A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values

Chao Ning, Dan Wang, Huimin Kang, Raphael Mrode, Lei Zhou, Shizhong Xu, Jian Feng Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
32 Downloads (Pure)


Motivation Epistasis provides a feasible way for probing potential genetic mechanism of complex traits. However, time-consuming computation challenges successful detection of interaction in practice, especially when linear mixed model (LMM) is used to control type I error in the presence of population structure and cryptic relatedness. Results A rapid epistatic mixed-model association analysis (REMMA) method was developed to overcome computational limitation. This method first estimates individuals' epistatic effects by an extended genomic best linear unbiased prediction (EG-BLUP) model with additive and epistatic kinship matrix, then pairwise interaction effects are obtained by linear retransformations of individuals' epistatic effects. Simulation studies showed that REMMA could control type I error and increase statistical power in detecting epistatic QTNs in comparison with existing LMM-based FaST-LMM. We applied REMMA to two real datasets, a mouse dataset and the Wellcome Trust Case Control Consortium (WTCCC) data. Application to the mouse data further confirmed the performance of REMMA in controlling type I error. For the WTCCC data, we found most epistatic QTNs for type 1 diabetes (T1D) located in a major histocompatibility complex (MHC) region, from which a large interacting network with 12 hub genes (interacting with ten or more genes) was established.

Original languageEnglish
Pages (from-to)1817-1825
Number of pages9
Issue number11
Early online date12 Jan 2018
Publication statusPrint publication - 1 Jun 2018
Externally publishedYes


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