Literature DB >> 19187246

Easy and flexible Bayesian inference of quantitative genetic parameters.

Patrik Waldmann1.   

Abstract

There has been a tremendous advancement of Bayesian methodology in quantitative genetics and evolutionary biology. Still, there are relatively few publications that apply this methodology, probably because the availability of multipurpose and user-friendly software is somewhat limited. It is here described how only a few rows of code of the well-developed and very flexible Bayesian software WinBUGS (Lunn et al. 2000) can be used for inference of the additive polygenic variance and heritabilty in pedigrees of general design. The presented code is illustrated by application to an earlier published dataset of Scots pine.

Entities:  

Mesh:

Year:  2009        PMID: 19187246     DOI: 10.1111/j.1558-5646.2009.00645.x

Source DB:  PubMed          Journal:  Evolution        ISSN: 0014-3820            Impact factor:   3.694


  3 in total

1.  Bayesian inference of genetic parameters based on conditional decompositions of multivariate normal distributions.

Authors:  Jon Hallander; Patrik Waldmann; Chunkao Wang; Mikko J Sillanpää
Journal:  Genetics       Date:  2010-03-29       Impact factor: 4.562

2.  Bayesian inference of mixed models in quantitative genetics of crop species.

Authors:  Fabyano Fonseca E Silva; José Marcelo Soriano Viana; Vinícius Ribeiro Faria; Marcos Deon Vilela de Resende
Journal:  Theor Appl Genet       Date:  2013-04-20       Impact factor: 5.699

3.  An efficient technique for Bayesian modeling of family data using the BUGS software.

Authors:  Harold T Bae; Thomas T Perls; Paola Sebastiani
Journal:  Front Genet       Date:  2014-11-18       Impact factor: 4.599

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.