Abstract
Family based association studies are employed less often than case-control designs in the search for disease-predisposing genes. The optimal statistical genetic approach for complex pedigrees is unclear when evaluating both common and rare variants. We examined the empirical power and type I error rates of 2 common approaches, the measured genotype approach and family-based association testing, through simulations from a set of multigenerational pedigrees. Overall, these results suggest that much larger sample sizes will be required for family-based studies and that power was better using MGA compared to FBAT. Taking into account computational time and potential bias, a 2-step strategy is recommended with FBAT followed by MGA.
Highlights
Phenotypic variation in complex traits is conferred through both common and rare variants
When using the Van Steen top 10 screening approach (FBAT-VS) the MAP4 single-nucleotide polymorphism (SNP) was detectable, but not at the rate conferred by measured genotype approach (MGA)
Using a cohort of extended families, we evaluated the performance of 2 family based methods (MGA and familybased association testing (FBAT)) to identify causal variants of varying allele frequency and effect size
Summary
Phenotypic variation in complex traits is conferred through both common and rare variants. It has been suggested that common variation plays a role at the level of the population, whereas rare variation has stronger effects at the levels of the clan (extended family) and the nuclear family [1]. A large number of genome-wide association studies (GWAS) have focused on population-level variation. By using predominantly case-control designs with single-variant analyses, these studies have identified common variants associated with common diseases and related phenotypes. In the past 10 years, studies of extended families have been much more limited, even though individuals sharing recent ancestors share regions of the genome other than disease-causing variants and may provide a better proxy for the total mutation load [1].
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