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      Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium

      research-article
      1 , 2 , 3 , 4 , 3 , 2 , 5 , 3 , 6 , 7 , 7 , 8 , 9 , 10 , 11 , 1 , 12 , 13 , 12 , 14 , 15 , 2 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 4 , 35 , 32 , 13 , 8 , 17 , 36 , 37 , 16 , 38 , 18 , 19 , 39 , 40 , 41 , 42 , 8 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 3 , 53 , 54 , 3 , 55 , 52 , 56 , 3 , 57 , 58 , 59 , 60 , 8 , 42 , 6 , 61 , 62 , 63 , 13 , 64 , 65 , 16 , 21 , 66 , 67 , 68 , 20 , 69 , 55 , 70 , 26 , 68 , 68 , 26 , 71 , The Bone Mineral Density in Childhood Study (BMDCS) Group, 72 , 20 , 23 , 69 , 73 , 26 , 74 , 75 , 21 , 22 , 25 , 76 , 69 , 77 , 6 , 7 , 78 , 16 , 16 , 18 , 19 , 79 , 12 , 80 , 81 , 82 , 83 , 84 , 13 , 85 , 86 , 1 , 2 , 87 , 5 , 88 , 8 , 43 , * , 4 , 89 , * , 3 , *
      PLoS Genetics
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          Abstract

          Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHR adjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHR adjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus ( TCF7L2/HABP2) for WHR adjBMI and eight previously established loci at P < 5×10 −8: seven for BMI, and one for WHR adjBMI in African ancestry individuals. An additional novel locus ( SPRYD7/DLEU2) was identified for WHR adjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI ( INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHR adjBMI ( SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (<5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHR adjBMI loci that were locus-wide significant ( P < 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHR adjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHR adjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations.

          Author summary

          Genome-wide association studies (GWAS) have identified >300 genetic regions that influence body size and shape as measured by body mass index (BMI) and waist-to-hip ratio (WHR), respectively, but few have been identified in populations of African ancestry. We conducted large scale high coverage GWAS and replication of these traits in 52,895 and 23,095 individuals of African ancestry, respectively, followed by additional replication in European populations. We identified 10 genome-wide significant loci in all individuals, and an additional seven loci by analyzing men and women separately. We combined African and European ancestry GWAS and were able to narrow down 43 out of 74 African ancestry associated genetic regions to contain small number of putative causal variants. Our results highlight the improvement of applying high density genome coverage and combining multiple ancestries in the identification and refinement of location of genetic regions associated with adiposity traits.

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          Most cited references66

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          Is Open Access

          ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

          High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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            A method and server for predicting damaging missense mutations

            To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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              GCTA: a tool for genome-wide complex trait analysis.

