Integrated Quantile RAnk Test (iQRAT) for gene-level associations

Abstract

Gene-based testing is a commonly employed strategy in many genetic association studies. Gene-trait associations can be complex due to underlying population heterogeneity, gene-environment interactions, and various other reasons. Existing gene-based tests, such as burden and sequence kernel association tests (SKAT), are mean-based tests and may miss or underestimate higher-order associations that could be scientifically interesting. In this paper we propose a new family of gene-level association tests that integrate quantile rank score process to better accommodate complex associations. The resulting test statistics have multiple advantages: (1) they are almost as efficient as the best existing tests when the associations are homogeneous across quantile levels and have improved efficiency for complex and heterogeneous associations; (2) they provide useful insights into risk stratification; (3) the test statistics are distribution free and could hence accommodate a wide range of underlying distributions, and (4) they are computationally efficient. We established the asymptotic properties of the proposed tests under the null and alternative hypotheses and conducted large-scale simulation studies to investigate their finite sample performance. The performance of the proposed approach is compared with that of conventional mean-based tests, that is, the burden and SKAT tests, through simulation studies and applications to a metabochip dataset on lipid traits and to the genotype-tissue expression data in GTEx to identify eGenes, that is, genes whose expression levels are associated with cis-eQTLs.