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ARS Home » Southeast Area » Houma, Louisiana » Sugarcane Research » Research » Publications at this Location » Publication #403662
Research Project: Genetic Improvement of Sugarcane for Adaptation to Temperate Climates

Location: Sugarcane Research

Title: Genomic prediction of sucrose and fiber contents in a mapping population of LCP 85-384 based on a genome-wide association study

Author
item Pan, Yong Bao
item XIONG, HAIZHENG - University Of Arkansas
item CHEN, YILING - University Of Arkansas
item SHI, AINONG - University Of Arkansas

Submitted to: American Society of Sugarcane Technologist
Publication Type: Abstract Only
Publication Acceptance Date: 4/15/2023
Publication Date: 6/11/2023
Citation: Pan, Y., Xiong, H., Chen, Y., Shi, A. 2023. Genomic prediction of sucrose and fiber contents in a mapping population of LCP 85-384 based on a genome-wide association study. American Society of Sugarcane Technologist. abstract.

Interpretive Summary:

Technical Abstract: Sugarcane (Saccharum spp. hybrids) is an economically important crop for both sugar and biofuel industries. Fiber and sucrose contents are the two most critical quantitative traits in sugarcane breeding that require multiple-year and multiple-location evaluations. Marker-assisted selection can significantly reduce the time and cost in developing new sugarcane varieties. The objectives of this study were to conduct a genome-wide association study (GWAS) to identify DNA markers associated with fiber and sucrose contents and to perform genomic prediction (GP) for the two traits. Fiber and sucrose data were collected from 237 self-pollinated progenies of LCP 85-384, the most popular Louisiana sugarcane cultivar during 1999 to 2007. The GWAS was per-formed using 1,310 polymorphic DNA marker alleles with three models of TASSEL 5, single marker regression (SMR), general linear model (GLM), and mixed linear model (MLM), and the fixed and random model circulating probability unification (FarmCPU) by GAPIT 3 of R package. The results showed that 13 and 9 markers were associated with fiber and sucrose contents, respectively. The GP was performed by cross-prediction with five models, ridge regression best linear unbiased prediction (rrBLUP), Bayesian ridge regression (BRR), Bayesian A (BA), Bayesian B (BB), and Bayesian least absolute shrinkage and selection operator (BL). The accuracy of GP varied from 55.8% to 58.9% for fiber content and 54.6% to 57.2% for sucrose content by GWAS derived alleles. Upon validation, these markers can be applied in marker-assisted genomic selection (GS) to select superior sugarcane varieties with improved fiber and sucrose traits.