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<p>This study aimed to investigate the prediction accuracy of the probability that alleles at unobserved loci are identity-by-state (IBS) using genome-based measures based on observed single nucleotide polymorphisms (SNPs). We performed a simulation analysis assumed to represent a cattle population with simulated and real SNP genotypes. The genome-based measures were based on the inbreeding coefficients in an individual and the additive relationship coefficients between two individuals. Reference values were defined as the probability that the alleles at unobserved SNPs were IBS. Reference values were predicted using both pedigree-based and genome-based measures with tens of thousands of SNPs. Prediction accuracy was calculated as the correlation coefficient between reference and predicted values. Our results showed that the inbreeding and additive relationship coefficients based on SNP-by-SNP with an allele frequency fixed at 0.5 and the coefficients based on the homozygous-segment with homozygous by descent and with run of homozygosity &gt; 4 Mbp long demonstrated consistent high prediction accuracy in both simulated and real cattle populations. Our results also showed that the correlation coefficients of these measures were higher than those of pedigree-based measures. Our results indicate that genome-based measures utilizing observed SNPs can offer a more accurate prediction of IBS relationships at unobserved loci than pedigree-based measures in cattle populations.</p>

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Probabilities of two alleles being identity by state at unobserved loci predicted by observed loci in cattle populations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月5日

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  • Rintaro Nagai,
  • Takeshi Honda,
  • Masahiro Satoh,
  • Yoshinobu Uemoto

摘要

This study aimed to investigate the prediction accuracy of the probability that alleles at unobserved loci are identity-by-state (IBS) using genome-based measures based on observed single nucleotide polymorphisms (SNPs). We performed a simulation analysis assumed to represent a cattle population with simulated and real SNP genotypes. The genome-based measures were based on the inbreeding coefficients in an individual and the additive relationship coefficients between two individuals. Reference values were defined as the probability that the alleles at unobserved SNPs were IBS. Reference values were predicted using both pedigree-based and genome-based measures with tens of thousands of SNPs. Prediction accuracy was calculated as the correlation coefficient between reference and predicted values. Our results showed that the inbreeding and additive relationship coefficients based on SNP-by-SNP with an allele frequency fixed at 0.5 and the coefficients based on the homozygous-segment with homozygous by descent and with run of homozygosity > 4 Mbp long demonstrated consistent high prediction accuracy in both simulated and real cattle populations. Our results also showed that the correlation coefficients of these measures were higher than those of pedigree-based measures. Our results indicate that genome-based measures utilizing observed SNPs can offer a more accurate prediction of IBS relationships at unobserved loci than pedigree-based measures in cattle populations.