              For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                21 April 2017
                April 2017
                : 13
                : 4
                : e1006719
                Affiliations
                [1 ]Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, United States of America
                [2 ]Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, United States of America
                [3 ]Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States of America
                [4 ]The Charles Bronfman Institute for Personalized Medicine, Icachn School of Medicine at Mount Sinai, New York, NY, United States of America
                [5 ]Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States of America
                [6 ]Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
                [7 ]Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis MO, United States of America
                [8 ]Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
                [9 ]Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States of America
                [10 ]Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, United States of America
                [11 ]Harvard Medical School, Boston, MA, United States of America
                [12 ]Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
                [13 ]Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, United States of America
                [14 ]Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States of America
                [15 ]Data Tecnica International, Glen Echo, MD, United States of America
                [16 ]National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
                [17 ]Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, United States of America
                [18 ]Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, United States of America
                [19 ]Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
                [20 ]Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
                [21 ]Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States of America
                [22 ]Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, United States of America
                [23 ]Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
                [24 ]Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States of America
                [25 ]Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, MI, United States of America
                [26 ]Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States of America
                [27 ]Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, United States of America
                [28 ]Division of Preventive Medicine, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, United States of America
                [29 ]Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, United States of America
                [30 ]Department of Population Science, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States of America
                [31 ]Cardiovascular Health Research Unit, Departments of Medicine and Biostatistics, University of Washington, Seattle, WA, United States of America
                [32 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, United States of America
                [33 ]Beckman Research Institute of the City of Hope, Duarte, CA, United States of America
                [34 ]International Epidemiology Institute, Rockville, MD, United States of America
                [35 ]Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
                [36 ]Center for Human Genetics, University of Texas Health Science Center at Houston, Houston, TX, United States of America
                [37 ]Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States of America
                [38 ]SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
                [39 ]Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, United States of America
                [40 ]Sylvester Comprehensive Cancer Center, University of Miami Leonard Miller School of Medicine, Miami, FL, United States of America
                [41 ]Department of Public Health Sciences, University of Miami Leonard Miller School of Medicine, Miami, FL, United States of America
                [42 ]Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, United States of America
                [43 ]Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, United States of America
                [44 ]Cancer Prevention Institute of California, Fremont, CA, United States of America
                [45 ]Department of Health Research and Policy (Epidemiology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, United States of America
                [46 ]Division of Urology, Department of Surgery, The University of Arizona, Tucson, AZ, United States of America
                [47 ]Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, United States of America
                [48 ]Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States of America
                [49 ]Cardiovascular Health Research Unit, Department of Biostatistics, University of Washington, Seattle, WA, United States of America
                [50 ]Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, United States of America
                [51 ]Department of Preventive Medicine, Stony Brook University, Stony Brook, NY, United States of America
                [52 ]Department of Medicine, University of Ibadan, Ibadan, Nigeria
                [53 ]Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States of America
                [54 ]Department of Pathology and Norris Comprehensive Cancer Center, University of Southern California Keck School of Medicine, Los Angeles, CA, United States of America
                [55 ]Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, United States of America
                [56 ]Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, United States of America
                [57 ]The New York Academy of Medicine, New York, NY, United States of America
                [58 ]Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
                [59 ]Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States of America
                [60 ]Epidemiology Research Program, American Cancer Society, Atlanta, GA, United States of America
                [61 ]Department of Epidemiology, Bloomberg School of Public Health, Baltimore, MD, United States of America
                [62 ]Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States of America
                [63 ]Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, United States of America
                [64 ]Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, United States of America
                [65 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America
                [66 ]Laboratory of Human Carcinogenesis, National Cancer Institute, Bethesda, MD, United States of America
                [67 ]Department of Medicine, University of Vermont College of Medicine, Burlington, VT, United States of America
                [68 ]Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, United States of America
                [69 ]Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
                [70 ]Department of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
                [71 ]School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States of America
                [72 ]School of Public Health, University of Kentucky, Lexington, KY, United States of America
                [73 ]Division of Endocrinology, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
                [74 ]Center for Clinical Cancer Genetics, Department of Medicine and Human Genetics, University of Chicago, Chicago, IL, United States of America
                [75 ]Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States of America
                [76 ]Department of Internal Medicine, Henry Ford Health System, Detroit, MI, United States of America
                [77 ]Division of Gastroenterology, Hepatology and Nutrition, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States of America
                [78 ]Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc, United States of America
                [79 ]Departments of Genetics and Pediatrics, Harvard Medical School, Boston, MA, United States of America
                [80 ]Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
                [81 ]Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
                [82 ]Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
                [83 ]Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, United States of America
                [84 ]Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of America
                [85 ]Division of Genomic Outcomes, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Los Angeles, CA, United States of America
                [86 ]Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, United States of America
                [87 ]Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, United States of America
                [88 ]NHLBI Framingham Heart Study, Framingham, MA, United States of America
                [89 ]The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, United States of America
                The University of North Carolina at Chapel Hill, UNITED STATES
                Author notes

                I have read the journal's policy and the authors of this manuscript have the following competing interests: BMP serves on the DSMB of a clinical trial funded by the manufacturer and on the Steering Committee for the Yale Open Data Access Project funded by Johnson & Johnson. IB works in Regeneron Pharmaceuticals, Inc. MAN's participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA. As a possible conflict of interest, MAN also consults for Illumina Inc, the Michael J. Fox Foundation and University of California Healthcare.

                • Conceptualization: CAH KEN MCYN RJFL.

                • Formal analysis: AC AEJ AML AS BEC BOT BP CTL DH DV DZ EBW GC GL HO JAB JAS JPB JDF JL JY KR KY LAL LD LRY MCYN MFF MGr MKW MAN MRI PM QD RR SMT SV TMB WMC WZha XG YHHH YLi YLu YS.

                • Project administration: AA ABZ AOl BAR BIF BM BMP BN BOT BSZ CAH CBA CDH CNR DCR DH DKA DMB DRW DSS DVC DWB EK EMJ EPB EVB HH IBB JC JGW JH JIR JLS JNH JSW KEN KLW LAC LB MCYN MF MKE MMS MFP MS OIO PJG RGZ RJFL RK RSC SAI SFAG SIB SJC SLRK SRP SSS TBH VLS WJB WZhe XZ.

