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Kernel machine methods for integrative analysis of genome‐wide methylation and genotyping studies
Author(s) -
Zhao Ni,
Zhan Xiang,
Huang YenTsung,
Almli Lynn M,
Smith Alicia,
Epstein Michael P.,
Conneely Karen,
Wu Michael C.
Publication year - 2018
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22100
Subject(s) - pairwise comparison , epigenetics , kernel (algebra) , dna methylation , biology , computational biology , genome wide association study , trait , genotype , methylation , genetics , genotyping , genetic association , kernel method , computer science , statistics , gene , machine learning , artificial intelligence , mathematics , support vector machine , gene expression , combinatorics , single nucleotide polymorphism , programming language
ABSTRACT Many large GWAS consortia are expanding to simultaneously examine the joint role of DNA methylation in addition to genotype in the same subjects. However, integrating information from both data types is challenging. In this paper, we propose a composite kernel machine regression model to test the joint epigenetic and genetic effect. Our approach works at the gene level, which allows for a common unit of analysis across different data types. The model compares the pairwise similarities in the phenotype to the pairwise similarities in the genotype and methylation values; and high correspondence is suggestive of association. A composite kernel is constructed to measure the similarities in the genotype and methylation values between pairs of samples. We demonstrate through simulations and real data applications that the proposed approach can correctly control type I error, and is more robust and powerful than using only the genotype or methylation data in detecting trait‐associated genes. We applied our method to investigate the genetic and epigenetic regulation of gene expression in response to stressful life events using data that are collected from the Grady Trauma Project. Within the kernel machine testing framework, our methods allow for heterogeneity in effect sizes, nonlinear, and interactive effects, as well as rapid P ‐value computation.