Swapnil R. Takale,
- Assistant Professor, Department of Electronics & Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India, Maharashtra, India
Abstract
Before genome-wide molecular markers became affordable, plant and animal breeding relied on pedigree-based prediction (P-BLUP), using the expected additive relationship matrix (A) derived from recorded ancestry. Genomic selection replaced or augmented this with a marker-derived genomic relationship matrix (G; GBLUP), and single-step methods combining A and G are now standard when only a subset of a breeding population is genotyped. It is less clear whether pedigree information retains any predictive value once every individual in a population is already densely genotyped, since realized (marker-based) relationships are, in principle, a more accurate estimate of actual shared ancestry than the expected relationships pedigree records provide. We tested this directly on the CIMMYT wheat dataset (599 lines, 1,279 DArT markers, grain yield in four environments, all lines genotyped), comparing pedigree-only prediction (kernel ridge regression with the pedigree relationship matrix A), marker-only prediction (GBLUP; kernel ridge regression with the VanRaden genomic relationship matrix G), and a naive equal-weight combined kernel, using the identical 5-fold cross-validation partitions as our companion marker-based study for direct comparability. We first verified analytically and numerically that kernel ridge regression with kernel G reproduces ridge regression on standardized markers exactly under the appropriate penalty rescaling (in-sample prediction correlation 1.000, maximum absolute difference 2.4 x 10⁻¹⁴), confirming our kernel ridge implementation is a faithful computational realization of GBLUP rather than an approximation. GBLUP substantially outperformed pedigree-only prediction (mean predictive ability r = 0.449 versus r = 0.330 across four environments), consistent with genomic relationships more accurately capturing realized rather than merely expected relatedness. The naive combined kernel produced a genuinely mixed result: it underperformed GBLUP alone in two of four environments and outperformed it in the other two, yielding a marginal average improvement (r = 0.455) that, with only four environments, we cannot distinguish from noise. We report this ambiguity directly rather than selectively emphasizing the two environments favoring combination, and discuss why, from quantitative genetics theory, pedigree information is expected to add little once dense genome-wide genotyping is already available for the full population, in contrast to single-step settings where only some individuals are genotyped.
Keywords: Genomic selection, GBLUP, pedigree, kernel ridge regression, VanRaden relationship matrix, wheat; grain yield, computational genomics
[This article belongs to Research and Reviews : Journal of Computational Biology ]
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Research and Reviews : Journal of Computational Biology
| Volume | 15 | |
| Issue | 02 | |
| Received | 13/07/2026 | |
| Accepted | 08/08/2026 | |
| Published | 20/08/2026 | |
| Publication Time | 38 Days |