{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:51:04Z","timestamp":1775757064878,"version":"3.50.1"},"reference-count":87,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:p>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 \u201cmissing heritability\u201d. Determining gene\u2013gene 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, \u201cHierarchical structural CoMponent analysis of Gene\u2013Gene Interactions\u201d (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\u2013SNP 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 ).<\/jats:p>","DOI":"10.1142\/s0219720018400267","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T06:11:09Z","timestamp":1540966269000},"page":"1840026","source":"Crossref","is-referenced-by-count":10,"title":["HisCoM-GGI: Hierarchical structural component analysis of gene\u2013gene interactions"],"prefix":"10.1142","volume":"16","author":[{"given":"Sungkyoung","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Pharmacology, Yonsei University College of Medicine, 50-1 Yonsei-ro Seodaemun-gu, Seoul 03722, Republic of Korea"}]},{"given":"Sungyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Precision Medicine, Seoul National University Hospital, 71 Daehak-ro Jongno-gu, Seoul 03082, Republic of 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