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Performance of statistical methods on CHARGE targeted sequencing data.

TitlePerformance of statistical methods on CHARGE targeted sequencing data.
Publication TypeJournal Article
Year of Publication2014
AuthorsXing C, Dupuis J
Secondary AuthorsL Cupples A
JournalBMC Genet
Date Published2014 Oct 03
KeywordsAlgorithms, Cardiovascular Diseases, Case-Control Studies, Data Interpretation, Statistical, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide

BACKGROUND: The CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Sequencing Project is a national, collaborative effort from 3 studies: Framingham Heart Study (FHS), Cardiovascular Health Study (CHS), and Atherosclerosis Risk in Communities (ARIC). It uses a case-cohort design, whereby a random sample of study participants is enriched with participants in extremes of traits. Although statistical methods are available to investigate the role of rare variants, few have evaluated their performance in a case-cohort design.

RESULTS: We evaluate several methods, including the sequence kernel association test (SKAT), Score-Seq, and weighted (Madsen and Browning) and unweighted burden tests. Using genotypes from the CHARGE targeted-sequencing project for FHS (n = 1096), we simulate phenotypes in a large population for 11 correlated traits and then sample individuals to mimic the CHARGE Sequencing study design. We evaluate type I error and power for 77 targeted regions.

CONCLUSIONS: We provide some guidelines on the performance of these aggregate-based tests to detect associations with rare variants when applied to case-cohort study designs, using CHARGE targeted sequencing data. Type I error is conservative when we consider variants with minor allele frequency (MAF)

Alternate JournalBMC Genet
PubMed ID25277365
PubMed Central IDPMC4197341
Grant ListRC2 HL102419 / HL / NHLBI NIH HHS / United States
5 RC2 HL102419-02 / HL / NHLBI NIH HHS / United States