findings of which can supplement and guide molecular science. Future directions in model systems and discovery of rare variants are discussed.
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Affiliation(s)
- David A Kalmbach
- Departments of Psychiatry and Neurology, University of Michigan Medical School, Ann Arbor, MI 48109
| | - Logan D Schneider
- Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA 94063
| | - Joseph Cheung
- Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA 94063
| | - Sarah J Bertrand
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins Hospital School of Medicine, Baltimore, MD 21205
| | - Thiruchelvam Kariharan
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109
| | - Allan I Pack
- Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA 19104
| | - Philip R Gehrman
- Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA 19104
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231
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de Vries PS, Sabater-Lleal M, Chasman DI, Trompet S, Ahluwalia TS, Teumer A, Kleber ME, Chen MH, Wang JJ, Attia JR, Marioni RE, Steri M, Weng LC, Pool R, Grossmann V, Brody JA, Venturini C, Tanaka T, Rose LM, Oldmeadow C, Mazur J, Basu S, Frånberg M, Yang Q, Ligthart S, Hottenga JJ, Rumley A, Mulas A, de Craen AJM, Grotevendt A, Taylor KD, Delgado GE, Kifley A, Lopez LM, Berentzen TL, Mangino M, Bandinelli S, Morrison AC, Hamsten A, Tofler G, de Maat MPM, Draisma HHM, Lowe GD, Zoledziewska M, Sattar N, Lackner KJ, Völker U, McKnight B, Huang J, Holliday EG, McEvoy MA, Starr JM, Hysi PG, Hernandez DG, Guan W, Rivadeneira F, McArdle WL, Slagboom PE, Zeller T, Psaty BM, Uitterlinden AG, de Geus EJC, Stott DJ, Binder H, Hofman A, Franco OH, Rotter JI, Ferrucci L, Spector TD, Deary IJ, März W, Greinacher A, Wild PS, Cucca F, Boomsma DI, Watkins H, Tang W, Ridker PM, Jukema JW, Scott RJ, Mitchell P, Hansen T, O'Donnell CJ, Smith NL, Strachan DP, Dehghan A. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study. PLoS One 2017; 12:e0167742. [PMID: 28107422 PMCID: PMC5249120 DOI: 10.1371/journal.pone.0167742] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 11/19/2016] [Indexed: 12/21/2022] Open
Abstract
An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
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Affiliation(s)
- Paul S. de Vries
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Maria Sabater-Lleal
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Tarunveer S. Ahluwalia
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Novo Nordisk Foundation Center For Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marcus E. Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ming-Huei Chen
- Department of Neurology, Boston University School of Medicine, Boston, MA, United States of America
- Framingham Heart Study, Population Sciences Branch, Division of Intramural Research National Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA, United States of America
| | - Jie Jin Wang
- Centre for Vision Research, Department of Ophthalmology, and Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
| | - John R. Attia
- Public Health Stream, Hunter Medical Research Institute, University of Newcastle, Newcastle, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - Riccardo E. Marioni
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Maristella Steri
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy
| | - Lu-Chen Weng
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Rene Pool
- Department of Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
- EMGO+ institute, VU University & VU medical center, Amsterdam, the Netherlands
| | - Vera Grossmann
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Jennifer A. Brody
- Department of Medicine, University of Washington, Seattle WA, United States of America
| | - Cristina Venturini
- Division of Infection and Immunology, UCL, London, United Kingdom
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, United Kingdom
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, United States of America
| | - Lynda M. Rose
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Christopher Oldmeadow
- Public Health Stream, Hunter Medical Research Institute, University of Newcastle, Newcastle, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - Johanna Mazur
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Saonli Basu
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, United States of America
| | - Mattias Frånberg
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden
| | - Qiong Yang
- Framingham Heart Study, Population Sciences Branch, Division of Intramural Research National Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States of America
| | - Symen Ligthart
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - Jouke J. Hottenga
- Department of Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
| | - Ann Rumley
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Antonella Mulas
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy
| | - Anton J. M. de Craen
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Anne Grotevendt
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Kent D. Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor/UCLA Medical Center, Torrance, CA, United States of America
- Division of Genomic Outcomes, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Graciela E. Delgado
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Annette Kifley
- Centre for Vision Research, Department of Ophthalmology, and Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
| | - Lorna M. Lopez
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
- Royal College of Surgeons in Ireland, Department of Psychiatry, Education and Research Centre, Beaumont Hospital, Dublin, Ireland
- University College Dublin, UCD Conway Institute, Centre for Proteome Research, UCD, Belfield, Dublin, Ireland
| | - Tina L. Berentzen
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, United Kingdom
- NIHR Biomedical Research Centre at Guy’s and St. Thomas’ Foundation Trust, London, United Kingdom
| | | | | | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Geoffrey Tofler
- Royal North Shore Hospital, Sydney University, Sydney, Australia
| | | | - Harmen H. M. Draisma
- Department of Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
- Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Gordon D. Lowe
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Magdalena Zoledziewska
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, United Kingdom
| | - Karl J. Lackner
- Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Barbara McKnight
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Jie Huang
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
| | - Elizabeth G. Holliday
- Public Health Stream, Hunter Medical Research Institute, and School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - Mark A. McEvoy
- School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
| | - John M. Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Pirro G. Hysi
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, United Kingdom
| | - Dena G. Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, United States of America
| | - Weihua Guan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, United States of America
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Wendy L. McArdle
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - P. Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Tanja Zeller
- Department of General and Interventional Cardiology, University Heart Centre, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Hamburg, Lübeck, Kiel, Hamburg, Germany
| | - Bruce M. Psaty
- Department of Medicine, Epidemiology, and Health Services, University of Washington, Seattle WA, United States of America
- Group Health Research Institute, Group Health Cooperative, Seattle WA, United States of America
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Eco J. C. de Geus
- Department of Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
- EMGO+ institute, VU University & VU medical center, Amsterdam, the Netherlands
| | - David J. Stott
- Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Harald Binder
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MS, United States of America
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - Jerome I. Rotter
- Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Institute for Translational Genomics and Population Sciences, Torrance, CA, United States of America
- Division of Genomic Outcomes, Departments of Pediatrics & Medicine, Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, United States of America
