HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions

J Bioinform Comput Biol. 2018 Dec;16(6):1840026. doi: 10.1142/S0219720018400267. Epub 2018 Oct 30.

Abstract

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene-gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP-SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 × SPOCK1) and (LINGO2 × ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website ( http://statgen.snu.ac.kr/software/hiscom-ggi ).

Keywords: Genome-wide association study; generalized structured component analysis; gene–gene interactions; ridge regression.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Asian People / genetics
  • Body Mass Index*
  • Databases, Genetic
  • Epistasis, Genetic*
  • Female
  • Genome-Wide Association Study / statistics & numerical data
  • Genomics / methods*
  • Genomics / statistics & numerical data
  • Humans
  • Male
  • Membrane Proteins / genetics
  • Models, Genetic
  • Nerve Tissue Proteins / genetics
  • Nuclear Receptor Co-Repressor 2 / genetics
  • Polymorphism, Single Nucleotide*
  • Proteoglycans / genetics
  • Transcription Factors / genetics

Substances

  • LINGO2 protein, human
  • Membrane Proteins
  • NCOR2 protein, human
  • Nerve Tissue Proteins
  • Nuclear Receptor Co-Repressor 2
  • Proteoglycans
  • SPOCK1 protein, human
  • Transcription Factors
  • ZNF385D protein, human