                • Resources: AGF AOg BIF BMP BN BS BSZ DRW HH JAS JDF JIR KLN MAA MC MGa OO SA SFAG SLRK TOO UN WZha WZhe YDIC.

                • Supervision: CAH KEN MCYN RJFL.

                • Writing – original draft: AEJ CTL LAC LRY MCYN MFF MGr MKW YLu.

                • Writing – review & editing: AEJ BEC BMP CAH CTL DCR DKA DMB JPB KEN KR KY LAC LRY MC MCYN MFF MGr MKW MAN MRI RJFL WMC XG YLu.

                ‡ These authors jointly supervised this work.

                ¶ Full membership of the Bone Mineral Density in Childhood Study (BMDCS) Group is provided in S2 Text.

                Author information
                http://orcid.org/0000-0002-4133-2007
                http://orcid.org/0000-0001-7117-1075
                http://orcid.org/0000-0002-2420-162X
                http://orcid.org/0000-0001-8509-148X
                http://orcid.org/0000-0003-1424-0673
                http://orcid.org/0000-0002-7919-8528
                http://orcid.org/0000-0002-2643-2333
                http://orcid.org/0000-0002-3575-5468
                http://orcid.org/0000-0003-3403-8763
                http://orcid.org/0000-0002-7692-6518
                http://orcid.org/0000-0002-2946-8956
                http://orcid.org/0000-0003-3400-6614
                http://orcid.org/0000-0001-5339-9535
                http://orcid.org/0000-0003-3190-3467
                http://orcid.org/0000-0003-0259-4407
                http://orcid.org/0000-0002-8278-6507
                http://orcid.org/0000-0002-7164-1601
                http://orcid.org/0000-0002-3105-3231
                http://orcid.org/0000-0003-4731-8158
                http://orcid.org/0000-0002-4032-5745
                http://orcid.org/0000-0003-2025-5302
                http://orcid.org/0000-0001-5759-053X
                http://orcid.org/0000-0003-1644-9040
                http://orcid.org/0000-0002-6164-7348
                http://orcid.org/0000-0002-9275-4189
                http://orcid.org/0000-0002-9142-5172
                http://orcid.org/0000-0001-7191-1723
                Article
                PGENETICS-D-16-02706
                10.1371/journal.pgen.1006719
                5419579
                28430825
                f66ab64d-b390-4a35-924c-d485ff7e0364

