ArticleBioinformatics (Oxford, England)2018
A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Whole-genome resequencing revealed genetic diversity and the haplotype containing MC1R associated with black coat color in Liangshan sheep.BMC genomics · 2026Article
- Extensive genetic interactions (epistasis) linked to alcohol use disorder in a high-risk population.Communications biology · 2026Article
- A genome-wide association study to elucidate the genetic basis of the cottonseed protein content in Upland cotton (Breeding science · 2026Article
- Meta-GWAS of Pig Semen Quality Traits Reveals Conserved Genes Regulating Mammalian Fertility.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Identification of MAPK10 as a Candidate Gene for High Milk Production in Water Buffaloes Through a Genome-Wide Association Study.Animals : an open access journal from MDPI · 2025Article
- GRAMMAR-Lambda Delivers Efficient Understanding of the Genetic Basis for Head Size in Catfish.Biology · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies.Genomics, proteomics & bioinformatics · 2024Article
- Genetic Diversity, Selection Signatures, and Genome-Wide Association Study Identify Candidate Genes Related to Litter Size in Hu Sheep.International journal of molecular sciences · 2024Article
- FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.Briefings in bioinformatics · 2024Article
- Whole-Genome Resequencing Reveals Genetic Diversity and Wool Trait-Related Genes in Liangshan Semi-Fine-Wool Sheep.Animals : an open access journal from MDPI · 2024Article
- Compressed variance component mixed model reveals epistasis associated with flowering inFrontiers in plant science · 2023Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Genetic Dissection of Epistatic Interactions Contributing Yield-Related Agronomic Traits in Rice Using the Compressed Mixed Model.Plants (Basel, Switzerland) · 2022Article
- Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations.G3 (Bethesda, Md.) · 2021Article
- Article
- Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency.The Plant journal : for cell and molecular biology · 2019Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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