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ARS Home » Southeast Area » Houma, Louisiana » Sugarcane Research » Research » Publications at this Location » Publication #401568

Research Project: Genetic Improvement of Sugarcane for Adaptation to Temperate Climates

Location: Sugarcane Research

Title: A genome-wide association study and genomic prediction for fiber and sucrose contents in a mapping population of LCP 85-384 sugarcane

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

Submitted to: Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/21/2023
Publication Date: 2/24/2023
Citation: Xiong, H., Chen, Y.-B, Pan, Y., Shi, A. 2023. A genome-wide association study and genomic prediction for fiber and sucrose contents in a mapping population of LCP 85-384 sugarcane. Plants. 12(5):1041. https://doi.org/10.3390/plants12051041.
DOI: https://doi.org/10.3390/plants12051041

Interpretive Summary: Fiber and sucrose contents are the two most important traits in sugarcane breeding. A favorable partition between sugar and fiber is essential for a good sugarcane cultivar. However, it is very difficult to balance the two traits in a sugarcane plant due to its complex and very large size genome and requires the breeders to conduct multiple-year and multiple-location evaluations. The objective of this study was 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. To do this, a superior sugarcane cultivar LCP 85-384 and its 237 self progenies were planted in two replicated randomized field experiments. Fiber and sucrose content data were collected from both plant cane and first ratoon crops. A total of 1,310 polymorphic DNA markers from an enriched genetic linkage map of LCP 85-384 and the fiber and sucrose content data were involved in the GWAS with several statistical models, including three models of TASSEL 5, single marker regression (SMR), general linear model (GLM), mixed linear model (MLM), and the fixed and random model circulating probability unification (FarmCPU) of R package. The results showed that 13 and 9 DNA markers were associated with fiber and sucrose contents, respectively. On the other hand, the GP was performed by cross-prediction with five statistical models, namely, 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. Upon validation, these fiber or sucrose content-associated DNA markers can be used to select superior sugarcane plants with a favorably balanced fiber and sugar yields via genomic selection.

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 (MAS) could 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 between 1997 and 2007. The GWAS was performed 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) of R package. The results showed that 13 and 9 markers were associated with fiber and sucrose contents, respectively. The GP was per-formed 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. Upon validation, these markers can be applied in MAS and genomic selection (GS) to select superior sugarcane with good fiber and high sucrose contents.