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, United Kingdom
| | - Ian J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Andreas Greinacher
- Institute for Immunology and Transfusion Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Philipp S. Wild
- Preventive Cardiology and Preventive Medicine, Center for Cardiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site RhineMain, Mainz, Germany
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy
| | - Dorret I. Boomsma
- Department of Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
| | - Hugh Watkins
- Cardiovascular Medicine Dept/Radcliffe Dept of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Weihong Tang
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United States of America
| | - Paul M. Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Jan W. Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Durrer Center for Cardiogenetic Research, Amsterdam, the Netherlands
- Interuniversity Cardiology Institute of the Netherlands, Utrecht, the Netherlands
| | - Rodney J. Scott
- Information based Medicine Program, Hunter Medical Research Institute, University of Newcastle, Newcastle, Australia
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, Australia
| | - Paul Mitchell
- Centre for Vision Research, Department of Ophthalmology, and Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
| | - Torben Hansen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christopher J. O'Donnell
- Framingham Heart Study, Population Sciences Branch, Division of Intramural Research National Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA, United States of America
- Cardiology Division, Massachusetts General Hospital, Boston, MA, United States of America
| | - Nicholas L. Smith
- Group Health Research Institute, Group Health Cooperative, Seattle WA, United States of America
- Department of Epidemiology, University of Washington, Seattle WA, United States of America
- Seattle Epidemiologic Research and Information Center, Department of Veteran Affairs Office of Research and Development, Seattle, WA, United States of America
| | - David P. Strachan
- Population Health Research Institute, St George's, University of London, London, United Kingdom
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- * E-mail:
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232
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Wu B, Pankow JS. Genome-wide association test of multiple continuous traits using imputed SNPs. STATISTICS AND ITS INTERFACE 2017; 10:379-386. [PMID: 28217245 PMCID: PMC5310616 DOI: 10.4310/sii.2017.v10.n3.a2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlated phenotypes that reflect common physiological processes. By jointly analyzing these correlated traits, we can gain more power by aggregating multiple weak effects and shed light on the mechanisms underlying complex human diseases. The majority of existing multi-trait association test methods are based on jointly modeling the multivariate traits conditional on the genotype as covariate, and can readily accommodate the imputed SNPs by using their imputed dosage as a covariate. An alternative class of multi-trait association tests is based on the inverted regression, which models the distribution of genotypes conditional on the covariate and multivariate traits, and has been shown to have competitive performance. To our knowledge, all existing inverted regression approaches have implicitly used the "best-guess" genotypes, which is not efficient and known to lead to dramatic power loss, and there have not been any proposed methods of incorporating imputation uncertainty into inverted regressions. In this work, we propose a general and efficient framework that can account for the imputation uncertainty to further improve the association test power of inverted regression models for imputed SNPs. We demonstrate through extensive numerical studies that the proposed method has competitive performance. We further illustrate its usefulness by application to association test of diabetes-related glycemic traits in the Atherosclerosis Risk in Communities (ARIC) Study.
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Affiliation(s)
- Baolin Wu
- Division of Biostatistics, University of Minnesota
| | - James S. Pankow
- Division of Epidemiology and Community Health School of Public Health, University of Minnesota
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233
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Abstract
Population of ethnic mixtures can be useful in genetic studies. Admixture mapping, or mapping by admixture linkage disequilibrium (MALD), is specially developed for admixed populations and can supplement traditional genome-wide association analyses in the search for genetic variants underlying complex traits. Admixture mapping tests the association between a trait and locus-specific ancestries. The locus-specific ancestries are in linkage disequilibrium (LD), which is generated by an admixture process between genetically distinct ancestral populations. Because of the highly correlated-locus specific ancestries, admixture mapping performs many fewer independent tests across the genome than current genome-wide association analysis. Therefore, admixture mapping can be more powerful because it reduces the penalty due to multiple tests. In this chapter, we introduce the theory behind admixture mapping and explain how to conduct the analysis in practice.
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Richmond RC, Timpson NJ, Felix JF, Palmer T, Gaillard R, McMahon G, Davey Smith G, Jaddoe VW, Lawlor DA. Using Genetic Variation to Explore the Causal Effect of Maternal Pregnancy Adiposity on Future Offspring Adiposity: A Mendelian Randomisation Study. PLoS Med 2017; 14:e1002221. [PMID: 28118352 PMCID: PMC5261553 DOI: 10.1371/journal.pmed.1002221] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 12/14/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND It has been suggested that greater maternal adiposity during pregnancy affects lifelong risk of offspring fatness via intrauterine mechanisms. Our aim was to use Mendelian randomisation (MR) to investigate the causal effect of intrauterine exposure to greater maternal body mass index (BMI) on offspring BMI and fat mass from childhood to early adulthood. METHODS AND FINDINGS We used maternal genetic variants as instrumental variables (IVs) to test the causal effect of maternal BMI in pregnancy on offspring fatness (BMI and dual-energy X-ray absorptiometry [DXA] determined fat mass index [FMI]) in a MR approach. This was investigated, with repeat measurements, from ages 7 to 18 in the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 2,521 to 3,720 for different ages). We then sought to replicate findings with results for BMI at age 6 in Generation R (n = 2,337 for replication sample; n = 6,057 for total pooled sample). In confounder-adjusted multivariable regression in ALSPAC, a 1 standard deviation (SD, equivalent of 3.7 kg/m2) increase in maternal BMI was associated with a 0.25 SD (95% CI 0.21-0.29) increase in offspring BMI at age 7, with similar results at later ages and when FMI was used as the outcome. A weighted genetic risk score was generated from 32 genetic variants robustly associated with BMI (minimum F-statistic = 45 in ALSPAC). The MR results using this genetic risk score as an IV in ALSPAC were close to the null at all ages (e.g., 0.04 SD (95% CI -0.21-0.30) at age 7 and 0.03 SD (95% CI -0.26-0.32) at age 18 per SD increase in maternal BMI), which was similar when a 97 variant generic risk score was used in ALSPAC. When findings from age 7 in ALSPAC were meta-analysed with those from age 6 in Generation R, the pooled confounder-adjusted multivariable regression association was 0.22 SD (95% CI 0.19-0.25) per SD increase in maternal BMI and the pooled MR effect (pooling the 97 variant score results from ALSPAC with the 32 variant score results from Generation R) was 0.05 SD (95%CI -0.11-0.21) per SD increase in maternal BMI (p-value for difference between the two results = 0.05). A number of sensitivity analyses exploring violation of the MR results supported our main findings. However, power was limited for some of the sensitivity tests and further studies with relevant data on maternal, offspring, and paternal genotype are required to obtain more precise (and unbiased) causal estimates. CONCLUSIONS Our findings provide little evidence to support a strong causal intrauterine effect of incrementally greater maternal BMI resulting in greater offspring adiposity.
