ArticleInternational journal of plant genomics2010
Significance test and genome selection in bayesian shrinkage analysis.
Article in International journal of plant genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models.Scientific reports · 2021Article
- Identifying rare and common variants with Bayesian variable selection.BMC proceedings · 2016Article
- Inference of posterior inclusion probability of QTLs in Bayesian shrinkage analysis.Genetics research · 2015Article
- Article
- Next generation modeling in GWAS: comparing different genetic architectures.Human genetics · 2014Article
- A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model.Genetics · 2012Article
- Estimates of marker effects for measures of milk flow in the Italian brown Swiss dairy cattle population.BMC veterinary research · 2012Article
- Genotype by environment interaction of quantitative traits: a case study in barley.G3 (Bethesda, Md.) · 2012Article
- An expectation and maximization algorithm for estimating Q X E interaction effects.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2012Article
- Article
- Mapping environment-specific quantitative trait loci.Genetics · 2010Article
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Authors and funding
2 authors.
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Abstract
Bayesian shrinkage analysis is the state-of-the-art method for whole genome analysis of quantitative traits. It can estimate the genetic effects for the entire genome using a dense marker map. The technique is now called genome selection. A nice property of the shrinkage analysis is that it can estimate effects of QTL as small as explaining 2% of the phenotypic variance in a typical sample size of 300-500 individuals. In most cases, QTL can be detected with simple visual inspection of the entire genome for the effect because the false positive rate is low. As a Bayesian method, no significance test is needed. However, it is still desirable to put some confidences on the estimated QTL effects. We proposed to use the permutation test to draw empirical thresholds to declare significance of QTL under a predetermined genome wide type I error. With the permutation test, Bayesian shrinkage analysis can be routinely used for QTL detection.
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