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PyMSQ: a Python package for fast Mendelian sampling (co)variance and haplotype-based similarity in genomic selection.

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Genomic selection can reduce genetic diversity. PyMSQ software estimates Mendelian sampling variance (MSV) and covariance (MSC), alongside a novel haplotype similarity measure, to preserve diversity and enable sustainable breeding strategies.

Keywords:
Gametic varianceGenetic diversity managementHaplotype similarity matricesMendelian sampling varianceOptimal mating decisions

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Area of Science:

  • Quantitative genetics
  • Animal breeding
  • Bioinformatics

Background:

  • Genomic selection (GS) accelerates genetic gain but risks reduced haplotype diversity and increased inbreeding.
  • Mendelian sampling variance (MSV) and covariance (MSC) can mitigate genetic variation loss by utilizing within-family segregation.
  • Haplotype-based similarity measures offer direct control over haplotype diversity.

Purpose of the Study:

  • Introduce PyMSQ, an open-source Python package for estimating MSV and MSC.
  • Implement a novel haplotype-based similarity matrix for parental Mendelian sampling terms.
  • Enhance genomic selection strategies by balancing genetic gain with diversity preservation.

Main Methods:

  • Developed a matrix-based approach for computing MSV and MSC across various contexts (single-trait, multi-trait, zygotic).
  • Created a haplotype-based similarity matrix quantifying shared heterozygous segments.
  • Integrated optimized scientific libraries for computational efficiency.

Main Results:

  • PyMSQ computes MSV and MSC significantly faster (up to 332-fold) than existing tools like gamevar, maintaining numerical accuracy.
  • Demonstrated the utility of MSV, MSC, and the novel similarity measure on a Holstein-Friesian dataset.
  • The new similarity measure complements standard genomic relationship matrices by focusing on heterozygous segments.

Conclusions:

  • PyMSQ facilitates the practical application of MSV, MSC, and haplotype similarity metrics in breeding programs.
  • Enables breeders to implement haplotype diversity constraints alongside optimal contribution selection.
  • Supports the development of more sustainable genomic selection strategies by preserving key haplotypic segments.