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Affiliation(s)
- Rebecca C. Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
- * E-mail:
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Janine F. Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Tom Palmer
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Romy Gaillard
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - George McMahon
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Vincent W. Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Debbie A. Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Reid BM, Permuth JB, Chen YA, Teer JK, Monteiro AN, Chen Z, Tyrer J, Berchuck A, Chenevix-Trench G, Doherty JA, Goode EL, Iverson ES, Lawrenson K, Pearce CL, Pharoah PD, Phelan CM, Ramus SJ, Rossing MA, Schildkraut JM, Cheng JQ, Gayther SA, Sellers TA. Integration of Population-Level Genotype Data with Functional Annotation Reveals Over-Representation of Long Noncoding RNAs at Ovarian Cancer Susceptibility Loci. Cancer Epidemiol Biomarkers Prev 2017; 26:116-125. [PMID: 28035019 PMCID: PMC5312656 DOI: 10.1158/1055-9965.epi-16-0341] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Revised: 08/19/2016] [Accepted: 08/30/2016] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified multiple loci associated with epithelial ovarian cancer (EOC) susceptibility, but further progress requires integration of epidemiology and biology to illuminate true risk loci below genome-wide significance levels (P < 5 × 10-8). Most risk SNPs lie within non-protein-encoding regions, and we hypothesize that long noncoding RNA (lncRNA) genes are enriched at EOC risk regions and represent biologically relevant functional targets. METHODS Using imputed GWAS data from about 18,000 invasive EOC cases and 34,000 controls of European ancestry, the GENCODE (v19) lncRNA database was used to annotate SNPs from 13,442 lncRNAs for permutation-based enrichment analysis. Tumor expression quantitative trait locus (eQTL) analysis was performed for sub-genome-wide regions (1 × 10-5 > P > 5 × 10-8) overlapping lncRNAs. RESULTS Of 5,294 EOC-associated SNPs (P < 1.0 × 10-5), 1,464 (28%) mapped within 53 unique lncRNAs and an additional 3,484 (66%) SNPs were correlated (r2 > 0.2) with SNPs within 115 lncRNAs. EOC-associated SNPs comprised 130 independent regions, of which 72 (55%) overlapped with lncRNAs, representing a significant enrichment (P = 5.0 × 10-4) that was more pronounced among a subset of 5,401 lncRNAs with active epigenetic regulation in normal ovarian tissue. EOC-associated lncRNAs and their putative promoters and transcription factors were enriched for biologically relevant pathways and eQTL analysis identified five novel putative risk regions with allele-specific effects on lncRNA gene expression. CONCLUSIONS lncRNAs are significantly enriched at EOC risk regions, suggesting a mechanistic role for lncRNAs in driving predisposition to EOC. IMPACT lncRNAs represent key candidates for integrative epidemiologic and functional studies. Further research on their biologic role in ovarian cancer is indicated. Cancer Epidemiol Biomarkers Prev; 26(1); 116-25. ©2016 AACR.
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Affiliation(s)
- Brett M. Reid
- Moffitt Cancer Center & Research Institute, Tampa, FL
| | | | - Y. Ann Chen
- Moffitt Cancer Center & Research Institute, Tampa, FL
| | - Jamie K. Teer
- Moffitt Cancer Center & Research Institute, Tampa, FL
| | | | - Zhihua Chen
- Moffitt Cancer Center & Research Institute, Tampa, FL
| | | | | | | | | | | | | | | | | | | | | | | | - Susan J. Ramus
- Keck School of Medicine, University of Southern California, Los Angeles, CA
| | | | | | - Jin Q. Cheng
- Moffitt Cancer Center & Research Institute, Tampa, FL
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Shi J, Zhang B, Choi JY, Gao YT, Li H, Lu W, Long J, Kang D, Xiang YB, Wen W, Park SK, Ye X, Noh DY, Zheng Y, Wang Y, Chung S, Lin X, Cai Q, Shu XO. Age at menarche and age at natural menopause in East Asian women: a genome-wide association study. AGE (DORDRECHT, NETHERLANDS) 2016; 38:513-523. [PMID: 27629107 PMCID: PMC5266214 DOI: 10.1007/s11357-016-9939-5] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 07/14/2016] [Indexed: 06/06/2023]
Abstract
Age at menarche (AM) and age at natural menopause (ANM) are complex traits with a high heritability. Abnormal timing of menarche or menopause is associated with a reduced span of fertility and risk for several age-related diseases including breast, endometrial and ovarian cancer, cardiovascular disease, and osteoporosis. To identify novel genetic loci for AM or ANM in East Asian women and to replicate previously identified loci primarily in women of European ancestry by genome-wide association studies (GWASs), we conducted a two-stage GWAS. Stage I aimed to discover promising novel AM and ANM loci using GWAS data of 8073 women from Shanghai, China. The Stage II replication study used the data from another Chinese GWAS (n = 1230 for AM and n = 1458 for ANM), a Korean GWAS (n = 4215 for AM and n = 1739 for ANM), and de novo genotyping of 2877 additional Chinese women. Previous GWAS-identified loci for AM and ANM were also evaluated. We identified two suggestive menarcheal age loci tagged by rs79195475 at 10q21.3 (beta = -0.118 years, P = 3.4 × 10-6) and rs1023935 at 4p15.1 (beta = -0.145 years, P = 4.9 × 10-6) and one menopausal age locus tagged by rs3818134 at 22q12.2 (beta = -0.276 years, P = 8.8 × 10-6). These suggestive loci warrant a further validation in independent populations. Although limited by low statistical power, we replicated 19 of the 98 menarche loci and 5 of the 20 menopause loci previously identified in women of European ancestry in East Asian women, suggesting a shared genetic architecture for these two traits across populations.
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Affiliation(s)
- Jiajun Shi
- Department of Medicine, Vanderbilt Epidemiology Center and Division of Epidemiology, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600, IMPH, Nashville, Tennessee, 37203, USA
| | - Ben Zhang
- Department of Medicine, Vanderbilt Epidemiology Center and Division of Epidemiology, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600, IMPH, Nashville, Tennessee, 37203, USA
| | - Ji-Yeob Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Wei Lu
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jirong Long
- Department of Medicine, Vanderbilt Epidemiology Center and Division of Epidemiology, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600, IMPH, Nashville, Tennessee, 37203, USA
| | - Daehee Kang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Yong-Bing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wanqing Wen
- Department of Medicine, Vanderbilt Epidemiology Center and Division of Epidemiology, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600, IMPH, Nashville, Tennessee, 37203, USA
| | - Sue K Park
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Xingwang Ye
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Dong-Young Noh
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Ying Zheng
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Yiqin Wang
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Seokang Chung
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China
| | - Qiuyin Cai
- Department of Medicine, Vanderbilt Epidemiology Center and Division of Epidemiology, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600, IMPH, Nashville, Tennessee, 37203, USA
| | - Xiao-Ou Shu
- Department of Medicine, Vanderbilt Epidemiology Center and Division of Epidemiology, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600, IMPH, Nashville, Tennessee, 37203, USA.
