An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.

Set-based analysis that jointly considers multiple predictors in a group has been broadly conducted for association tests. However, their power can be sensitive to the distribution of phenotypes, and the underlying relationships between predictors and outcomes. Moreover, most of the set-based methods are designed for single-trait analysis, making it hard to explore the pleiotropic effect and borrow information when multiple phenotypes are available. Here, we propose a kernel-based multivariate U-statistics (KMU) that is robust and powerful in testing the association between a set of predictors and multiple outcomes. We employed a rank-based kernel function for the outcomes, which makes our method robust to various outcome distributions. Rather than selecting a single kernel, our test statistics is built based on multiple kernels selected in a data-driven manner, and thus is capable of capturing various complex relationships between predictors and outcomes. The asymptotic properties of our test statistics have been developed. Through simulations, we have demonstrated that KMU has controlled type I error and higher power than its counterparts. We further showed its practical utility by analyzing a whole genome sequencing data from Alzheimer's Disease Neuroimaging Initiative study, where novel genes have been detected to be associated with imaging phenotypes.

Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection.

Single-marker genome-wide association studies (GWAS) have successfully detected associations between single nucleotide polymorphisms (SNPs) and agronomic traits such as flowering time and grain yield in barley. However, the analysis of individual SNPs can only account for a small proportion of genetic variation, and can only provide limited knowledge on gene network interactions. Gene-based GWAS approaches provide enormous opportunity both to combine genetic information and to examine interactions among genetic variants. Here, we revisited a previously published phenotypic and genotypic data set of 895 barley varieties grown in two years at four different field locations in Australia. We employed statistical models to examine gene-phenotype associations, as well as two-way epistasis analyses to increase the capability to find novel genes that have significant roles in controlling flowering time in barley. Genetic associations were tested between flowering time and corresponding genotypes of 174 putative flowering time-related genes. Gene-phenotype association analysis detected 113 genes associated with flowering time in barley, demonstrating the unprecedented power of gene-based analysis. Subsequent two-way epistasis analysis revealed 19 pairs of gene×gene interactions involved in controlling flowering time. Our study demonstrates that gene-based association approaches can provide higher capacity for future crop improvement to increase crop performance and adaptation to different environments.

An optimal kernel-based U-statistic method for quantitative gene-set association analysis.

Single-variant-based genome-wide association studies have successfully detected many genetic variants that are associated with a number of complex traits. However, their power is limited due to weak marginal signals and ignoring potential complex interactions among genetic variants. The set-based strategy was proposed to provide a remedy where multiple genetic variants in a given set (e.g., gene or pathway) are jointly evaluated, so that the systematic effect of the set is considered. Among many, the kernel-based testing (KBT) framework is one of the most popular and powerful methods in set-based association studies. Given a set of candidate kernels, the method has been proposed to choose the one with the smallest p-value. Such a method, however, can yield inflated Type 1 error, especially when the number of variants in a set is large. Alternatively one can get p values by permutations which, however, could be very time-consuming. In this study, we proposed an efficient testing procedure that cannot only control Type 1 error rate but also have power close to the one obtained under the optimal kernel in the candidate kernel set, for quantitative trait association studies. Our method, a maximum kernel-based U-statistic method, is built upon the KBT framework and is based on asymptotic results under a high-dimensional setting. Hence it can efficiently deal with the case where the number of variants in a set is much larger than the sample size. Both simulation and real data analysis demonstrate the advantages of the method compared with its counterparts.