Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies

Ni Zhao, Xiang Zhan, Yen Tsung Huang, Lynn M. Almli, Alicia Smith, Michael P. Epstein, Karen Conneely, Michael C. Wu

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalGenetic Epidemiology
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Methylation
Genotype
Genome
Epigenomics
Joints
Genome-Wide Association Study
Gene Expression Regulation
DNA Methylation
Genes
Phenotype
Wounds and Injuries

Keywords

  • Composite kernel
  • Integrative analysis
  • Kernel machine regression
  • Variance component test

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

Cite this

Zhao, N., Zhan, X., Huang, Y. T., Almli, L. M., Smith, A., Epstein, M. P., ... Wu, M. C. (Accepted/In press). Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies. Genetic Epidemiology. https://doi.org/10.1002/gepi.22100

Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies. / Zhao, Ni; Zhan, Xiang; Huang, Yen Tsung; Almli, Lynn M.; Smith, Alicia; Epstein, Michael P.; Conneely, Karen; Wu, Michael C.

In: Genetic Epidemiology, 01.01.2018.

Research output: Contribution to journalArticle

Zhao, Ni ; Zhan, Xiang ; Huang, Yen Tsung ; Almli, Lynn M. ; Smith, Alicia ; Epstein, Michael P. ; Conneely, Karen ; Wu, Michael C. / Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies. In: Genetic Epidemiology. 2018.
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