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<p>This study aimed to identify the best model for combining traits via Best Subset Regression (BSR) and the simultaneous selection index (SSI) to maximize genetic gains in reciprocal recurrent selection (RRS) of maize multi-trait progenies. The experiment evaluated 56 and 46 interpopulation hybrids (PAB and PBA, respectively) derived from reciprocal crosses of the PA and PB populations and two checks, in an alpha-lattice design, at the Center for Scientific and Technological Development of Agriculture of the Federal University of Lavras/Brazil. Genetic parameters were estimated by Restricted Maximum Likelihood (REML) and means by Best Linear Unbiased Prediction (BLUP). The best trait combination models (M) were defined via BSR and used in SSI (Factor Analysis and Ideotype-Design: FAI-BLUP, Multi-trait Genotype-Ideotype Distance: MGIDI, and Smith-Hazel), with 20% selection intensity. The simultaneous selection efficiency (SSE %) was evaluated in comparison to direct selection (DIS) and the full model (FM), in addition to the agreement between the hybrids selected by the different selection strategies. The results showed genetic variability in both populations and combinations of BSR and SSI that maximized SSE %. The M2 + MGIDI and M3 + MGIDI models of the GY+PROL+SM and GY+PROL+SM+ASI trait combinations provided SSE % of 99.33 and 98.92 for PAB and PBA near DIS and 1.01 to 1.00 of SSE % over FM, respectively. Integrating BSR and SSI methodologies is an effective approach to guide the identification of promising progenies and boost the genetic progress of maize populations in RRS.</p>

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Optimizing multi-trait reciprocal recurrent selection in maize using best subset regression and selection indices

  • 期刊论文

  • 发表日期：

    2025年8月12日

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  • César Pedro,
  • Maria Angélica Marçola,
  • Alcides Mário Charimba,
  • Lorena Gabriela Coelho de Queiroz,
  • João Cândido de Souza

摘要

This study aimed to identify the best model for combining traits via Best Subset Regression (BSR) and the simultaneous selection index (SSI) to maximize genetic gains in reciprocal recurrent selection (RRS) of maize multi-trait progenies. The experiment evaluated 56 and 46 interpopulation hybrids (PAB and PBA, respectively) derived from reciprocal crosses of the PA and PB populations and two checks, in an alpha-lattice design, at the Center for Scientific and Technological Development of Agriculture of the Federal University of Lavras/Brazil. Genetic parameters were estimated by Restricted Maximum Likelihood (REML) and means by Best Linear Unbiased Prediction (BLUP). The best trait combination models (M) were defined via BSR and used in SSI (Factor Analysis and Ideotype-Design: FAI-BLUP, Multi-trait Genotype-Ideotype Distance: MGIDI, and Smith-Hazel), with 20% selection intensity. The simultaneous selection efficiency (SSE %) was evaluated in comparison to direct selection (DIS) and the full model (FM), in addition to the agreement between the hybrids selected by the different selection strategies. The results showed genetic variability in both populations and combinations of BSR and SSI that maximized SSE %. The M2 + MGIDI and M3 + MGIDI models of the GY+PROL+SM and GY+PROL+SM+ASI trait combinations provided SSE % of 99.33 and 98.92 for PAB and PBA near DIS and 1.01 to 1.00 of SSE % over FM, respectively. Integrating BSR and SSI methodologies is an effective approach to guide the identification of promising progenies and boost the genetic progress of maize populations in RRS.