The Usage of Genomic Selection Strategy in Plant Breeding

Abstract

Major paradigm shift in plant breeding since the availability of molecular marker technology is that mapping and characterizing the genetic loci that control a trait will lead to improved breeding. Often, one of the rationales for cloning of QTL is to develop the "perfect marker" for MAS, perhaps based on a functional polymorphism. In contrast, an advantage of genomic selection is precisely its black box approach to exploiting genotyping technology to expedite genetic progress. This is an advantage in our view because it does not rely on a "breeding by design" engineering approach to cultivar development requiring knowledge of biological function before the creation of phenotypes. Breeders can therefore use genomic selection without the large upfront cost of obtaining that knowledge. In addition, genomic selection can maintain the creative nature of phenotypic selection which couple's random mutation and recombination to sometimes arrive at solutions outside the engineer's scope. Currently, the lion's share of research on genomic selection has been performed in livestock breeding, where effective population size, extent of LD, breeding objectives, experimental design, and other characteristics of populations and breeding programs are quite different from those of crop species. Nevertheless, a great number of findings within this literature are very illuminating for genomic selection in crops and should be studied and built upon by crop geneticists and breeders. The application of powerful, relatively new statistical methods to the problem of high dimensional marker data for genomic selection has been nearly as important to the development of genomic selection as the creation of high-density marker platforms and greater computing power. The methods can be classified by what type of genetic architecture they try to capture.

Exploring the Realm of Possibilities: Trying to Predict Promising Crosses and Successful Offspring through Genomic Mating in Barley

“…Introducing an element of genomic prediction at the cross-selection stage (genomic mating) is much more palatable to established breeding programs than at later stages (genomic selection), such as selecting the best progeny. Indeed, using genomic selection during the actual selection phase entails major changes in resource allocation (from performing field trials to generating and processing genomic data) [11]. In contrast, using genomic prediction methods to identify promising crosses can be introduced into a breeding program with much more limited impact on how it is run.…”

“…The ability to predict complex quantitative traits solely from genotypic data (molecular markers) is a desirable goal for plant breeding programs [5]. During the last decade, much work has been done to optimize GS models in plants, but practical implementation of GS in breeding programs remains limited [6][7][8][9][10] as the shift from phenotypic to genomic selection entails major changes in resource allocation and logistics [11]. In GS, genome-wide genotypic data are used to estimate trait values for each line derived from crossing a pair of parental lines and to select the most promising lines.…”

Minor QTLs mining through the combination of GWAS and machine learning feature selection

“…Minor QTLs and genomic selection Genomic selection is a marker-assisted selection approach to enhance quantitative traits in breeding population, in which whole genome SNPs (single-nucleotide polymorphisms) markers can be used to predict breeding values. Genomic selection has been proved to increase breeding efficiency in both plant and animal breeding, such as dairy cattle, pig, rice and soybean [41]. To get an accurate prediction in genomic selection, we need a better understanding of the population of SNP makers and the contribution of each markers.…”

Minor QTLs mining through the combination of GWAS and machine learning feature selection

“…Genomic selection is a marker-assisted selection approach to enhance quantitative traits in breeding population, in which whole genome SNPs (single-nucleotide polymorphisms) markers can be used to predict breeding values. Genomic selection has been proved to increase breeding efficiency in both plant and animal breeding, such as dairy cattle, pig, rice and soybean [41]. To get an accurate prediction in genomic selection, we need a better understanding of the population of SNP makers and the contribution of each markers.…”