A computationally efficient Bayesian seemingly unrelated regressions model for high‐dimensional quantitative trait loci discovery
نویسندگان
چکیده
منابع مشابه
Bayesian modelling of multivariate quantitative traits using seemingly unrelated regressions.
We investigate a Bayesian approach to modelling the statistical association between markers at multiple loci and multivariate quantitative traits. In particular, we describe the use of Bayesian Seemingly Unrelated Regressions (SUR) whereby genotypes at the different loci are allowed to have non-simultaneous effects on the phenotypes considered with residuals from each regression assumed correla...
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This article considers the seemingly unrelated regression (SUR) model first analyzed by Zellner (1962). We describe estimators used in the basic model as well as recent extensions.
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A reversible jump Markov chain Monte Carlo (MCMC) algorithm is illustrated to infer the number of quantitative trait loci (QTL) a ecting a phenotypic trait, their chromosomal locations, and their e ects. A multi-loci model is t to quantitative trait and molecular marker data, with the trait response modeled as a linear function of the additive and dominance e ects of the unknown QTL genotypes. ...
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The mapping of quantitative trait loci (QTL) is to identify molecular markers or genomic loci that influence the variation of complex traits. The problem is complicated by the facts that QTL data usually contain a large number of markers across the entire genome and most of them have little or no effect on the phenotype. In this article, we propose several Bayesian hierarchical models for mappi...
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Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covaria...
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ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series C (Applied Statistics)
سال: 2021
ISSN: 0035-9254,1467-9876
DOI: 10.1111/rssc.12490