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<p>Soybean drought tolerance relies on root traits. Genomic prediction (GP) offers a non-destructive alternative to laborious phenotyping. This study explores a multi-kernel GP approach for predicting soybean root traits by also incorporating easily measurable non-destructive aerial traits as secondary covariates. The main idea is to leverage the correlation between the aerial (visible) and root traits (not visible). In addition, we contrasted the predictive ability (PA) shown by the multi-kernel approach to those obtained from single-trait and multi-trait genomic prediction models. Data comprising 100 cultivars evaluated in two years and genotyped for 5,403 single-nucleotide polymorphism markers was analyzed. To comprehensively assess model performance, two cross-validation schemes were considered (CV1 and CV0). CV1 used a five-fold approach, and CV0 used a time-lagged cross-validation (i.e., data from years 1 and 2 were used for training and testing, respectively). Aerial traits added as covariates enhanced the GP predictive ability for all the traits and cross-validation (CV) schemes, outperforming single- and multi-trait models without this information. The inclusion of the interaction term between markers and secondary traits did not improved PA compared to the main effects models.</p>

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Assisted genomic prediction models for soybean root traits using secondary aerial phenotypes

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • Moyses Nascimento,
  • Ana Carolina Campana Nascimento,
  • Vitor Seiti Sagae,
  • Felipe Lopes da Silva,
  • Diego Jarquin

摘要

Soybean drought tolerance relies on root traits. Genomic prediction (GP) offers a non-destructive alternative to laborious phenotyping. This study explores a multi-kernel GP approach for predicting soybean root traits by also incorporating easily measurable non-destructive aerial traits as secondary covariates. The main idea is to leverage the correlation between the aerial (visible) and root traits (not visible). In addition, we contrasted the predictive ability (PA) shown by the multi-kernel approach to those obtained from single-trait and multi-trait genomic prediction models. Data comprising 100 cultivars evaluated in two years and genotyped for 5,403 single-nucleotide polymorphism markers was analyzed. To comprehensively assess model performance, two cross-validation schemes were considered (CV1 and CV0). CV1 used a five-fold approach, and CV0 used a time-lagged cross-validation (i.e., data from years 1 and 2 were used for training and testing, respectively). Aerial traits added as covariates enhanced the GP predictive ability for all the traits and cross-validation (CV) schemes, outperforming single- and multi-trait models without this information. The inclusion of the interaction term between markers and secondary traits did not improved PA compared to the main effects models.