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    Online Resource
    Online Resource
    World Scientific Pub Co Pte Ltd ; 2018
    In:  Journal of Bioinformatics and Computational Biology Vol. 16, No. 06 ( 2018-12), p. 1840026-
    In: Journal of Bioinformatics and Computational Biology, World Scientific Pub Co Pte Ltd, Vol. 16, No. 06 ( 2018-12), p. 1840026-
    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 [Formula: see text] SPOCK1) and (LINGO2 [Formula: see text] 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 ).
    Type of Medium: Online Resource
    ISSN: 0219-7200 , 1757-6334
    Language: English
    Publisher: World Scientific Pub Co Pte Ltd
    Publication Date: 2018
    SSG: 12
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