Evaluation of genomic selection methods for predicting fiber quality traits in upland cotton

Authors: Islam, Md; Fang, David; Jenkins, Johnie; GUO, JIA - University Of Florida; McCarty, Jack; JONES, DON - Cotton, Inc

Submitted to: Molecular Genetics and Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/29/2019
Publication Date: 8/31/2019
Citation: Islam, M.S., Fang, D.D., Jenkins, J.N., Guo, J., Mccarty Jr, J.C., Jones, D.C. 2019. Evaluation of genomic selection methods for predicting fiber quality traits in upland cotton. Molecular Genetics and Genomics. 295:67-69. https://doi.org/10.1007/s00438-019-01599-z.
DOI: https://doi.org/10.1007/s00438-019-01599-z

Interpretive Summary: The use of genomic selection (GS) has stimulated a new way to utilize molecular markers in breeding for complex traits such as yield or fiber quality in the absence of phenotypic data. Numerous statistical models and approaches have been proposed in several crops; however, no comparative analysis is available so far to identify the most promising one in Upland cotton. The objective of this study is to experimentally evaluate the potential value of genomic selection in Upland Cotton breeding. Six fiber quality traits were obtained from three years’ replicated field trials in Starkville, MS. Genotyping-by-Sequencing based genotyping was performed using 550 recombinant inbred lines of the multi-parent advanced generation inter-cross population, and 6,292 molecular markers were used for the GS analysis. Five GS analysis methods were compared. The average heritability (h2) ranged from 0.38 to 0.88 for all tested traits across the three years evaluated. BayesB predicted the highest accuracies among the five GS methods tested. The prediction ability (PA) and prediction accuracy (PACC) varied widely across three years for all tested traits and the highest PA and PACC were 0.65, and 0.69, respectively at 2010 for fiber elongation. Marker density and training population size appeared to be very important factor for PA and PACC in GS. Results indicated that BayesB-based GS could predict genomic estimated breeding value efficiently in Upland cotton fiber quality attributes and has great potential utility in breeding by reducing cost and time.

Technical Abstract: The use of genomic selection (GS) has stimulated a new way to utilize molecular markers in breeding for complex traits in the absence of phenotypic data. Numerous statistical models and approaches have been proposed in several crops; however, no comparative analysis is available so far to identify the most promising one in Upland cotton. The objective of this study was to experimentally evaluate the potential value of genomic selection in Upland Cotton breeding. Six fiber quality traits were obtained from three years’ replicated field trials in Starkville, MS. Genotyping-by-Sequencing based genotyping was performed using 550 recombinant inbred lines of the multi-parent advanced generation inter-cross population, and 6,292 molecular markers were used for the GS analysis. Several methods were compared, including Genomic BLUP (GBLUP), Ridge Regression BLUP (rrBLUP), BayesB, Bayesian LASSO, and Reproducing Kernel Hilbert Spaces (RKHS). The average heritability (h2) ranged from 0.38 to 0.88 for all tested traits across the three years evaluated. BayesB predicted the highest accuracies among the five GS methods tested. The prediction ability (PA) and prediction accuracy (PACC) varied widely across three years for all tested traits and the highest PA and PACC were 0.65, and 0.69, respectively at 2010 for fiber elongation. Marker density and training population size appeared to be very important factor for PA and PACC in GS. Results indicated that BayesB-based GS could predict genomic estimated breeding value efficiently in Upland cotton fiber quality attributes and has great potential utility in breeding by reducing cost and time.