Strategies to design and analyze targeted sequencing data: cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study. - Lin, Honghuang; Wang, Min; Brody, Jennifer A; Bis, Joshua C; Dupuis, Josée; Lumley, Thomas; McKnight, Barbara; Rice, Kenneth M; Sitlani, Colleen M; Reid, Jeffrey G; Bressler, Jan; Liu, Xiaoming; et al. (Circ Cardiovasc Genet, 2014)
BACKGROUND: Genome-wide association studies 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. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study aims to follow up genome-wide association study signals and identify novel associations of the allelic spectrum of identified variants with cardiovascular-related traits. METHODS AND RESULTS: The study included 4231 participants from 3 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 ≥1 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 2 approaches: collapsed aggregate statistics and joint analysis of variants using the sequence kernel association test. CONCLUSIONS: We sequenced 77 genomic loci in participants from 3 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 genome-wide association study loci.
Keywords: Adult; Aged; Aged, 80 and over; Aging; Cohort Studies; Female; Genetic Variation; Genome-Wide Association Study; Genomics; Heart Diseases; Humans; Male; Middle Aged; Polymorphism, Single Nucleotide; Research Design; Sequence Analysis, DNA