Dataset Information
A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values.
Ontology highlight
ABSTRACT: 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.Availability and implementation
Our REMMA method can be freely accessed at https://github.com/chaoning/REMMA.Contact
liujf@cau.edu.cn.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Ning C
PROVIDER: S-EPMC5972602 | biostudies-literature | 2018 Jun
REPOSITORIES: biostudies-literature
Publications
A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values.
Bioinformatics (Oxford, England) 20180601 11
<h4>Motivation</h4>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.<h4>Results</h4>A rapid epistatic mixed-model association analysis (REMMA) method was developed to overcome computational limitation. This method first est ...[more]