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Genomic prediction accuracy improves with preselected sequencing single nucleotide polymorphisms (SNPs). Imputation is accurate for old bulls but suggests direct genotyping for contemporary animals to enhance genomic selection.

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Area of Science:

  • Animal Genetics
  • Genomic Prediction
  • Bioinformatics

Background:

  • Preselected sequencing variants can enhance genomic prediction accuracy.
  • Imputation strategies for these variants require investigation across different animal populations.

Purpose of the Study:

  • To evaluate the imputation accuracy of preselected sequencing single nucleotide polymorphisms (SNPs) from Danish-Finnish-Swedish (DFS) and French (FRA) dairy breeds in Danish Jersey cattle.
  • To compare imputation performance in contemporary animals versus old bulls.

Main Methods:

  • A two-step imputation process was employed for contemporary animals, imputing to 54K and then to 54K + DFS + FRA SNPs.
  • Genotype imputation accuracy was assessed using correlations and concordance rates for DFS and FRA SNPs.
  • Imputation performance was analyzed concerning minor allele frequency (MAF) and animal age (contemporary vs. old bulls).

Main Results:

  • The two-step imputation achieved the highest accuracy for contemporary animals.
  • High imputation accuracy (correlation >97.2%, concordance >98.4%) was observed for DFS and FRA SNPs in old bulls across all MAF ranges.
  • Lower correlations but higher concordance rates were noted for SNPs with lower MAF in contemporary animals.

Conclusions:

  • Directly genotyping preselected sequencing SNPs using a customized SNP chip is recommended for contemporary animals due to variable imputation accuracy, especially for low MAF SNPs.
  • Re-genotyping is unnecessary for old bulls as preselected sequencing SNPs show high imputation accuracy across all MAF ranges.