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

A rapid epistatic mixed-model association analysis by linear retransformations of genomic... MotivationEpistasis 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.ResultsA 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 implementationOur REMMA method can be freely accessed at https://github.com/chaoning/REMMA.Supplementary informationSupplementary data are available at Bioinformatics online. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

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

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Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press.
ISSN
1367-4803
eISSN
1460-2059
DOI
10.1093/bioinformatics/bty017
Publisher site
See Article on Publisher Site

Abstract

MotivationEpistasis 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.ResultsA 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 implementationOur REMMA method can be freely accessed at https://github.com/chaoning/REMMA.Supplementary informationSupplementary data are available at Bioinformatics online.

Journal

Bioinformatics – Oxford University Press

Published: Jan 12, 2018

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