23
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Bayesian LASSO for Quantitative Trait Loci Mapping

      ,

      Genetics

      Genetics Society of America

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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 mapping multiple QTL that simultaneously fit and estimate all possible genetic effects associated with all markers. The proposed models use prior distributions for the genetic effects that are scale mixtures of normal distributions with mean zero and variances distributed to give each effect a high probability of being near zero. We consider two types of priors for the variances, exponential and scaled inverse-chi(2) distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Student's t model, respectively. Unlike most applications where fixed values are preset for hyperparameters in the priors, we treat all hyperparameters as unknowns and estimate them along with other parameters. Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data.

          Related collections

          Most cited references 21

          • Record: found
          • Abstract: not found
          • Article: not found

          Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)

           Andrew Gelman (2006)
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A simple regression method for mapping quantitative trait loci in line crosses using flanking markers.

             C S Haley,  S Knott (1992)
            The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Adaptive sparseness for supervised learning

                Bookmark

                Author and article information

                Journal
                Genetics
                Genetics
                Genetics Society of America
                0016-6731
                1943-2631
                June 16 2008
                June 2008
                June 2008
                May 27 2008
                : 179
                : 2
                : 1045-1055
                Article
                10.1534/genetics.107.085589
                2429858
                18505874
                © 2008

                Comments

                Comment on this article