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Investigating the pleiotropic effects of genetic variants can increasestatistical power, provide important information to achieve deep understandingof the complex genetic structures of disease, and offer powerful tools fordesigning effective treatments with fewer side effects. However, the currentmultiple phenotype association analysis paradigm lacks breadth (number ofphenotypes and genetic variants jointly analyzed at the same time) and depth(hierarchical structure of phenotype and genotypes). A key issue for highdimensional pleiotropic analysis is to effectively extract informative internalrepresentation and features from high dimensional genotype and phenotype data.To explore multiple levels of representations of genetic variants, learn theirinternal patterns involved in the disease development, and overcome criticalbarriers in advancing the development of novel statistical methods andcomputational algorithms for genetic pleiotropic analysis, we proposed a newframework referred to as a quadratically regularized functional CCA (QRFCCA)for association analysis which combines three approaches: (1) quadraticallyregularized matrix factorization, (2) functional data analysis and (3)canonical correlation analysis (CCA). Large-scale simulations show that theQRFCCA has a much higher power than that of the nine competing statistics whileretaining the appropriate type 1 errors. To further evaluate performance, theQRFCCA and nine other statistics are applied to the whole genome sequencingdataset from the TwinsUK study. We identify a total of 79 genes with rarevariants and 67 genes with common variants significantly associated with the 46traits using QRFCCA. The results show that the QRFCCA substantially outperformsthe nine other statistics.

A Quadratically Regularized Functional Canonical Correlation Analysis for Identifying the Global Structure of Pleiotropy with NGS Data