INTEGRATED QUANTILE RANK TEST (IQRAT) FOR GENE-LEVEL ASSOCIATIONSINTEGRATED QUANTILE RANK TEST (IQRAT) FOR GENE-LEVEL ASSOCIATIONS

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.

Automated summary

Gene-based testing is commonly used in genetic association studies, but existing tests may miss complex associations. This paper proposes a new gene-level association test that integrates quantile rank score process for better accommodation. The proposed test statistics are efficient, provide insights into risk stratification, are distribution free, and computationally efficient. Simulation studies and real data applications show that the proposed approach outperforms conventional mean-based tests.