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237
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The Future is The Past: Methylation QTLs in Schizophrenia. Genes (Basel) 2016; 7:genes7120104. [PMID: 27886132 PMCID: PMC5192480 DOI: 10.3390/genes7120104] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 11/13/2016] [Accepted: 11/16/2016] [Indexed: 12/12/2022] Open
Abstract
Genome-wide association studies (GWAS) have remarkably advanced insight into the genetic basis of schizophrenia (SCZ). Still, most of the functional variance in disease risk remains unexplained. Hence, there is a growing need to map genetic variability-to-genes-to-functions for understanding the pathophysiology of SCZ and the development of better treatments. Genetic variation can regulate various cellular functions including DNA methylation, an epigenetic mark with important roles in transcription and the mediation of environmental influences. Methylation quantitative trait loci (meQTLs) are derived by mapping levels of DNA methylation in genetically different, genotyped individuals and define loci at which DNA methylation is influenced by genetic variation. Recent evidence points to an abundance of meQTLs in brain tissues whose functional contributions to development and mental diseases are still poorly understood. Interestingly, fetal meQTLs reside in regulatory domains affecting methylome reconfiguration during early brain development and are enriched in loci identified by GWAS for SCZ. Moreover, fetal meQTLs are preserved in the adult brain and could trace early epigenomic deregulation during vulnerable periods. Overall, these findings highlight the role of fetal meQTLs in the genetic risk for and in the possible neurodevelopmental origin of SCZ.
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238
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Herskind C, Talbot CJ, Kerns SL, Veldwijk MR, Rosenstein BS, West CML. Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity? Cancer Lett 2016; 382:95-109. [PMID: 26944314 PMCID: PMC5016239 DOI: 10.1016/j.canlet.2016.02.035] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 02/17/2016] [Accepted: 02/19/2016] [Indexed: 02/06/2023]
Abstract
Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review 'omics' approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different 'omics' approaches may be more efficient in identifying critical pathways than pathway analysis based on single 'omics' data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterised by different mechanisms. Thus 'omics' and functional approaches may synergise if they are integrated into radiogenomics 'systems biology' to facilitate the goal of individualised radiotherapy.
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Affiliation(s)
- Carsten Herskind
- Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Germany.
| | | | - Sarah L Kerns
- Department of Radiation Oncology, Mount Sinai School of Medicine, New York, USA; Department of Radiation Oncology, University of Rochester Medical Center, Rochester, USA
| | - Marlon R Veldwijk
- Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Barry S Rosenstein
- Department of Radiation Oncology, Mount Sinai School of Medicine, New York, USA; Department of Radiation Oncology, New York University School of Medicine, USA; Department of Dermatology, Mount Sinai School of Medicine, New York, USA
| | - Catharine M L West
- Institute of Cancer Sciences, University of Manchester, Christie Hospital, Manchester, UK
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239
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Latsuzbaia A, Jaddoe VWV, Hofman A, Franco OH, Felix JF. Associations of genetic variants for adult lipid levels with lipid levels in children. The Generation R Study. J Lipid Res 2016; 57:2185-2192. [PMID: 27777320 DOI: 10.1194/jlr.p066902] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 10/19/2016] [Indexed: 01/14/2023] Open
Abstract
Lipid concentrations are heritable traits. Recently, the number of known genetic loci associated with lipid levels in adults increased from 95 to 157. The effects of these 157 loci have not been tested in children. Considering that lipid levels track from childhood to adulthood, we studied to determine whether these variants already affected lipid concentrations in a large group of 2,645 children with a median age of 6.0 years (95% range 5.7-7.3 years) from the population-based Generation R Study. Twenty-eight SNPs associated with TGs, 39 SNPs associated with total cholesterol (TC), 28 SNPs associated with LDL cholesterol (LDL-C), and 56 SNPs associated with HDL cholesterol (HDL-C) were analyzed individually and combined into genetic risk scores (GRSs). All risk scores were associated with their specific outcomes. The differences in mean absolute lipid and lipoprotein values between the 10% of children with the highest lipid or lipoprotein GRS versus the 10% with the lowest score were 0.28, 0.25, 0.32, and 0.30 mmol/l for TGs, TC, LDL-C, and HDL-C, respectively. In conclusion, we show for the first time that GRSs based on 157 SNPs associated with adult lipid concentrations are associated with lipid levels in children. The genetic background of these phenotypes at least partly overlaps between children and adults.
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Affiliation(s)
- Ardashel Latsuzbaia
- The Generation R Study Group Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Departments of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Albert Hofman
- Departments of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Oscar H Franco
- Departments of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Janine F Felix
- The Generation R Study Group Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands .,Departments of Epidemiology Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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240
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Zhuang WV, Murabito JM, Lunetta KL. Phenotypically Enriched Genotypic Imputation in Genetic Association Tests. Hum Hered 2016; 81:35-45. [PMID: 27576319 DOI: 10.1159/000446986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 05/20/2016] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND In longitudinal epidemiological studies there may be individuals with rich phenotype data who die or are lost to follow-up before providing DNA for genetic studies. Often, the genotypic and phenotypic data of the relatives are available. Two strategies for analyzing the incomplete data are to exclude ungenotyped subjects from analysis (the complete-case method, CC) and to include phenotyped but ungenotyped individuals in analysis by using relatives' genotypes for genotype imputation (GI). In both strategies, the information in the phenotypic data was not used to handle the missing-genotype problem. METHODS We propose a phenotypically enriched genotypic imputation (PEGI) method that uses the EM (expectation-maximization)-based maximum likelihood method to incorporate observed phenotypes into genotype imputation. RESULTS Our simulations with genotypes missing completely at random show that, for a single-nucleotide polymorphism (SNP) with moderate to strong effect on a phenotype, PEGI improves power more than GI without excess type I errors. Using the Framingham Heart Study data set, we compare the ability of the PEGI, GI, and CC to detect the associations between 5 SNPs and age at natural menopause. CONCLUSION The PEGI method may improve power to detect an association over both CC and GI under many circumstances.
