Author Information

R. W. Doerge
Bruce A. Craig

Abstract

Substantial gains have been made in locating regions of agricultural genomes associated with characteristics, diseases, and agroeconomic traits. These gains have relied heavily on the ability to estimate the association between DNA markers and regions of a genome (quantitative trait loci or QTL) related to a particular trait. The majority of these advances have focused on diploid species (two homologous chromosomes per set), even though many important agricultural crops are, in fact, polyploid (more than two homologous chromosomes per set). The purpose of our work is to initiate an algorithmic approach for model selection and QTL detection in polyploid species. This approach involves the enumeration of all possible chromosomal configurations (models) that may result in a gamete, model reduction based on estimation of marker dosage from progeny data, and lastly model selection. While simplified for initial explanation, our approach has demonstrated itself as being extendible to many breeding schemes and less restricted settings.

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Apr 30th, 8:30 AM

AN INTRODUCTION TO MODEL SELECTION FOR QUANTITATIVE TRAIT LOCUS ANALYSIS IN POLYPLOIDS

Substantial gains have been made in locating regions of agricultural genomes associated with characteristics, diseases, and agroeconomic traits. These gains have relied heavily on the ability to estimate the association between DNA markers and regions of a genome (quantitative trait loci or QTL) related to a particular trait. The majority of these advances have focused on diploid species (two homologous chromosomes per set), even though many important agricultural crops are, in fact, polyploid (more than two homologous chromosomes per set). The purpose of our work is to initiate an algorithmic approach for model selection and QTL detection in polyploid species. This approach involves the enumeration of all possible chromosomal configurations (models) that may result in a gamete, model reduction based on estimation of marker dosage from progeny data, and lastly model selection. While simplified for initial explanation, our approach has demonstrated itself as being extendible to many breeding schemes and less restricted settings.