title: Using evidence for population stratification bias in combined individual- and family-level genetic association analyses of quantitative traits.
abstract: Genetic association studies are generally performed either by examining differences in the genotype distribution between individuals or by testing for preferential allele transmission within families. In the absence of population stratification bias (PSB), integrated analyses of individual and family data can increase power to identify susceptibility loci [Abecasis et al., 2000. Am. J. Hum. Genet. 66:279-292; Chen and Lin, 2008. Genet. Epidemiol. 32:520-527; Epstein et al., 2005. Am. J. Hum. Genet. 76:592-608]. In existing methods, the presence of PSB is initially assessed by comparing results from between-individual and within-family analyses, and then combined analyses are performed only if no significant PSB is detected. However, this strategy requires specification of an arbitrary testing level alpha(PSB), typically 5%, to declare PSB significance. As a novel alternative, we propose to directly use the PSB evidence in weights that combine results from between-individual and within-family analyses. The weighted approach generalizes previous methods by using a continuous weighting function that depends only on the observed P-value instead of a binary weight that depends on alpha(PSB). Using simulations, we demonstrate that for quantitative trait analysis, the weighted approach provides a good compromise between type I error control and power to detect association in studies with few genotyped markers and limited information regarding population structure.
authors.affiliation: Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
authors.forename: Lucia
authors.initials: L
authors.lastname: Mirea
authors.forename: Lei
authors.initials: L
authors.lastname: Sun
authors.forename: James E
authors.initials: JE
authors.lastname: Stafford
authors.forename: Shelley B
authors.initials: SB
authors.lastname: Bull
mesh_terms: D000483:Alleles; D003198:Computer Simulation; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D005828:Genetics, Population / Q000379:methods*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D009679:Nuclear Family; D010641:Phenotype; D019655:Quantitative Trait, Heritable
chemical_list: D005819:Genetic Markers