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Abstract
High-throughput sequencing technology allows researchers to test associations between phenotypes and all the variants identified throughout the genome, and is especially useful for analyzing rare variants. However, the statistical power to identify phenotype-associated rare variants is very low with typical genome-wide association studies because of their low allele frequencies among unrelated individuals. In contrast, a family-based design may have more power because rare variants are more likely to be enriched in families than among unrelated individuals. Regardless, an analysis of family-based association studies needs to account appropriately for relatedness between family members. We analyzed the observed quantitative trait systolic blood pressure as well as the simulated Q1 data in the Genetic Analysis Workshop 18 data set using 4 tests: (a) a single-variant test, (b) a collapsing test, (c) a single-variant test where familial relatedness was accounted for, and (d) a collapsing test where familial relatedness was accounted for. We then compared the results of the 4 methods and observed that adjusting for familial relatedness could appropriately control the false-positive rate while maintaining reasonable power to detect several strongly associated variants/genes.
Highlights
Current platforms for genome-wide association studies are limited to scanning common variants
Rare variants may contribute a significant proportion of heritability, the statistical power to detect rare variants is low because of their low allele frequencies
We used the following linear mixed model to adjust for familial relatedness: Y = Cγ + Xβ + Zα + e where Y represents the phenotype, C represents the collection of the covariates, X includes the genotypes of the variants to be tested, and Z is the design matrix for the whole-genome polygenic random effects α
Summary
Current platforms for genome-wide association studies are limited to scanning common variants. Recent advances in high-throughput sequencing technologies have provided us great opportunities to delve deeper into the genetic components of complex traits by identifying millions of rare variants in the human genome [1] and allowing them to be tested for associations with complex traits. In an effort to increase the power to detect rare-variant associations, many methods have been proposed to aggregate the effects of multiple rare variants within a specific functional unit, for example, a gene [2]. Among those methods, the kernel score test has enjoyed great popularity thanks to its flexibility and computational efficiency [3].
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