  Investigating the pleiotropic effects of genetic variants can increase
statistical power, provide important information to achieve deep understanding
of the complex genetic structures of disease, and offer powerful tools for
designing effective treatments with fewer side effects. However, the current
multiple phenotype association analysis paradigm lacks breadth (number of
phenotypes and genetic variants jointly analyzed at the same time) and depth
(hierarchical structure of phenotype and genotypes). A key issue for high
dimensional pleiotropic analysis is to effectively extract informative internal
representation and features from high dimensional genotype and phenotype data.
To explore multiple levels of representations of genetic variants, learn their
internal patterns involved in the disease development, and overcome critical
barriers in advancing the development of novel statistical methods and
computational algorithms for genetic pleiotropic analysis, we proposed a new
framework referred to as a quadratically regularized functional CCA (QRFCCA)
for association analysis which combines three approaches: (1) quadratically
regularized matrix factorization, (2) functional data analysis and (3)
canonical correlation analysis (CCA). Large-scale simulations show that the
QRFCCA has a much higher power than that of the nine competing statistics while
retaining the appropriate type 1 errors. To further evaluate performance, the
QRFCCA and nine other statistics are applied to the whole genome sequencing
dataset from the TwinsUK study. We identify a total of 79 genes with rare
variants and 67 genes with common variants significantly associated with the 46
traits using QRFCCA. The results show that the QRFCCA substantially outperforms
the nine other statistics.