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 8 December 2016
                : 29 March 2017
                Page count
                Figures: 2, Tables: 2, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: DK066358
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: DK087914
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: DK053591
                Award Recipient :
                AABC-MEC: The MEC and genotyping of samples for the GWAS of breast and prostate cancer was supported by National Institutes of Health grants CA63464, CA54281, CA164973, CA1326792, CA148085, HG004726 and a Department of Defense Breast Cancer Research Program Era of Hope Scholar Award to CAH (W81XWH-08-1-0383) and the Norris Foundation. AABC-CARE: CARE was supported by National Institute for Child Health and Development contract NO1-HD-3-3175. AABC-WCHS: WCHS is supported by a U.S. Army Medical Research and Material Command (USAMRMC) grant DAMD-17-01-0-0334, National Institutes of Health grant CA100598, and the Breast Cancer Research Foundation. AABC-SFBCS: SFBCS was supported by National Institutes of Health grant CA77305 and United States Army Medical Research Program grant DAMD17-96-6071. AABC-NC-BCFR: The Breast Cancer Family Registry (BCFR) was supported by the National Cancer Institute, National Institutes of Health under RFA CA-06-503 and through cooperative agreements with members of the Breast Cancer Family Registry and Principal Investigators, including the Cancer Prevention Institute of California (U01 CA69417). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the BCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government or the BCFR. AABC-CBCS: CBCS is supported by National Institutes of Health Specialized Program of Research Excellence in Breast Cancer grant CA58223 and Center for Environmental Health and Susceptibility, National Institute of Environmental Health Sciences, National Institutes of Health, grant ES10126. AABC-PLCO: Genotyping of the PLCO samples was funded by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, the National Institutes of Health. The authors thank Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention, National Cancer Institute, the screening center investigators and staff of the PLCO Cancer Screening Trial, Mr. Thomas Riley and staff at Information Management Services, Inc., and Ms. Barbara O’Brien and staff at Westat, Inc. for their contributions to the PLCO Cancer Screening Trial. AABC-NBHS: NBHS is support by National Institutes of Health grant CA100374. NBHS sample preparation was conducted at the Biospecimen Core Lab that is supported in part by the Vanderbilt-Ingram Cancer Center (CA68485). AABC-WFBC: WFBC is supported by National Institutes of Health grant CA73629. AAPC-CPS-II: CPSII is supported by the American Cancer Society. AAPC-KCPCS: KCPCS was supported by National Institutes of Health grants CA056678, CA082664 and CA092579, with additional support from the Fred Hutchinson Cancer Research Center. AAPC-LAAPC: LAAPC was funded by grant 99-00524V-10258 from the Cancer Research Fund, under Interagency Agreement #97-12013 (University of California contract #98-00924V) with the Department of Health Services Cancer Research Program. AAPC-DCPC: DCPC was supported by National Institutes of Health grant S06GM08016 and Department of Defense grants DAMD W81XWH-07-1-0203 and DAMD W81XWH-06-1-0066. AAPC-MEC: The MEC and genotyping of samples for the GWAS of prostate cancer was supported by National Institutes of Health grants CA63464, CA54281, CA164973, CA1326792, CA148085 and HG004726. AAPC-SELECT trial: The SELECT trial is supported by National Cancer Institute grants U10CA37429 and 5UM1CA182883. AAPC-PLCO: See AABC-PLCO. AAPC-GECAP: GECAP was supported by National Institutes of Health grant ES011126. AAPC-SCCS: SCCS is funded by National Institutes of Health grant CA092447. SCCS sample preparation was conducted at the Biospecimen Core Lab that is supported in part by the Vanderbilt-Ingram Cancer Center (CA68485). AAPC-MDA: MDA was support by grants CA68578, ES007784, DAMD W81XWH-07-1-0645, and CA140388. AAPC-CaP Genes: CaP Genes was supported by CA88164 and CA127298. ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. BioMe: NIH/NHGRI U01HG007417. BMDCS: We thank the investigators Heidi Kalkwarf, Joan Lappe, Sharon Oberfield, Vicente Gilsanz, John Shepherd and Andrea Kelly for their contribution. BMDCS is supported by NIH: R01 HD58886, HD076321, DK094723-01, DK102659-01; NICHD N01-HD-1-3228, -3329, -3330, -3331, -3332, -3333; CTSA program Grant 8 UL1 TR000077. CARDIA: The Coronary Artery Risk Development in Young Adults (CARDIA) study is supported by the NHLBI (contracts HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C, and HHSN268200900041C); the Intramural Research Program of the National Institute on Aging (NIA); and an intra-agency agreement between the NIA and the NHLBI (grant AG0005). CFS: The Cleveland Family Study (CFS) is supported by the National Institutes of Health: HL046380, HL113338, RR000080. Dr. Cade has been supported by HL007901. CHOP: This research was financially supported by an Institute Development Award from the Children’s Hospital of Philadelphia, a Research Development Award from the Cotswold Foundation, NIH grant R01 HD056465 and the Daniel B. Burke Endowed Chair for Diabetes Research (SFAG). CHS: This CHS research was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants U01HL080295, R01HL085251, R01HL087652, R01HL105756, R01HL103612, R01HL120393 and HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. Participants provided informed consent for participation in DNA studies. