ArticleGenetics2015
An Efficient Genome-Wide Multilocus Epistasis Search.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat.International journal of molecular sciences · 2023Article
- Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways.PLoS computational biology · 2021Article
- Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection.Genetics · 2020Article
- Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits.Genetics · 2019Article
- Article
- Lost in Translation: On the Problem of Data Coding in Penalized Whole Genome Regression with Interactions.G3 (Bethesda, Md.) · 2019Article
- Enrichment of B cell receptor signaling and epidermal growth factor receptor pathways in monoclonal gammopathy of undetermined significance: a genome-wide genetic interaction study.Molecular medicine (Cambridge, Mass.) · 2018Article
- Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population.Genetics · 2018Article
- A fast algorithm for Bayesian multi-locus model in genome-wide association studies.Molecular genetics and genomics : MGG · 2017Article
- Bayesian reversible-jump for epistasis analysis in genomic studies.BMC genomics · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There has been a continuing interest in approaches that analyze pairwise locus-by-locus (epistasis) interactions using multilocus association models in genome-wide data sets. In this paper, we suggest an approach that uses sure independence screening to first lower the dimension of the problem by considering the marginal importance of each interaction term within the huge loop. Subsequent multilocus association steps are executed using an extended Bayesian least absolute shrinkage and selection operator (LASSO) model and fast generalized expectation-maximization estimation algorithms. The potential of this approach is illustrated and compared with PLINK software using data examples where phenotypes have been simulated conditionally on marker data from the Quantitative Trait Loci Mapping and Marker Assisted Selection (QTLMAS) Workshop 2008 and real pig data sets.
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