Genotype imputation accuracy in multiple equine breeds from medium- to high-density genotypes

Area of Science:

• * Genomics and Animal Breeding
• * Quantitative Genetics
• * Bioinformatics

Background:

• * Genotype imputation is crucial for increasing genotype density in genomic analyses.
• * Imputation accuracy can be affected by various factors, including reference population composition.
• * Combining data from different genotyping platforms is increasingly necessary in equine genomics.

Purpose of the Study:

• * To evaluate and compare the accuracy of imputing high-density genotypes from moderate-density ones.
• * To assess the impact of single-breed versus multiple-breed reference sets on imputation accuracy.
• * To determine the effectiveness of genotype imputation for merging data from different equine genotyping arrays.

Main Methods:

• * Imputation of high-density genotypes (Affymetrix Axiom Equine array) from moderate-density arrays (Illumina Equine SNP50 and SNP70) was performed.
• * Validation was conducted using five horse breed groups (Arab, Trotteur Français, Selle Français, Anglo-Arab, Foreign Horses).
• * Two imputation strategies were compared: using a single-breed reference set versus a multiple-breed reference set.

Main Results:

• * Concordance rates ranged from 97.8% to 99.0%, and correlations (r²) from 0.94 to 0.99.
• * Single-breed imputation demonstrated a small but consistent advantage over multiple-breed imputation across breeds.
• * Adding horses from different breed groups to the reference population did not improve imputation accuracy.

Conclusions:

• * Genotype imputation is an accurate method for combining datasets from different genotyping platforms in horses.
• * Within-breed reference sets offer a slight advantage in imputation accuracy compared to multi-breed sets.
• * The findings support the use of imputation for large-scale equine genomic studies utilizing diverse data sources.