Scholarlink

<p>We evaluated the predictive performance of machine learning-augmented single- and multi-trait single-step genomic best linear prediction (ssGBLUP) models for 30 complex traits spanning productivity, chemical defense, and climate adaptability in white spruce (<i>Picea glauca</i>) from Alberta, Canada, genotyped at 211,061 SNPs. We extend a novel integration of the ssGBLUP with non-linear kernels for forest tree improvement. Using the conventional ssGBLUP model with VanRaden’s linear genomic relationship matrix as baseline, we compared two non-linear extensions: an averaged Gaussian kernel (GK) with trait-specific bandwidths and an arc-cosine kernel (AK) with 1–20 layers (depth). To relate prediction gains to genetic architecture, we also fitted an extended GBLUP model explicitly including additive, dominance, and epistatic (additive × additive and additive × dominance) effects. Additive effects predominated in 21 traits, dominance contributed &gt; 50% of variance in several chemical-defense traits, and epistasis explained ~ 25% in key physiological traits. Predictive performance varied by kernel type and trait architecture. AK achieved the highest accuracy in 29 traits, improving prediction by up to 27% (predictive differences up to ~ 0.10), particularly for traits with more complex genetic architectures. GK yielded modest gains (&lt; 7%; predictive differences &lt; 0.03), while linear G kernel remained competitive for core productivity traits. In multi-trait models, non-linear kernels performed similar to the linear alternatives. Notably, low-heritability traits such as drought resistance improved from 0.446-0.517 to 0.461-0.547 accuracy with multi-trait integration. These findings suggest that matching kernel complexity to genetic architecture may enhance genomic prediction and highlight the potential of non-linear kernels, particularly AK, in forest tree breeding.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrating machine learning with single-step GBLUP for enhanced genomic prediction in white spruce

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

查看全文
  • Eduardo Pablo Cappa,
  • Charles Chen,
  • Andy Benowicz,
  • Barb R. Thomas,
  • Yousry A. El-Kassaby

摘要

We evaluated the predictive performance of machine learning-augmented single- and multi-trait single-step genomic best linear prediction (ssGBLUP) models for 30 complex traits spanning productivity, chemical defense, and climate adaptability in white spruce (Picea glauca) from Alberta, Canada, genotyped at 211,061 SNPs. We extend a novel integration of the ssGBLUP with non-linear kernels for forest tree improvement. Using the conventional ssGBLUP model with VanRaden’s linear genomic relationship matrix as baseline, we compared two non-linear extensions: an averaged Gaussian kernel (GK) with trait-specific bandwidths and an arc-cosine kernel (AK) with 1–20 layers (depth). To relate prediction gains to genetic architecture, we also fitted an extended GBLUP model explicitly including additive, dominance, and epistatic (additive × additive and additive × dominance) effects. Additive effects predominated in 21 traits, dominance contributed > 50% of variance in several chemical-defense traits, and epistasis explained ~ 25% in key physiological traits. Predictive performance varied by kernel type and trait architecture. AK achieved the highest accuracy in 29 traits, improving prediction by up to 27% (predictive differences up to ~ 0.10), particularly for traits with more complex genetic architectures. GK yielded modest gains (< 7%; predictive differences < 0.03), while linear G kernel remained competitive for core productivity traits. In multi-trait models, non-linear kernels performed similar to the linear alternatives. Notably, low-heritability traits such as drought resistance improved from 0.446-0.517 to 0.461-0.547 accuracy with multi-trait integration. These findings suggest that matching kernel complexity to genetic architecture may enhance genomic prediction and highlight the potential of non-linear kernels, particularly AK, in forest tree breeding.