Quantitative trait locus analysis in polyploids

Dachuang Cao, Purdue University

Abstract

The development of quantitative trait locus (QTL) mapping in diploid species has evolved from QTL mapping for a single locus and single trait to multiple loci and multiple traits. Similar progress in polyploid QTL mapping has not occurred primarily due to the complex nature of the genetic architecture in polyploids. To date, a single marker analysis method has been proposed for any autopolyploid with an even number of ploidy level for dominant marker systems, and QTL interval mapping has primarily focused on tetraploids since the number of parameters and the complexity of the model expands quickly as the ploidy number increases. Here, a model selection based interval mapping method is proposed to handle any polyploid of even ploidy level. This is an extension of the single marker analysis method which involves estimating the marker dosages, QTL dosages, and the trait effect simultaneously. The performance of this method is investigated through extensive simulation studies, and via application to autotetraploid alfalfa data. A Bayesian approach for QTL mapping in polyploids is also proposed in order to assess the uncertainty in the estimated parameters. Rather than selecting the most likely configuration, as in the frequentist case, the Bayesian method provides posterior probability estimates for parental QTL configurations and density estimates for parameters within each possible QTL configuration. Because the dimensionality of the respective parameter space varies for different parental configurations, a reversible jump Monte Carlo Markov chain (MCMC) is employed to sample from the joint posterior distribution for Bayesian inference.

Degree

Ph.D.

Advisors

Craig, Purdue University.

Subject Area

Statistics

COinS