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<p>Genomic prediction has the potential to improve genetic progress in sugarcane. However, genomic information may not be available for all phenotyped individuals. The single-step genomic best linear unbiased prediction (ssGBLUP) method can provide an alternative genetic evaluation approach by combining nongenotyped and genotyped individuals. Here, we investigated ssGBLUP for genetic evaluation of a sugarcane population at an early selection stage. The pedigree contained 4450 individuals, of which 3704 were phenotyped and nongenotyped, and 377 were phenotyped and genotyped. We evaluated five models: a pedigree-based (PBLUP-2) and a genomic-based (GBLUP) model considering only genotyped individuals; a pedigree-based model considering all phenotyped individuals (PBLUP-1); and two ssGBLUP models considering all phenotyped individuals, one using the standard genomic relationship matrix (ssGBLUP-1) and one adjusted genomic relationship matrix (ssGBLUP-2). The models were evaluated in two cross-validation (CV) schemes: validation using genotyped individuals (CV-G) and validation using non-genotyped individuals (CV-NG). In CV-G, we found that ssGBLUP and PBLUP had overall superior performance compared to GBLUP. The two ssGBLUP models evaluated also gave similar performance. For stalk number and stalk diameter, PBLUP showed higher prediction accuracy than ssGBLUP. For CV-NG, we found no significant differences in performance between PBLUP and ssGBLUP models. Our results suggest that genetic evaluation using ssGBLUP models may be an alternative approach for sugarcane. Our results also showed that models including only pedigree information gave relatively high prediction accuracies, suggesting that pedigrees are an important potential source of genetic information, particularly for sugarcane and other crop species with complex polyploid genomes.</p>

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Single-step genomic best linear unbiased predictions of sugarcane genotype performance

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Mateus Teles Vital Gonçalves,
  • Kaio Olimpio das Graças Dias,
  • Guilherme da Silva Pereira,
  • Pedro Henrique Silva Ferreira,
  • Márcio Henrique Pereira Barbosa,
  • Gota Morota,
  • Luiz Alexandre Peternelli

摘要

Genomic prediction has the potential to improve genetic progress in sugarcane. However, genomic information may not be available for all phenotyped individuals. The single-step genomic best linear unbiased prediction (ssGBLUP) method can provide an alternative genetic evaluation approach by combining nongenotyped and genotyped individuals. Here, we investigated ssGBLUP for genetic evaluation of a sugarcane population at an early selection stage. The pedigree contained 4450 individuals, of which 3704 were phenotyped and nongenotyped, and 377 were phenotyped and genotyped. We evaluated five models: a pedigree-based (PBLUP-2) and a genomic-based (GBLUP) model considering only genotyped individuals; a pedigree-based model considering all phenotyped individuals (PBLUP-1); and two ssGBLUP models considering all phenotyped individuals, one using the standard genomic relationship matrix (ssGBLUP-1) and one adjusted genomic relationship matrix (ssGBLUP-2). The models were evaluated in two cross-validation (CV) schemes: validation using genotyped individuals (CV-G) and validation using non-genotyped individuals (CV-NG). In CV-G, we found that ssGBLUP and PBLUP had overall superior performance compared to GBLUP. The two ssGBLUP models evaluated also gave similar performance. For stalk number and stalk diameter, PBLUP showed higher prediction accuracy than ssGBLUP. For CV-NG, we found no significant differences in performance between PBLUP and ssGBLUP models. Our results suggest that genetic evaluation using ssGBLUP models may be an alternative approach for sugarcane. Our results also showed that models including only pedigree information gave relatively high prediction accuracies, suggesting that pedigrees are an important potential source of genetic information, particularly for sugarcane and other crop species with complex polyploid genomes.