Power of family-based association designs to detect rare variants in large pedigrees using imputed genotypes.
Recently, the "Common Disease-Multiple Rare Variants" hypothesis has received much attention, especially with current availability of next-generation sequencing. Family-based designs are well suited for discovery of rare variants, with large and carefully selected pedigrees enriching for multiple copies of such variants. However, sequencing a large number of samples is still prohibitive. Here, we evaluate a cost-effective strategy (pseudosequencing) to detect association with rare variants in large pedigrees. This strategy consists of sequencing a small subset of subjects, genotyping the remaining sampled subjects on a set of sparse markers, and imputing the untyped markers in the remaining subjects conditional on the sequenced subjects and pedigree information. We used a recent pedigree imputation method (GIGI), which is able to efficiently handle large pedigrees and accurately impute rare variants. We used burden and kernel association tests, famWS and famSKAT, which both account for family relationships and heterogeneity of allelic effect for famSKAT only. We simulated pedigree sequence data and compared the power of association tests for pseudosequence data, a subset of sequence data used for imputation, and all subjects sequenced. We also compared, within the pseudosequence data, the power of association test using best-guess genotypes and allelic dosages. Our results show that the pseudosequencing strategy considerably improves the power to detect association with rare variants. They also show that the use of allelic dosages results in much higher power than use of best-guess genotypes in these family-based data. Moreover, famSKAT shows greater power than famWS in most of scenarios we considered.
Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, United States of America; Department of Biostatistics, University of Washington, Seattle, Washington, United States of America.
Mohamad
M
Saad
Ellen M
EM
Wijsman
D000483:Alleles; D056726:Genetic Association Studies / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics*; D055106:Genome-Wide Association Study; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D010375:Pedigree; D012107:Research Design; D017422:Sequence Analysis, DNA / Q000191:economics; D012984:Software
MCMC; burden test; inheritance vectors; kernel statistic; mixed linear model; sequence data