Strategies to design and analyze targeted sequencing data: cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study.
Strategies to design and analyze targeted sequencing data: cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study.
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DOI：
10.1161/circgenetics.113.000350
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
Boerwinkle E
中科院分区：
文献类型：
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作者：
Lin H;Wang M;Brody JA;Bis JC;Dupuis J;Lumley T;McKnight B;Rice KM;Sitlani CM;Reid JG;Bressler J;Liu X;Davis BC;Johnson AD;O'Donnell CJ;Kovar CL;Dinh H;Wu Y;Newsham I;Chen H;Broka A;DeStefano AL;Gupta M;Lunetta KL;Liu CT;White CC;Xing C;Zhou Y;Benjamin EJ;Schnabel RB;Heckbert SR;Psaty BM;Muzny DM;Cupples LA;Morrison AC;Boerwinkle E
Genome-wide association studies (GWAS) have identified thousands of genetic variants that influence a variety of diseases and health-related quantitative traits. However, the causal variants underlying the majority of genetic associations remain unknown. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study aims to follow up GWAS signals and identify novel associations of the allelic spectrum of identified variants with cardiovascular related traits. The study included 4,231 participants from three CHARGE cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, and the Framingham Heart Study. We used a case-cohort design in which we selected both a random sample of participants and participants with extreme phenotypes for each of 14 traits. We sequenced and analyzed 77 genomic loci, which had previously been associated with one or more of 14 phenotypes. A total of 52,736 variants were characterized by sequencing and passed our stringent quality control criteria. For common variants (minor allele frequency ≥1%), we performed unweighted regression analyses to obtain p-values for associations and weighted regression analyses to obtain effect estimates that accounted for the sampling design. For rare variants, we applied two approaches: collapsed aggregate statistics and joint analysis of variants using the Sequence Kernel Association Test. We sequenced 77 genomic loci in participants from three cohorts. We established a set of filters to identify high-quality variants, and implemented statistical and bioinformatics strategies to analyze the sequence data, and identify potentially functional variants within GWAS loci.
