Abstract
Genome-wide association studies have proven successful but they remain underpowered for detecting variants of weaker effect. Alternative methods propose to test for association by using an aggregate score that combines the effects of the most associated variants. The set of variants that are to be aggregated may come from either of two modeling approaches: single-marker or multi-marker. The goal of this paper is to evaluate this alternative strategy by using sets of single-nucleotide polymorphisms identified by the two modeling approaches in the simulated pedigree data set provided for the Genetic Analysis Workshop 18. We focused on quantitative traits association analysis of diastolic blood pressure and of Q1, which served to control the statistical significance of our results. We carried out all analyses with knowledge of the underlying simulation model. We found that the probability to replicate association with the aggregate score depends on the single-nucleotide polymorphism set size and, for smaller sets (≤100), on the modeling approach. Nonetheless, assessing the statistical significance of these results in this data set was challenging, likely because of linkage because we are analyzing pedigree data, and also because the genotypes were the same across the replicates. Further methods need to be developed to facilitate the application of this alternative strategy in pedigree data.
Highlights
Genome-wide association studies (GWAS) have proven successful in identifying common single-nucleotide polymorphisms (SNPs) associated with complex traits, but the underlying genetic architecture of these traits remains largely unknown
We focused on the simulated quantitative trait diastolic blood pressure measured at exam 1 (DBP_1)
The results are expressed as the percentage of replicates with significant evidence of association of polygenic score (PS) with DBP_1 at different nominal p values and by SNP set size S
Summary
Genome-wide association studies (GWAS) have proven successful in identifying common single-nucleotide polymorphisms (SNPs) associated with complex traits, but the underlying genetic architecture of these traits remains largely unknown. This classical approach is restricted to analyzing one SNP at a time and only those reaching genome-wide significance (a ≤1 × 10−8) are retained for further analyses. One way to circumvent this limitation has been through using larger sample sizes, increasing the power to detect SNPs of weaker effect Following their successful application in a through a classical single-marker analysis whereby each effect is estimated one at a time or, alternatively, through a multi-marker analysis whereby all effects are estimated simultaneously. The goal of this study is to compare the PS analysis using sets of SNPs derived from single-marker and multi-marker analyses and to evaluate the value of this novel analytical approach, with the intent of shedding light on the true genetic architecture of a complex quantitative trait in family-based data
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