Bayesian analysis of complex traits in pedigreed plant populations

By Marco C. A. M. Bink, Martin P. Boer, C.J.F. ter Braak, Johannes Jansen, Roeland E. Voorrips, W. Weg. Euphytica, 2008

122 citations recorded by the source corpus.

A Bayesian approach to analyze complex traits is presented that can help plant eneticists and breeders in exploiting the marker and phenotypic data on pedigreed populations as available from ongoing breeding programs. The statistical model for the quantitative trait may include non-genetic and genetic components. The latter component can be divided into QTL on known marker linkage groups, major genes and a polygenic component. The full probability model, prior assumptions on model variables are presented and criterion for model selection and posterior inferences are given. Simulated data on a known pedigreed population structure of the EU project HiDRAS was used to illustrate the use of the Bayesian approach to analyze complex traits. It was shown that estimates for QTL parameters were more accurate when non-genetic factors were included in the model and when a polygenic component was included when not all linkage groups were analyzed simultaneously. The Bayesian approach has been implemented into the software package FlexQTL and allows plant breeders explore their pedigreed populations for segregating QTL alleles that are relevant in their breeding program.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/14cc847b815fad6a72e6e016e57db6fcc7ee6f69

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Citations in the corpus

Genotyping of pedigreed apple breeding material with a genome-covering set of SSRs: trueness-to-type of cultivars and their parentages (2011, methodology): “…breeding programmes, thus reducing the need to establish large and expensive specific mapping progenies, as well as reducing the need for the high marker densities required for linkage disequilibrium-mapping in presumed genetically unstructured germplasm (van de Weg et al. 2004; Bink et al. 2008).”
Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree (2009, methodology): “This happens because, in P&M, part of the infinitesimal additive effect is captured by the regression on markers (e.g., Habier et al. 2007; Bink et al. 2008).”