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Affiliation(s)
- Wei Vivian Zhuang
- Department of Biostatistics, Boston University School of Public Health, Boston, Mass., USA
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241
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Eaton DAR, Spriggs EL, Park B, Donoghue MJ. Misconceptions on Missing Data in RAD-seq Phylogenetics with a Deep-scale Example from Flowering Plants. Syst Biol 2016; 66:399-412. [DOI: 10.1093/sysbio/syw092] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 10/10/2016] [Indexed: 01/08/2023] Open
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242
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Sailer A, Scholz SW, Nalls MA, Schulte C, Federoff M, Price TR, Lees A, Ross OA, Dickson DW, Mok K, Mencacci NE, Schottlaender L, Chelban V, Ling H, O'Sullivan SS, Wood NW, Traynor BJ, Ferrucci L, Federoff HJ, Mhyre TR, Morris HR, Deuschl G, Quinn N, Widner H, Albanese A, Infante J, Bhatia KP, Poewe W, Oertel W, Höglinger GU, Wüllner U, Goldwurm S, Pellecchia MT, Ferreira J, Tolosa E, Bloem BR, Rascol O, Meissner WG, Hardy JA, Revesz T, Holton JL, Gasser T, Wenning GK, Singleton AB, Houlden H. A genome-wide association study in multiple system atrophy. Neurology 2016; 87:1591-1598. [PMID: 27629089 PMCID: PMC5067544 DOI: 10.1212/wnl.0000000000003221] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2016] [Accepted: 06/15/2016] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE To identify genetic variants that play a role in the pathogenesis of multiple system atrophy (MSA), we undertook a genome-wide association study (GWAS). METHODS We performed a GWAS with >5 million genotyped and imputed single nucleotide polymorphisms (SNPs) in 918 patients with MSA of European ancestry and 3,864 controls. MSA cases were collected from North American and European centers, one third of which were neuropathologically confirmed. RESULTS We found no significant loci after stringent multiple testing correction. A number of regions emerged as potentially interesting for follow-up at p < 1 × 10-6, including SNPs in the genes FBXO47, ELOVL7, EDN1, and MAPT. Contrary to previous reports, we found no association of the genes SNCA and COQ2 with MSA. CONCLUSIONS We present a GWAS in MSA. We have identified several potentially interesting gene loci, including the MAPT locus, whose significance will have to be evaluated in a larger sample set. Common genetic variation in SNCA and COQ2 does not seem to be associated with MSA. In the future, additional samples of well-characterized patients with MSA will need to be collected to perform a larger MSA GWAS, but this initial study forms the basis for these next steps.
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Affiliation(s)
- Anna Sailer
- Authors' affiliations are listed at the end of the article
| | - Sonja W Scholz
- Authors' affiliations are listed at the end of the article.
| | | | | | | | - T Ryan Price
- Authors' affiliations are listed at the end of the article
| | - Andrew Lees
- Authors' affiliations are listed at the end of the article
| | - Owen A Ross
- Authors' affiliations are listed at the end of the article
| | | | - Kin Mok
- Authors' affiliations are listed at the end of the article
| | | | | | | | - Helen Ling
- Authors' affiliations are listed at the end of the article
| | | | | | | | - Luigi Ferrucci
- Authors' affiliations are listed at the end of the article
| | | | | | - Huw R Morris
- Authors' affiliations are listed at the end of the article
| | | | - Niall Quinn
- Authors' affiliations are listed at the end of the article
| | - Hakan Widner
- Authors' affiliations are listed at the end of the article
| | | | - Jon Infante
- Authors' affiliations are listed at the end of the article
| | | | - Werner Poewe
- Authors' affiliations are listed at the end of the article
| | | | | | | | | | | | | | - Eduardo Tolosa
- Authors' affiliations are listed at the end of the article
| | | | - Olivier Rascol
- Authors' affiliations are listed at the end of the article
| | | | - John A Hardy
- Authors' affiliations are listed at the end of the article
| | - Tamas Revesz
- Authors' affiliations are listed at the end of the article
| | | | - Thomas Gasser
- Authors' affiliations are listed at the end of the article
| | | | | | - Henry Houlden
- Authors' affiliations are listed at the end of the article.
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243
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Comparing performance of modern genotype imputation methods in different ethnicities. Sci Rep 2016; 6:34386. [PMID: 27698363 PMCID: PMC5048136 DOI: 10.1038/srep34386] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 09/05/2016] [Indexed: 11/19/2022] Open
Abstract
A variety of modern software packages are available for genotype imputation relying on advanced concepts such as pre-phasing of the target dataset or utilization of admixed reference panels. In this study, we performed a comprehensive evaluation of the accuracy of modern imputation methods on the basis of the publicly available POPRES samples. Good quality genotypes were masked and re-imputed by different imputation frameworks: namely MaCH, IMPUTE2, MaCH-Minimac, SHAPEIT-IMPUTE2 and MaCH-Admix. Results were compared to evaluate the relative merit of pre-phasing and the usage of admixed references. We showed that the pre-phasing framework SHAPEIT-IMPUTE2 can overestimate the certainty of genotype distributions resulting in the lowest percentage of correctly imputed genotypes in our case. MaCH-Minimac performed better than SHAPEIT-IMPUTE2. Pre-phasing always reduced imputation accuracy. IMPUTE2 and MaCH-Admix, both relying on admixed-reference panels, showed comparable results. MaCH showed superior results if well-matched references were available (Nei’s GST ≤ 0.010). For small to medium datasets, frameworks using genetically closest reference panel are recommended if the genetic distance between target and reference data set is small. Our results are valid for small to medium data sets. As shown on a larger data set of population based German samples, the disadvantage of pre-phasing decreases for larger sample sizes.
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An Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies. Sci Rep 2016; 6:34323. [PMID: 27694844 PMCID: PMC5046106 DOI: 10.1038/srep34323] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 09/12/2016] [Indexed: 12/22/2022] Open
Abstract
Currently, the analyses of most genome-wide association studies (GWAS) have been performed on a single phenotype. There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Therefore, using only one single phenotype may lose statistical power to identify the underlying genetic mechanism. There is an increasing need to develop and apply powerful statistical tests to detect association between multiple phenotypes and a genetic variant. In this paper, we develop an Adaptive Fisher’s Combination (AFC) method for joint analysis of multiple phenotypes in association studies. The AFC method combines p-values obtained in standard univariate GWAS by using the optimal number of p-values which is determined by the data. We perform extensive simulations to evaluate the performance of the AFC method and compare the power of our method with the powers of TATES, Tippett’s method, Fisher’s combination test, MANOVA, MultiPhen, and SUMSCORE. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful test. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.
