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Enhancing Crop Breeding Using Population Genomics Approaches

Authors :
Jeffrey C. Dunne
James B. Holland
Luis Fernando Samayoa
Ryan J. Andres
Publication Year :
2020
Publisher :
Springer International Publishing, 2020.

Abstract

The use of genetic information to predict the value of individuals in plant breeding populations began about 40 years ago. The original paradigm was to identify genomic regions with outsize influence on a trait of economic value and then to use markers in that genomic region to select individuals carrying the desired allelic variants. An explosion of interest in mapping such quantitative trait loci (QTL) followed, with thousands of genomic regions associated with important traits across many species. The practical use of such information lagged well behind the discovery of QTL, however, due mostly to the problem that individual markers were often only associated with a small proportion of genetic variation, such that their value in selection was very small. In a few lucky cases, individual genes with very large effects on important traits were discovered, and these could be more easily turned into useful selection targets. Genome-wide association studies have improved the ability to identify individual variants associated with useful effects in crops, but the fundamental problem of accurately estimating marker effects and using them in selection remains for traits affected by many genes. Genomic selection was proposed by animal breeders as a way to more effectively use the information contained in dense genetic marker sets for the prediction of quantitative traits. Crop breeders subsequently discovered that this approach could be generalized across the diverse population structures and mating systems of plants and have begun implementing genomic selection in crops with success. Here, we first discuss how to identify and select for major genes and QTL in the breeding process. We then discuss the myriad benefits of implementing genomic selection to improve the rate of genetic gain.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........e95ea420f86a7d7ce723ad1abd8ae168
Full Text :
https://doi.org/10.1007/13836_2020_78