Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

Abstract

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

Morphological, Physiological, and Genetic Responses to Salt Stress in Alfalfa: A Review

“…As salt-tolerant traits are genetically complex and multi-gene controlled, the development of genome-wide markers might be useful for conducting a genomic selection based on the predictive breeding values of genotypes. Genomic selection can enhance the genetic gain and reduce the breeding cycle when it increases the selection accuracy [107,108]. The trait prediction accuracy remains generally low in current genomic selection models, even with the aid of dense, genome-wide single nucleotide polymorphism (SNP) markers [108].…”

“…Genomic selection can enhance the genetic gain and reduce the breeding cycle when it increases the selection accuracy [107,108]. The trait prediction accuracy remains generally low in current genomic selection models, even with the aid of dense, genome-wide single nucleotide polymorphism (SNP) markers [108]. As demonstrated by Fu et al [108], salt tolerance-associated SNP markers in alfalfa could be explored to achieve a more accurate trait-specific prediction in genomic selection.…”

“…The trait prediction accuracy remains generally low in current genomic selection models, even with the aid of dense, genome-wide single nucleotide polymorphism (SNP) markers [108]. As demonstrated by Fu et al [108], salt tolerance-associated SNP markers in alfalfa could be explored to achieve a more accurate trait-specific prediction in genomic selection. This is feasible, as the application of the RNA-Seq technique in alfalfa salinity tolerance studies is relatively common and a large amount of genomic information is already available.…”

Advancing Bromegrass Breeding Through Imaging Phenotyping and Genomic Selection: A Review

“…GBS can provide the high-density markers typically needed for GS based complex traits selection in forage species (Hayes et al, 2013;Baral et al, 2018). Thus, GS can enhance the rate of genetic gain by reducing the length of a breeding cycle and increasing selection accuracy (Fu et al, 2017).…”

“…Genomic selection (GS) is a new breeding tool to enhance the rate of genetic gain by reducing the length of the breeding cycle and increasing selection accuracy (Meuwissen et al, 2001). GS is based on genome-wide molecular markers to predict genetic value of individual plants for a trait of interest, and has been effective in the prediction of the genetically complex traits (Fu et al, 2017) such as yield and abiotic stress tolerance. Recent advances in high-throughput imaging phenotyping tools along with low cost genetic technologies provide crop breeders with the potential to speed up the genetic gain in forage yield (Fahlgren et al, 2015).…”