Performance of statistical methods on CHARGE targeted sequencing data

• Published in: BMC genetics Vol. 15; no. 1; p. 104
• Main Authors: Xing, Chuanhua, Dupuis, Josée, Cupples, L Adrienne
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.10.2014; BioMed Central
• Subjects: Algorithms; Analysis; Bias; Cardiovascular Diseases - genetics; Case-Control Studies; Data Interpretation, Statistical; Data processing; Disease; Epidemiology; Genetic Predisposition to Disease; Genome-Wide Association Study - methods; Health risk assessment; Heart; Humans; Methods; Polymorphism, Single Nucleotide; Studies
• ISSN: 1471-2156; 1471-2156
• DOI: 10.1186/s12863-014-0104-9

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. 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. 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) < 1%. Power is generally low, although it is relatively larger for Score-Seq. Greater numbers of causal variants and a greater proportion of variance improve the power, but it tends to be lower in the presence of bi-directionality of effects of causal genotypes, especially for Score-Seq.