A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data

Abstract

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 correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method 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 ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten 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 ten other statistics.

UGT1A1 genetic variants are associated with increases in bilirubin levels in rheumatoid arthritis patients treated with sarilumab

“…To assess the association between each endpoint and candidate variants in the UGT1A1 gene region, we applied functional canonical correlation analysis (FCCA). The mathematical and computational details of FCCA have been described previously [ 19 ]. In this analysis, the variant is treated as the X matrix, while the longitudinal endpoint data are treated as the Y matrix.…”

Bivariate Causal Discovery and Its Applications to Gene Expression and Imaging Data Analysis

“…The ANMs were used to infer causal relationships between cholesterol, or working memory and image where only first FPC score (accounting for more than 95% of the imaging signal variation in the segmented region) was used to present the imaging signals in the segmented region. Table 2 presented P -values for testing causation (cholesterol → image variation) and association of cholesterol with images of 19 brain regions where the canonical correlation method was used to test association (Lin et al, 2017 ). Two remarkable features emerged.…”