Outlook for Implementation of Genomics-Based Selection in Public Cotton Breeding Programs

Abstract

Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.

A first look at the ability to use genomic prediction for improving the ratooning ability of sugarcane

“…It is reported that incorporating major gene or QTL in the GS models as fixed covariates could increase the prediction accuracy most of the time ( Bernardo, 2014 ; Islam et al., 2022a ). However, it is not true all the time: it has also been reported that prediction accuracy was decreased and model bias was increased by incorporating the GWAS peak marker as a fixed effect in the GS models depending on the genetic architecture of the trait ( Rice and Lipka, 2019 ; Billings et al., 2022 ).…”