Abstract
Genome-wide association studies have helped us identify thousands of common variants associated with several widespread complex diseases. However, for most traits, these variants account for only a small fraction of phenotypic variance or heritability. Next-generation sequencing technologies are being used to identify additional rare variants hypothesized to have higher effect sizes than the already identified common variants, and to contribute significantly to the fraction of heritability that is still unexplained. Several pooling strategies have been proposed to test the joint association of multiple rare variants, because testing them individually may not be optimal. Within a gene or genomic region, if there are both rare and common variants, testing their joint association may be desirable to determine their synergistic effects. We propose new methods to test the joint association of several rare and common variants with binary and quantitative traits. Our association test for quantitative traits is based on genotypic and phenotypic measures of similarity between pairs of individuals. For the binary trait or case-control samples, we recently proposed an association test based on the genotypic similarity between individuals. Here, we develop a modified version of this test for rare variants. Our tests can be used for samples taken from multiple subpopulations. The power of our test statistics for case-control samples and quantitative traits was evaluated using the GAW17 simulated data sets. Type I error rates for the proposed tests are well controlled. Our tests are able to identify some of the important causal genes in the GAW17 simulated data sets.
Highlights
Genome-wide association studies have helped us to understand the genetic basis of several complex diseases and have identified thousands of variants associated with such diseases [http://www.genome.gov/gwastudies]
To test the association of quantitative traits, we propose a new test statistic, which we refer to as the quantitative trait kernel-based association test (KBAT) (QT-KBAT); this statistic is based on genotypic and phenotypic measures of similarity between pairs of individuals
Test statistic for quantitative traits (QT-KBAT) Based on the measures of genotypic and phenotypic similarity, we introduce a KBAT-type method to test the association of genotypes with quantitative phenotypes
Summary
Genome-wide association studies have helped us to understand the genetic basis of several complex diseases and have identified thousands of variants associated with such diseases [http://www.genome.gov/gwastudies]. These variants explain only a small proportion of the phenotypic variance or heritability of a trait [1,2]. One active area of research in statistical epidemiology is the development of efficient statistical tests to detect associations involving rare variants. Many of the procedures proposed in the literature involve some kind of pooling or the use of a weighted combination of the rare variants to establish the joint association [5]. Zawistowski et al [8] proposed a simple pooling technique based on cumulative minor allele counts, which can be used for imputed rare variants (e.g., rare variants imputed based on the 1000 Genomes Project)
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