Hunting trait-specific DNA markers for sugarcane breeding

Authors: Pan, Yong Bao; XIONG, HAIZHENG - University Of Arkansas; PHIRI, THERESA - University Of Arkansas; SONG, JINJIN - Fujian Agricultural & Forestry University; MING, RAY - Fujian Agricultural & Forestry University; SHI, AINONG - University Of Arkansas

Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: 12/31/2023
Publication Date: 1/11/2024
Citation: Pan, Y., Xiong, H., Phiri, T.M., Song, J., Ming, R., Shi, A. 2024. Hunting trait-specific DNA markers for sugarcane breeding. Plant and Animal Genome Conference. abstract.

Interpretive Summary: Sugarcane varieties (Saccharum spp. hybrids) are interspecific polyploidy and aneuploidy hybrids with 110-130 chromosomes. A conventional breeding cycle takes 12 years and is extremely laborious during visual selection and multi-year and multi-location evaluations of sugar yield/quality and biotic/abiotic stress responses. To develop trait-specific DNA markers for marker-assisted selection, we have conducted genome-wide association (GWAS) and genomic prediction (GP) studies using a meticulously validated self-progeny mapping population of the cultivar LCP 85-384. Phenotyping data [plant height, stalk number, stalk weight, stalk diameter, fiber content, total sugar (Brix), polarization associated with sucrose (Pol), theoretical recoverable sugar (TRS), and purity] were collected from replicated plant cane and first ratoon crops of the self-progenies were analyzed. Genotyping was performed using amplified fragment length polymorphism (AFLP), simple sequence repeats (SSR), target region amplification polymorphism (TRAP), and restriction site-associated DNA sequencing (RADseq)-based single nucleotide polymorphism (SNP). The GWAS was conducted via a spectrum of statistical methodologies, including mixed linear model (MLM), generalized linear model (GLM), and single marker regression (SMR) algorithms of TASSEL 5 and FarmCPU of GAPIT 3. Notably, the exhaustive analyses culminated in the identification of 77 significant markers, including 11 markers associated with stalk number, 9 with stalk weight, 21 with stalk diameter, 5 with plant height, 13 with fiber content, 9 with sucrose content, 1 with brix, 4 with Pol, and 4 with TRS. Furthermore, to assess the predictive power of these markers, GP was conducted using 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) models. The predictive accuracies ranged from 0.36 to 0.54 for plant height and stalk traits, 0.55 to 0.58 for fiber content, and 0.54 to 0.57 for sucrose content. The integrated approach combining GWAS and GP represents a valuable tool for enhancing sugarcane breeding and significant advancements in sugarcane improvement efforts.

Technical Abstract: Sugarcane varieties (Saccharum spp. hybrids) are interspecific polyploid and aneuploid hybrids with 110-130 chromosomes. A conventional breeding cycle takes 12 years and is extremely laborious due to visual selection and multi-year and multi-location evaluations of sugar yield/quality and biotic/abiotic stress responses. To develop trait-specific DNA markers for marker-assisted selection, we have conducted genome-wide association (GWAS) and genomic prediction (GP) studies using a meticulously validated self-progeny mapping population of the cultivar LCP 85-384. Phenotyping data [plant height, stalk number, stalk weight, stalk diameter, fiber content, total sugar (Brix), polarization associated with sucrose (Pol), theoretical recoverable sugar (TRS), and purity] were collected from replicated plant cane and first ratoon crops of the self-progenies were analyzed. Genotyping was performed using amplified fragment length polymorphism (AFLP), simple sequence repeats (SSR), target region amplification polymorphism (TRAP), and restriction site-associated DNA sequencing (RADseq)-based single nucleotide polymorphism (SNP). The GWAS was conducted via a spectrum of statistical methodologies, including mixed linear model (MLM), generalized linear model (GLM), and single marker regression (SMR) algorithms of TASSEL 5 and FarmCPU of GAPIT 3. Notably, the exhaustive analyses culminated in the identification of 77 significant markers, including 11 markers associated with stalk number, 9 with stalk weight, 21 with stalk diameter, 5 with plant height, 13 with fiber content, 9 with sucrose content, 1 with brix, 4 with Pol, and 4 with TRS. Furthermore, to assess the predictive power of these markers, GP was conducted using 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) models. The predictive accuracies ranged from 0.36 to 0.54 for plant height and stalk traits, 0.55 to 0.58 for fiber content, and 0.54 to 0.57 for sucrose content. The integrated approach combining GWAS and GP represents a valuable tool for enhancing sugarcane breeding and significant advancements in sugarcane improvement efforts.