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Sequence- vs. chip-assisted genomic selection: accurate biological information is advised.

Miguel Pérez-Enciso1,2,3, Juan C Rincón4,5, Andrés Legarra6

  • 1Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, 08193, Bellaterra, Barcelona, Spain. miguel.perez@uab.es.

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PubMed
Summary

Whole-genome sequencing data offers limited improvement over high-density SNP arrays for genetic evaluations due to diminishing returns. Accurate prior knowledge of causal variants is key for significant gains in selection response.

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Area of Science:

  • Animal and plant breeding
  • Genomics
  • Quantitative genetics

Background:

  • Next-generation sequencing (NGS) enables whole-genome data use in genetic evaluations, sparking interest in breeding.
  • Investigated if complete or partial sequence data enhances SNP array-based selection strategies.

Purpose of the Study:

  • To compare the effectiveness of SNP arrays versus whole-genome sequence data for genetic evaluations.
  • To assess the impact of SNP density and accuracy on selection strategies.

Main Methods:

  • Simulated quantitative trait nucleotides (QTN) within defined gene regions.
  • Employed a mixed coalescence-gene dropping approach for simulations.
  • Utilized GBLUP (Genomic Best Linear Unbiased Prediction) for genetic evaluation and cross-validation.

Main Results:

  • Diminishing returns observed with increasing SNP density.
  • Whole-genome sequence data showed only a marginal ~4% advantage over high-density arrays.
  • Including SNPs within causal genes significantly increased accuracy (~40%) but was sensitive to correct gene identification.

Conclusions:

  • Increasing SNP density yields diminishing returns for genetic prediction accuracy.
  • Whole-genome sequence data may not substantially improve selection response over high-density genotyping without prior functional information.
  • Accurate prior knowledge of SNP functionality is crucial for maximizing selection gains.