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Amin N, Allebrandt KV, van der Spek A, Müller-Myhsok B, Hek K, Teder-Laving M, Hayward C, Esko T, van Mill JG, Mbarek H, Watson NF, Melville SA, Del Greco FM, Byrne EM, Oole E, Kolcic I, Chen TH, Evans DS, Coresh J, Vogelzangs N, Karjalainen J, Willemsen G, Gharib SA, Zgaga L, Mihailov E, Stone KL, Campbell H, Brouwer RWW, Demirkan A, Isaacs A, Dogas Z, Marciante KD, Campbell S, Borovecki F, Luik AI, Li M, Hottenga JJ, Huffman JE, van den Hout MCGN, Cummings SR, Aulchenko YS, Gehrman PR, Uitterlinden AG, Wichmann HE, Müller-Nurasyid M, Fehrmann RSN, Montgomery GW, Hofman A, Kao WHL, Oostra BA, Wright AF, Vink JM, Wilson JF, Pramstaller PP, Hicks AA, Polasek O, Punjabi NM, Redline S, Psaty BM, Heath AC, Merrow M, Tranah GJ, Gottlieb DJ, Boomsma DI, Martin NG, Rudan I, Tiemeier H, van IJcken WFJ, Penninx BW, Metspalu A, Meitinger T, Franke L, Roenneberg T, van Duijn CM. Genetic variants in RBFOX3 are associated with sleep latency. Eur J Hum Genet 2016; 24:1488-95. [PMID: 27142678 PMCID: PMC5027680 DOI: 10.1038/ejhg.2016.31] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 01/13/2016] [Accepted: 02/01/2016] [Indexed: 01/30/2023] Open
Abstract
Time to fall asleep (sleep latency) is a major determinant of sleep quality. Chronic, long sleep latency is a major characteristic of sleep-onset insomnia and/or delayed sleep phase syndrome. In this study we aimed to discover common polymorphisms that contribute to the genetics of sleep latency. We performed a meta-analysis of genome-wide association studies (GWAS) including 2 572 737 single nucleotide polymorphisms (SNPs) established in seven European cohorts including 4242 individuals. We found a cluster of three highly correlated variants (rs9900428, rs9907432 and rs7211029) in the RNA-binding protein fox-1 homolog 3 gene (RBFOX3) associated with sleep latency (P-values=5.77 × 10(-08), 6.59 × 10(-)(08) and 9.17 × 10(-)(08)). These SNPs were replicated in up to 12 independent populations including 30 377 individuals (P-values=1.5 × 10(-)(02), 7.0 × 10(-)(03) and 2.5 × 10(-)(03); combined meta-analysis P-values=5.5 × 10(-07), 5.4 × 10(-07) and 1.0 × 10(-07)). A functional prediction of RBFOX3 based on co-expression with other genes shows that this gene is predominantly expressed in brain (P-value=1.4 × 10(-316)) and the central nervous system (P-value=7.5 × 10(-)(321)). The predicted function of RBFOX3 based on co-expression analysis with other genes shows that this gene is significantly involved in the release cycle of neurotransmitters including gamma-aminobutyric acid and various monoamines (P-values<2.9 × 10(-11)) that are crucial in triggering the onset of sleep. To conclude, in this first large-scale GWAS of sleep latency we report a novel association of variants in RBFOX3 gene. Further, a functional prediction of RBFOX3 supports the involvement of RBFOX3 with sleep latency.
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Affiliation(s)
- Najaf Amin
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Karla V Allebrandt
- Institute of Medical Psychology, Ludwig-Maximilians-University, Munich, Germany
| | - Ashley van der Spek
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | | | - Karin Hek
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Maris Teder-Laving
- Estonian Genome Center, University of Tartu and Estonian Biocenter, Tartu, Estonia
| | - Caroline Hayward
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu and Estonian Biocenter, Tartu, Estonia
| | - Josine G van Mill
- Department of Psychiatry, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Hamdi Mbarek
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Nathaniel F Watson
- Department of Neurology, University of Washington, Seattle, WA, USA
- University of Washington Medicine Sleep Center, Seattle, WA, USA
| | - Scott A Melville
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Fabiola M Del Greco
- Center for Biomedicine, European Academy of Bolzano, Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Enda M Byrne
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Queensland Institute of Medical Research, Brisbane, QLD, Australia
| | - Edwin Oole
- Center for Biomics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ivana Kolcic
- School of Medicine, University of Split, Split, Croatia
| | - Ting-hsu Chen
- VA Boston Healthcare System, Boston University, Boston, MA, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Josef Coresh
- Departments of Epidemiology, Biostatistics, and Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Nicole Vogelzangs
- Department of Psychiatry, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Juha Karjalainen
- Department of Genetics, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Sina A Gharib
- University of Washington Medicine Sleep Center, Seattle, WA, USA
- Department of Medicine, Division of Pulmonary & Critical Care Medicine, University of Washington, Seattle, WA, USA
| | - Lina Zgaga
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | - Evelin Mihailov
- Estonian Genome Center, University of Tartu and Estonian Biocenter, Tartu, Estonia
| | - Katie L Stone
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
| | - Rutger WW Brouwer
- Center for Biomics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ayse Demirkan
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Aaron Isaacs
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Zoran Dogas
- Department of Neuroscience and Sleep Medicine Centre, University of Split School of Medicine, Split, Croatia
| | - Kristin D Marciante
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Susan Campbell
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | - Fran Borovecki
- Centre for Functional Genomics and Department of Neurology, Faculty of Medicine, University of Zagreb, Zagreb, Croatia
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Man Li
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Jennifer E Huffman
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | | | - Steven R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Yurii S Aulchenko
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Philip R Gehrman
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
- Netherlands Consortium for Healthy Ageing and National Genomics Initiative, Leiden, The Netherlands
| | - Heinz-Erich Wichmann
- Institute of Epidemiology I, Helmholtz Zentrum Munich-German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-University and Klinikum Grosshadern, Munich, Germany
- Institute of Medical Statistics and Epidemiology, Technical University Munich, Munich, Germany
| | - Martina Müller-Nurasyid
- Institute of Epidemiology I, Helmholtz Zentrum Munich-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Rudolf SN Fehrmann
- Department of Genetics, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands
| | | | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Wen Hong Linda Kao
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Ben A Oostra
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Alan F Wright
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
| | - Jacqueline M Vink
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - James F Wilson
- Medical Research Council, Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, Scotland
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
| | - Peter P Pramstaller
