Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Jun;7(3):335-43.
doi: 10.1161/CIRCGENETICS.113.000350.

Strategies to design and analyze targeted sequencing data: cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study

Honghuang Lin, Min Wang, Jennifer A Brody, Joshua C Bis, Josée Dupuis, Thomas Lumley, Barbara McKnight, Kenneth M Rice, Colleen M Sitlani, Jeffrey G Reid, Jan Bressler, Xiaoming Liu, Brian C Davis, Andrew D Johnson, Christopher J O'Donnell, Christie L Kovar, Huyen Dinh, Yuanqing Wu, Irene Newsham, Han Chen, Andi Broka, Anita L DeStefano, Mayetri Gupta, Kathryn L Lunetta, Ching-Ti Liu, Charles C White, Chuanhua Xing, Yanhua Zhou, Emelia J Benjamin, Renate B Schnabel, Susan R Heckbert, Bruce M Psaty, Donna M Muzny, L Adrienne Cupples, Alanna C Morrison, Eric Boerwinkle

Strategies to design and analyze targeted sequencing data: cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study

Honghuang Lin et al. Circ Cardiovasc Genet. 2014 Jun.

Abstract

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: epidemiology; genetics; sampling studies.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: B.M.P. serves on the DSMB of a clinical trial of a device funded by Zoll LifeCor and on the Steering Committee of the Yale Open Data Access Project funded by Medtronic. Other authors declare no commercial conflicts of interest.

Figures

Figure 1
Figure 1
Distribution of Targeted Sequence Coverage in 4,646 samples using SOLiD multiplexed capture sequencing. Each dot represents one sample. The x-axis represents the total depth of each sample (in terms of raw aligned bases), whereas the y-axis represents the proportion of targeted regions with more than 20× coverage. ARIC = Atherosclerosis Risk in Communities, CHS = Cardiovascular Health Study, FHS = Framingham Heart Study, MGH = Massachusetts General Hospital.
Figure 2
Figure 2
Minor allele frequency distributions for variants passing QC (all three cohorts combined). (A) Distribution of functional classes in common/rare variants (B) Minor allele frequency spectrum (C) Frequencies of minor allele count <= 10
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009;106:9362–9367. - PMC - PubMed
    1. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–1073. - PMC - PubMed
    1. Johansen CT, Wang J, Lanktree MB, Cao H, McIntyre AD, Ban MR, et al. Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nat Genet. 2010;42:684–687. - PMC - PubMed
    1. Johnson AD, O'Donnell CJ. An open access database of genome-wide association results. BMC Med Genet. 2009;10:6. - PMC - PubMed
    1. Psaty BM, O'Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2:73–80. - PMC - PubMed

Publication types

Grants and funding