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Exploring gene–gene interaction in family‐based data with an unsupervised machine learning method: EPISFA
Author(s) -
Xiang Xiao,
Wang Siyue,
Liu Tianyi,
Wang Mengying,
Li Jiawen,
Jiang Jin,
Wu Tao,
Hu Yonghua
Publication year - 2020
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22342
Subject(s) - multifactor dimensionality reduction , epistasis , population stratification , linkage disequilibrium , curse of dimensionality , computer science , machine learning , artificial intelligence , dimensionality reduction , population , biology , single nucleotide polymorphism , genetics , gene , genotype , demography , sociology
Gene-gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were often developed for case-control designs with less considerations for their applications in families. Family-based studies are robust against bias led from population stratification in genetic studies and helpful in understanding G × G. We proposed a new algorithm epistasis sparse factor analysis (EPISFA) and epistasis sparse factor analysis for linkage disequilibrium (EPISFA-LD) based on unsupervised machine learning to screen G × G. Extensive simulations were performed to compare EPISFA/EPISFA-LD with a classical family-based algorithm FAM-MDR (family-based multifactor dimensionality reduction). The results showed that EPISFA/EPISFA-LD is a tool of both high power and computational efficiency that could be applied in family designs and is applicable within high-dimensionality datasets. Finally, we applied EPISFA/EPISFA-LD to a real dataset drawn from the Fangshan/family-based Ischemic Stroke Study in China. Five pairs of G × G were discovered by EPISFA/EPISFA-LD, including three pairs verified by other algorithms (FAM-MDR and logistic), and an additional two pairs uniquely identified by EPISFA/EPISFA-LD only. The results from EPISFA might offer new insights for understanding the genetic etiology of complex diseases. EPISFA/EPISFA-LD was implemented in R. All relevant source code as well as simulated data could be freely downloaded from https://github.com/doublexism/episfa.