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A nested mixture model for genomic prediction using whole-genome SNP genotypes.

Jian Zeng1, Dorian Garrick2, Jack Dekkers3

  • 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

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|March 22, 2018
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Summary

A new nested mixture model, BayesN, improves genomic prediction accuracy by accounting for linked single nucleotide polymorphism (SNP) effects. This method is especially effective for traits with rare quantitative trait loci (QTL) and offers computational efficiency.

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genomic prediction models typically assume independence of single nucleotide polymorphism (SNP) effects.
  • Ignoring the local dependence of SNP effects can lead to inaccurate genetic merit predictions, particularly for markers flanking quantitative trait loci (QTL).

Purpose of the Study:

  • To develop and evaluate a novel nested mixture model, BayesN, that accounts for the dependence of SNP effects in genomic prediction.
  • To compare the performance of BayesN against existing models like BayesB and antiBayesB in terms of prediction accuracy and computational efficiency.

Main Methods:

  • A nested mixture model (BayesN) was developed, where SNP effects within genomic windows follow a mixture distribution.
  • Simulations using Angus cattle genotypes were performed, with scenarios involving common and rare QTL.
  • The performance of BayesN was evaluated using SNP panels of 50k and 600k, with comparisons against BayesB and a modified antiBayesB.

Main Results:

  • BayesN improved prediction accuracy by up to 2.0% (50k SNPs) and 7.0% (600k SNPs) compared to BayesB and antiBayesB.
  • The most significant improvements were observed in scenarios with rare QTL.
  • BayesN reduced computing time by up to 60% (50k SNPs) and 75% (600k SNPs).

Conclusions:

  • BayesN is an accurate and computationally efficient method for genomic prediction using whole-genome SNPs.
  • The model shows particular promise for traits influenced by rare QTL.
  • BayesN offers a parsimonious alternative to existing antedependence models.