HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions
- Authors
- Choi, Sungkyoung; Lee, Sungyoung; Kim, Yongkang; Hwang, Heungsun; Park, Taesung
- Issue Date
- Dec-2018
- Publisher
- Imperial College Press
- Keywords
- Genome-wide association study; gene-gene interactions; generalized structured component analysis; ridge regression
- Citation
- Journal of Bioinformatics and Computational Biology, v.16, no.6(SI), pp.1 - 25
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Bioinformatics and Computational Biology
- Volume
- 16
- Number
- 6(SI)
- Start Page
- 1
- End Page
- 25
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/5092
- DOI
- 10.1142/S0219720018400267
- ISSN
- 0219-7200
- 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 x SPOCK1) and (LINGO2 x 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).
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