Development and validation of a horse reference panel for genotype imputation.

Area of Science:

• Animal Genomics
• Bioinformatics
• Population Genetics

Background:

• Genotype imputation is crucial for cost-effectively generating sequence-level genotypes in animals.
• High imputation accuracy is essential for improving the power of genomic studies.
• This study focuses on optimizing genotype imputation strategies in German warmblood horses.

Purpose of the Study:

• To develop an optimal strategy for genotype imputation from array data to sequence level in horses.
• To investigate factors influencing genotype imputation accuracy.
• To assess the impact of reference panel characteristics and imputation software.

Main Methods:

• Utilized whole-genome sequence data from 317 horses across 46 breeds.
• Employed Beagle 5.1 software for imputation, comparing its performance with Impute 5 and Minimac 4.
• Evaluated imputation accuracy based on reference panel size, composition, and marker density.

Main Results:

• Imputation accuracy ranged from 0.64 to 0.70 for chromosome 3, generally increasing with reference panel size.
• Accuracy decreased with genetically distant individuals in the reference panel.
• Beagle 5.1 showed superior performance; imputation from higher density (670K) arrays yielded higher accuracy (approx. 0.90) than medium density (60K).

Conclusions:

• Genotype imputation accuracy in horses is influenced by reference panel characteristics, marker density, and software choice.
• Genotype imputation can effectively augment limited sequence data in horses.
• This approach enhances downstream analyses like genome-wide association studies and detection of embryonic lethal variants.