HisCoM-PCA: Hierarchical structural Component Model for Pathway analysis of Common vAriants

common variants;Genome-wide association study;hierarchical components;pathway analysis;574.8732

Nan Jiang자연과학대학 협동과정 생물정보학전공 ;

University:서울대학교 대학원

关键词: common variants;    Genome-wide association study;    hierarchical components;    pathway analysis;    574.8732;   

Others  :  http://s-space.snu.ac.kr/bitstream/10371/161673/1/000000158187.pdf

美国|英语

来源: Seoul National University Open Repository

【 摘 要 】

Genome-wide association studies (GWAS) have been widely used in identifying phenotype-related genetic variants by many statistical methods, such as logistic regression and linear regression. However, the identified SNPs with stringent statistical significance just explain a small portion of the overall estimated genetic heritability. To address this ;;missing heritability’ issue, gene-based and pathway-based analysis have been developed in many studies. The biological mechanisms and some related pathways have been reported using pathway-based methods in GWAS datasets. However, many of these methods often neglecting the correlation between genes and between pathways. Here, we construct a hierarchical component model with considering of the correlation existing both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, named Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summaries the common variants in each gene into the gene-level statistics and then analyzes all pathways simultaneously by ridge-type penalization on both gene and pathway effects on the phenotype. The statistical significance of the gene and pathway coefficients can be examined by permutation tests. Through simulation study for both binary and continuous phenotypes using GAW17 simulation dataset, HisCoM-PCA controlled type I error well and showed a higher empirical power than several comparison methods. In addition, we applied our method to SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance. Our approach has an advantage of providing an intuitive biological interpretation for the association between common variants and phenotypes through the pathway information.