| Autor: |
Billings GT; Bioinformatics Graduate Program, North Carolina State University, Raleigh, NC 27695, USA.; Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA., Jones MA; Pee Dee Research and Education Center, Clemson University, Florence, SC 29506, USA., Rustgi S; Pee Dee Research and Education Center, Clemson University, Florence, SC 29506, USA., Bridges WC Jr; Department of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA., Holland JB; Bioinformatics Graduate Program, North Carolina State University, Raleigh, NC 27695, USA.; Plant Sciences Research Unit, The Agricultural Research Service of U.S. Department of Agriculture, Raleigh, NC 27695, USA., Hulse-Kemp AM; Bioinformatics Graduate Program, North Carolina State University, Raleigh, NC 27695, USA.; Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA.; Genomics and Bioinformatics Research Unit, The Agricultural Research Service of U.S. Department of Agriculture, Raleigh, NC 27965, USA., Campbell BT; Coastal Plains Soil, Water, and Plant Research Center, The Agricultural Research Service of U.S. Department of Agriculture, Florence, SC 29501, USA. |
| Abstrakt: |
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. |
