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ARS Home » Southeast Area » Florence, South Carolina » Coastal Plain Soil, Water and Plant Conservation Research » Research » Publications at this Location » Publication #393292

Research Project: Effective Cotton Genetics and Management Practices for Improved Cotton Quality and Production

Location: Coastal Plain Soil, Water and Plant Conservation Research

Title: Outlook for implementation of genomics-based selection in public cotton breeding programs

item BILLINGS, GRANT - North Carolina State University
item JONES, MICHAEL - Clemson University
item RUSTGI, SACHIN - Clemson University
item BRIDGES, WILLIAM - Clemson University
item Holland, Jim - Jim
item Hulse-Kemp, Amanda
item Campbell, Benjamin - Todd

Submitted to: Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/16/2022
Publication Date: 5/29/2022
Citation: Billings, G.T., Jones, M.A., Rustgi, S., Bridges, W.C., Holland, J.B., Hulse-Kemp, A.M., Campbell, B.T. 2022. Outlook for implementation of genomics-based selection in public cotton breeding programs. Plants. 11(11).

Interpretive Summary: Cotton breeders have faced many challenges historically. Public and private investment has stimulated the development of genetics tools that allows cotton breeders to inspect individual plants at the DNA level. However, integration of the tools and concepts behind them into selection for improved cotton characteristics is lacking. We leveraged historical datasets across the southeast and mid-south USA originating from a long-term USDA-ARS cotton breeding program in Florence, SC, together with state of the art CottonSNP63K array genotyping technology, to perform a technical evaluation of statistical methodology for accelerating the rate and quality of cotton breeding. Overall, we found that cotton breeders can use genomic selection to improve the cotton fiber quality, but seed protein/oil and yield characteristics are going to be more challenging. Using genomics to improve the characteristics of a plant depends on how significant the genetic portion of the trait is (heritability), the number of observations you have from field trials, and the cost and efficiency to accumulate the necessary genetic data. We emphasize the need for the community to come together and develop lower cost, higher-throughput genotyping methods and integrate data sharing between breeding programs. These two strategies will enable breeders to expand genomic selection to a wider range of traits critical for the competitiveness of the US cotton crop and use of highly desirable natural fibers for consumer needs.

Technical 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 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 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 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 of 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.