A genomic wide association study and genomic prediction for yield-related traits in a ‘LCP 85-384’-derived sugarcane mapping population

Authors: PHIRI, THERESA - University Of Arkansas; XIONG, HAIZHENG - University Of Arkansas; Pan, Yong-Bao; DICKSON, RYAN - University Of Arkansas; JOSHI, NEELENDRA - University Of Arkansas; ROJAS, ALEJANDRO - University Of Arkansas; SHI, AINONG - University Of Arkansas

Submitted to: Plant Breeding
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/15/2024
Publication Date: 9/8/2024
Citation: Phiri, T.M., Xiong, H., Pan, Y., Dickson, R.W., Joshi, N., Rojas, A., Shi, A. 2024. A genomic wide association study and genomic prediction for yield-related traits in a ‘LCP 85-384’-derived sugarcane mapping population. Plant Breeding. https://doi.org/10.1111/pbr.13221.
DOI: https://doi.org/10.1111/pbr.13221

Interpretive Summary: The complex sugarcane genome has limited the progress through genetics-based breeding and a conventional sugarcane breeding cycle takes 13-15 years to develop a cultivar. To expedite the development of new sugarcane varieties, marker-assisted selection (MAS) is employed for early-stage selection. The primary objectives of this study were twofold: firstly, to conduct a genome-wide association study to identify DNA markers associated with cane yield-related traits including plant height, stalk number, stalk diameter, and stalk weight, and secondly, to employ newly developed techniques called "Genomic Prediction" to evaluate the precision of these markers in predicting yield levels. The LCP 85-384 cultivar and its mapping population of 263 self-progenies were planted in two randomly replicated field plots and data on cane yield-related traits' data were collected. The mapping population was also fingerprinted with three types of DNA markers, namely, SSR, AFLP, and TRAP, to produce DNA data. A large variation was observed for each trait. Associations between traits and DNA fingerprints were analyzed using four computer-assisted models, including mixed linear model, generalized linear model, single marker regression model, and Farm CPU model. A total of 64 yield trait-associated fingerprints were identified, including 11 for stalk number, 36 for stalk weight, 21 for stalk diameter, and five for plant height. Seven fingerprints were linked to two traits and one fingerprint was linked to three traits. In "Genomic Prediction" study, five models, namely, ridge regression best linear unbiased prediction, Bayesian ridge regression, Bayesian A, Bayesian B, and Bayesian least absolute shrinkage and selection operator, were tested. The results showed that the prediction accuracy of yield trait-associated DNA fingerprints reached 0.40 for plant height, 0.36 for stalk number, 0.44 for stalk diameter, and 0.54 for stalk weight with standard errors of 0.009 to 0.012. Once verified in other sugarcane breeding populations, these DNA fingerprints will be a valuable tool to aid in the selection of yield-related traits in sugarcane improvement programs.

Technical Abstract: Sugarcane (Saccharum spp. hybrids) are complex polyploid and aneuploid interspecific hybrids with 110–130 chromosomes. A traditional sugarcane breeding cycle takes 12 years and involves multiple-year and multiple-location testing of yield-related traits. To identify molecular markers associated with yield-related traits, the LCP 85-384 cultivar and its mapping population of 263 self-progenies were planted in two randomly replicated field plots. The mapping population was genotyped with amplified fragment length polymorphism (AFLP), simple sequence repeats (SSR), and target region amplification polymorphism (TRAP) markers. Data on plant height, stalk number, stalk diameter, and stalk weight were collected. A large variation was observed for each trait. A genome-wide association study (GWAS) was conducted using mixed linear model (MLM), generalized linear model (GLM), and single marker regression (SMR) programs of TASSEL 5 and FarmCPU of GAPIT 3. A total of 64 yield trait-associated alleles were identified, including 11 for stalk number, 36 for stalk weight, 21 for stalk diameter, and five for plant height. Of the 64 alleles, seven were linked to two traits and one to three traits. Genomic prediction (GP) was also per-formed by cross-prediction with five models, namely, ridge regression best linear unbiased pre-diction (rrBLUP), Bayesian ridge regression (BRR), Bayesian A (BA), Bayesian B (BB), and Bayesian least absolute shrinkage and selection operator (BL). Prediction accuracy (r-value) reached 0.40 for plant height, 0.36 for stalk number, 0.44 for stalk diameter, and 0.54 for stalk weight with the standard errors from 0.009 to 0.012. Once verified, these markers will be a val-uable tool to aid in the selection of yield-related traits in sugarcane improvement programs.