- Center for Biomedicine, European Academy of Bolzano, Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Andrew A Hicks
- Center for Biomedicine, European Academy of Bolzano, Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Ozren Polasek
- School of Medicine, University of Split, Split, Croatia
- Centre for Global Health, University of Split School of Medicine, Split, Croatia
| | - Naresh M Punjabi
- Department of Pulmonary Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital and Beth Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
- Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University, St Louis, MO, USA
| | - Martha Merrow
- Institute of Medical Psychology, Ludwig-Maximilians-University, Munich, Germany
| | - Gregory J Tranah
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Daniel J Gottlieb
- Department of Medicine, Brigham and Women's Hospital and Beth Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | | | - Igor Rudan
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland
| | - Henning Tiemeier
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, The Netherlands
| | | | - Brenda W Penninx
- Department of Psychiatry, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu and Estonian Biocenter, Tartu, Estonia
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Techinsche Universität München, München, Germany
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands
| | - Till Roenneberg
- Institute of Medical Psychology, Ludwig-Maximilians-University, Munich, Germany
| | - Cornelia M van Duijn
- Unit of Genetic Epidemiology, Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
- Netherlands Consortium for Healthy Ageing and National Genomics Initiative, Leiden, The Netherlands
- Centre for Medical Systems Biology, Leiden, The Netherlands
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Dennis J, Truong V, Aïssi D, Medina-Rivera A, Blankenberg S, Germain M, Lemire M, Antounians L, Civelek M, Schnabel R, Wells P, Wilson MD, Morange PE, Trégouët DA, Gagnon F. Single nucleotide polymorphisms in an intergenic chromosome 2q region associated with tissue factor pathway inhibitor plasma levels and venous thromboembolism. J Thromb Haemost 2016; 14:1960-1970. [PMID: 27490645 PMCID: PMC6544906 DOI: 10.1111/jth.13431] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 07/01/2016] [Indexed: 02/01/2023]
Abstract
Essentials Tissue factor pathway inhibitor (TFPI) regulates the blood coagulation cascade. We replicated previously reported linkage of TFPI plasma levels to the chromosome 2q region. The putative causal locus, rs62187992, was associated with TFPI plasma levels and thrombosis. rs62187992 was marginally associated with TFPI expression in human aortic endothelial cells. Click to hear Ann Gil's presentation on new insights into thrombin activatable fibrinolysis inhibitor SUMMARY: Background Tissue factor pathway inhibitor (TFPI) regulates fibrin clot formation, and low TFPI plasma levels increase the risk of arterial thromboembolism and venous thromboembolism (VTE). TFPI plasma levels are also heritable, and a previous linkage scan implicated the chromosome 2q region, but no specific genes. Objectives To replicate the finding of the linkage region in an independent sample, and to identify the causal locus. Methods We first performed a linkage analysis of microsatellite markers and TFPI plasma levels in 251 individuals from the F5L Family Study, and replicated the finding of the linkage peak on chromosome 2q (LOD = 3.06). We next defined a follow-up region that included 112 603 single nucleotide polymorphisms (SNPs) under the linkage peak, and meta-analyzed associations between these SNPs and TFPI plasma levels across the F5L Family Study and the Marseille Thrombosis Association (MARTHA) Study, a study of 1033 unrelated VTE patients. SNPs with false discovery rate q-values of < 0.10 were tested for association with TFPI plasma levels in 892 patients with coronary artery disease in the AtheroGene Study. Results and Conclusions One SNP, rs62187992, was associated with TFPI plasma levels in all three samples (β = + 0.14 and P = 4.23 × 10-6 combined; β = + 0.16 and P = 0.02 in the F5L Family Study; β = + 0.13 and P = 6.3 × 10-4 in the MARTHA Study; β = + 0.17 and P = 0.03 in the AtheroGene Study), and contributed to the linkage peak in the F5L Family Study. rs62187992 was also associated with clinical VTE (odds ratio 0.90, P = 0.03) in the INVENT Consortium of > 7000 cases and their controls, and was marginally associated with TFPI expression (β = + 0.19, P = 0.08) in human aortic endothelial cells, a primary site of TFPI synthesis. The biological mechanisms underlying these associations remain to be elucidated.
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Affiliation(s)
- J Dennis
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - V Truong
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - D Aïssi
- Sorbonne Universités, UPMC Univ. Paris 06, Paris, France
- INSERM, UMR_S 1166, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - A Medina-Rivera
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario, Canada
| | - S Blankenberg
- Department of General and Interventional Cardiology, University of Hamburg, Hamburg, Germany
| | - M Germain
- Sorbonne Universités, UPMC Univ. Paris 06, Paris, France
- INSERM, UMR_S 1166, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - M Lemire
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - L Antounians
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - M Civelek
- Center for Public Health Genomics, Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - R Schnabel
- Department of General and Interventional Cardiology, University of Hamburg, Hamburg, Germany
| | - P Wells
- Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - M D Wilson
- Genetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - P-E Morange
- INSERM, UMR_S 1062, Marseille, France
- Inra, UMR_INRA 1260, Marseille, France
- Aix Marseille Université, Marseille, France
| | - D-A Trégouët
- Sorbonne Universités, UPMC Univ. Paris 06, Paris, France
- INSERM, UMR_S 1166, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - F Gagnon
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
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247
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Tao C, Nichols TE, Hua X, Ching CRK, Rolls ET, Thompson PM, Feng J. Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications. Neuroimage 2016; 144:35-57. [PMID: 27666385 DOI: 10.1016/j.neuroimage.2016.08.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Revised: 08/01/2016] [Accepted: 08/14/2016] [Indexed: 11/18/2022] Open
Abstract
We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches.
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Affiliation(s)
- Chenyang Tao
- Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China; Department of Computer Science, Warwick University, Coventry, UK
| | | | - Xue Hua
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Interdepartmental Neuroscience Graduate Program, UCLA School of Medicine, Los Angeles, CA, USA
| | - Edmund T Rolls
- Department of Computer Science, Warwick University, Coventry, UK; Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and Ophthalmology, USC, Los Angeles, CA, USA
| | - Jianfeng Feng
- Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, PR China; Department of Computer Science, Warwick University, Coventry, UK; School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai 200433, PR China.