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. FamHS: NIH/NIDDK R01DK089256, and NIH/NHLBI R01HL117078. GeneSTAR: NIH/NHLBI U01 HL72518; NIH/NHLBI HL087698; NIH/NHLBI 58625-01A1; NIH/NHLBI HL071025-01A1; NIH/NINR NR0224103; NIH/NCRR M01-RR000052. HANDLS: Healthy Aging in Neighborhoods of Diversity across the Life Span Study (HANDLS) was supported by the Intramural Research Program of the NIH, National Institute on Aging and the National Center on Minority Health and Health Disparities (project # Z01-AG000513 and human subjects protocol # 2009-149). HealthABC: Health, Aging, and Body Composition Study (Health ABC Study) was supported by NIA contracts N01AG62101, N01AG62103, and N01AG62106. The genome-wide association study was funded by NIA grant 1R01AG032098-01A1 to Wake Forest University Health Sciences and genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging. HRS: HRS is supported by the National Institute on Aging (NIA U01AG009740). The genotyping was funded separately by the National Institute on Aging (RC2 AG036495, RC4 AG039029), and the analysis was funded in part by R03 AG046389. Our genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation of the data were performed by the Genetics Coordinating Center at the University of Washington. HUFS: The HUFS (Howard University Family Study) was supported by grants S06GM008016-380111 to AA and S06GM008016-320107 to CR, both from the NIGMS/MBRS/SCORE Program. Participant enrollment for the HUFS was carried out at the Howard University General Clinical Research Center (GCRC), which was supported by grant 2M01RR010284 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). This research was supported in part by the NIH Intramural Research Program in the Center for Research on Genomics and Global Health (CRGGH) with support from the National Human Genome Research Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the Center for Information Technology, and the Office of the Director at the National Institutes of Health (Z01HG200362). HyperGEN: The HyperGEN network is funded by cooperative agreements (U10) with NHLBI: HL54471, HL54472, HL54473, HL54495, HL54496, HL54497, HL54509, HL54515, and R01 HL55673 from the National Heart, Lung, and Blood Institute. The study involves: University of Utah: (Network Coordinating Center, Field Center, and Molecular Genetics Lab); Univ. of Alabama at Birmingham: (Field Center and Echo Coordinating and Analysis Center); Medical College of Wisconsin: (Echo Genotyping Lab); Boston University: (Field Center); University of Minnesota: (Field Center and Biochemistry Lab); University of North Carolina: (Field Center); Washington University: (Data Coordinating Center); Weil Cornell Medical College: (Echo Reading Center); National Heart, Lung, & Blood Institute. For a complete list of HyperGEN Investigators: http://www.biostat.wustl.edu/hypergen/Acknowledge.html. JHS: The Jackson Heart Study is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. JGW is supported by U54GM115428 from the National Institute of General Medical Sciences. Maywood: NIH: R37-HL045508, R01-HL074166, R01-HL086718, R01-HG003054; analysis also funded through R01-DK075787. MESA: The Multi-Ethnic Study of Atherosclerosis study (MESA) was supported by the Multi-Ethnic Study of Atherosclerosis (MESA) contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169 and by grants UL1-TR-000040 and UL1-RR-025005 from NCRR. Funding for MESA SHARe genotyping was provided by NHLBI Contract N02-HL-6-4278. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. Nigeria: NIH: R37-HL045508, R01-HL053353, R01-DK075787 and U01-HL054512; analysis also funded through R01-DK075787. ROOT: NIH: R01-CA142996, P50-CA125183, R01-CA89085, and U01-CA161032. American Cancer Society: MRSG-13-063-01-TBG and CRP-10-119-01-CCE. SAPPHIRE: NIH/NHLBI R01HL118267; NIH/NIAID R01AI079139; NIH/NIDDK R01DK064695 and R01 DK113003; American Asthma Foundation. SIGNET-REGARDS: The Sea Islands Genetics Network (SIGNET) was supported by R01 DK084350 (MM Sale). The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at http://www.regardsstudy.org. This study (REGARDS) was supported by a cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke (NINDS). WFSM: Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSC268200782096C. This work was supported by National Institutes of Health grants R01 DK087914, R01 DK066358, R01 DK053591, R01 HL56266, R01 DK070941 and by the Wake Forest School of Medicine grant M01 RR07122 and Venture Fund 20543. WHI: Funding support for the “Epidemiology of putative genetic variants: The Women’s Health Initiative” study is provided through the NHGRI PAGE program (U01HG004790 and its NHGRI ARRA supplement). The WHI program is funded by the National Heart, Lung, and Blood Institute; NIH; and U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221. The authors thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at: http://www.whiscience.org/publications/ WHI_investigators_shortlist.pdf. CAH: R01 DK101855-01. CL: R01 DK089256. KEN: R01 DK089256, R01 DK101855-01, 15GRNT25880008. KLY: KL2TR001109. LAC: R01 DK089256. RJFL: R01 DK101855-01. YLi: R01HG006292 and R01HL129132. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                2017-05-05
                All relevant data are within the paper and its Supporting Information files. Summary association statistics from meta-analyses are available from dbGAP at accession number phs000930.

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