  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 based on detecting differences in a single
summary statistic, such as the mean or the variance, 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 which integrate quantile rank score processes 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 on 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 hypothesis and conducted
large scale simulation studies to investigate their finite sample performance.
We applied the proposed tests to the Metabochip data to identify genetic
associations with lipid traits and compared the results with those of the
Burden and SKAT tests.