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248
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Next-generation genotype imputation service and methods. Nat Genet 2016; 48:1284-1287. [PMID: 27571263 DOI: 10.1038/ng.3656] [Citation(s) in RCA: 2347] [Impact Index Per Article: 293.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 08/02/2016] [Indexed: 02/07/2023]
Abstract
Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.
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249
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Franzén O, Ermel R, Cohain A, Akers NK, Di Narzo A, Talukdar HA, Foroughi-Asl H, Giambartolomei C, Fullard JF, Sukhavasi K, Köks S, Gan LM, Giannarelli C, Kovacic JC, Betsholtz C, Losic B, Michoel T, Hao K, Roussos P, Skogsberg J, Ruusalepp A, Schadt EE, Björkegren JLM. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 2016; 353:827-30. [PMID: 27540175 DOI: 10.1126/science.aad6970] [Citation(s) in RCA: 187] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Accepted: 07/22/2016] [Indexed: 12/11/2022]
Abstract
Genome-wide association studies (GWAS) have identified hundreds of cardiometabolic disease (CMD) risk loci. However, they contribute little to genetic variance, and most downstream gene-regulatory mechanisms are unknown. We genotyped and RNA-sequenced vascular and metabolic tissues from 600 coronary artery disease patients in the Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET). Gene expression traits associated with CMD risk single-nucleotide polymorphism (SNPs) identified by GWAS were more extensively found in STARNET than in tissue- and disease-unspecific gene-tissue expression studies, indicating sharing of downstream cis-/trans-gene regulation across tissues and CMDs. In contrast, the regulatory effects of other GWAS risk SNPs were tissue-specific; abdominal fat emerged as an important gene-regulatory site for blood lipids, such as for the low-density lipoprotein cholesterol and coronary artery disease risk gene PCSK9 STARNET provides insights into gene-regulatory mechanisms for CMD risk loci, facilitating their translation into opportunities for diagnosis, therapy, and prevention.
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Affiliation(s)
- Oscar Franzén
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Clinical Gene Networks AB, Jungfrugatan 10, 114 44 Stockholm, Sweden
| | - Raili Ermel
- Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia. Department of Cardiac Surgery, Tartu University Hospital, 1a Ludwig Puusepa Street, 50406 Tartu, Estonia
| | - Ariella Cohain
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Nicholas K Akers
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Antonio Di Narzo
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Husain A Talukdar
- Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles Väg 2, 171 77 Stockholm, Sweden
| | - Hassan Foroughi-Asl
- Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles Väg 2, 171 77 Stockholm, Sweden
| | - Claudia Giambartolomei
- Division of Psychiatric Genomics, Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - John F Fullard
- Division of Psychiatric Genomics, Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Katyayani Sukhavasi
- Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia
| | - Sulev Köks
- Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia
| | - Li-Ming Gan
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Pepparedsleden 1, Mölndal, 431 83, Sweden
| | - Chiara Giannarelli
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Cardiovascular Research Center Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Jason C Kovacic
- Cardiovascular Research Center Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Christer Betsholtz
- AstraZeneca-Karolinska Integrated CardioMetabolic Centre (ICMC), Karolinska Institutet, Novum, Blickagången 6, 141 57 Huddinge, Sweden. Department of Immunology, Genetics and Pathology Dag Hammarskjölds Väg 20, 751 85 Uppsala, Sweden
| | - Bojan Losic
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Tom Michoel
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Old College, South Bridge, Edinburgh EH8 9YL, UK
| | - Ke Hao
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Division of Psychiatric Genomics, Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Department of Psychiatry, J. J. Peters VA Medical Center, Mental Illness Research Education and Clinical Center (MIRECC), 130 West Kingsbridge Road, Bronx, NY 10468, USA
| | - Josefin Skogsberg
- Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles Väg 2, 171 77 Stockholm, Sweden
| | - Arno Ruusalepp
- Clinical Gene Networks AB, Jungfrugatan 10, 114 44 Stockholm, Sweden. Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia. Department of Cardiac Surgery, Tartu University Hospital, 1a Ludwig Puusepa Street, 50406 Tartu, Estonia
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Clinical Gene Networks AB, Jungfrugatan 10, 114 44 Stockholm, Sweden. Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia. Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles Väg 2, 171 77 Stockholm, Sweden.
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Monnereau C, Vogelezang S, Kruithof CJ, Jaddoe VWV, Felix JF. Associations of genetic risk scores based on adult adiposity pathways with childhood growth and adiposity measures. BMC Genet 2016; 17:120. [PMID: 27538985 PMCID: PMC4991119 DOI: 10.1186/s12863-016-0425-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 08/11/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Results from genome-wide association studies (GWAS) identified many loci and biological pathways that influence adult body mass index (BMI). We aimed to identify if biological pathways related to adult BMI also affect infant growth and childhood adiposity measures. METHODS We used data from a population-based prospective cohort study among 3,975 children with a mean age of 6 years. Genetic risk scores were constructed based on the 97 SNPs associated with adult BMI previously identified with GWAS and on 28 BMI related biological pathways based on subsets of these 97 SNPs. Outcomes were infant peak weight velocity, BMI at adiposity peak and age at adiposity peak, and childhood BMI, total fat mass percentage, android/gynoid fat ratio, and preperitoneal fat area. Analyses were performed using linear regression models. RESULTS A higher overall adult BMI risk score was associated with infant BMI at adiposity peak and childhood BMI, total fat mass, android/gynoid fat ratio, and preperitoneal fat area (all p-values < 0.05). Analyses focused on specific biological pathways showed that the membrane proteins genetic risk score was associated with infant peak weight velocity, and the genetic risk scores related to neuronal developmental processes, hypothalamic processes, cyclicAMP, WNT-signaling, membrane proteins, monogenic obesity and/or energy homeostasis, glucose homeostasis, cell cycle, and muscle biology pathways were associated with childhood adiposity measures (all p-values <0.05). None of the pathways were associated with childhood preperitoneal fat area. CONCLUSIONS A genetic risk score based on 97 SNPs related to adult BMI was associated with peak weight velocity during infancy and general and abdominal fat measurements at the age of 6 years. Risk scores based on genetic variants linked to specific biological pathways, including central nervous system and hypothalamic processes, influence body fat development from early life onwards.
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Affiliation(s)
- Claire Monnereau
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands
| | - Suzanne Vogelezang
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands
| | - Claudia J Kruithof
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands. .,Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands. .,Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.
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