  With advancements in next generation sequencing technology, a massive amount
of sequencing data are generated, offering a great opportunity to
comprehensively investigate the role of rare variants in the genetic etiology
of complex diseases. Nevertheless, this poses a great challenge for the
statistical analysis of high-dimensional sequencing data. The association
analyses based on traditional statistical methods suffer substantial power loss
because of the low frequency of genetic variants and the extremely high
dimensionality of the data. We developed a weighted U statistic, referred to as
WU-seq, for the high-dimensional association analysis of sequencing data. Based
on a non-parametric U statistic, WU-SEQ makes no assumption of the underlying
disease model and phenotype distribution, and can be applied to a variety of
phenotypes. Through simulation studies and an empirical study, we showed that
WU-SEQ outperformed a commonly used SKAT method when the underlying assumptions
were violated (e.g., the phenotype followed a heavy-tailed distribution). Even
when the assumptions were satisfied, WU-SEQ still attained comparable
performance to SKAT. Finally, we applied WU-seq to sequencing data from the
Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very
low density lipoprotein cholesterol.
