Analysis of genotype by methylation interactions through sparsity-inducing regularized regression

Wenda Zhou,Shaw-Hwa Lo

doi:10.1186/s12919-018-0145-6

Abstract

In this paper, we consider the use of the least absolute shrinkage and selection operator (LASSO)-type regression techniques to detect important genetic or epigenetic loci in genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS). We demonstrate how these techniques can be adapted to provide quantifiable uncertainty using stability selection, including explicit control of the family-wise error rate. We also consider variants of the LASSO, such as the group LASSO, to study genetic and epigenetic interactions. We use these techniques to reproduce some existing results on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) data set, which collects from 991 individuals blood triglyceride and differential methylation at 464,000 cytosine-phosphate-guanine (CpG) sites and 761,000 single-nucleotide polymorphisms (SNPs), and to identify new research directions. Epigenome-wide and genome-wide models based on the LASSO are considered, as well as an interaction model limited to chromosome 11. The analyses replicate findings concerning 2 CpGs in carnitine palmitoyltransferase 1A (CPT1A). Some suggestions are made regarding potentially interesting directions for the analysis of genetic and epigenetic interactions.

Highlights

Blood lipids are linked to increased risk of heart disease, heart attack and stroke
Researchers have explored the effects of genetic variation [1] and epigenetic variation [2] on lipid metabolism
Tend to lose power when faced with the high-dimensional nature of these problems, as adjusting for multiple comparisons is necessary

Summary

Introduction

Blood lipids are linked to increased risk of heart disease, heart attack and stroke. Researchers have explored the effects of genetic variation [1] and epigenetic variation [2] on lipid metabolism. Adaptations of the least absolute shrinkage and selection operator (LASSO) selector, first proposed by Tibshirani [3], have proven powerful in the context of high-dimensional variable selection. We combine such a method with the strategy of stability selection [4] to obtain a global selection strategy that enables us to control the family-wise error rate. We apply these methods to study both genetic and epigenetic effects on blood lipids. We attempt to extend such methods to tackle the genetic– epigenetic interactions and their effects on blood lipids

Methods

Results

Conclusion