Mitochondrial sequence variants: testing imputation accuracy and their association with dairy cattle milk traits

Area of Science:

• Genomics
• Animal Genetics
• Bioinformatics

Background:

• Mitochondrial variants are known to contribute to genetic disorders in humans.
• Previous livestock studies on mitochondrial genomes were limited and inconclusive.
• Modern genome sequencing allows re-evaluation of mitochondrial variation's impact on livestock traits.

Purpose of the Study:

• To evaluate the accuracy of mitochondrial sequence imputation in dairy cattle.
• To investigate the role of mitochondrial variants in milk production traits using imputed and real genotypes.

Main Methods:

• Assessed imputation accuracy using Beagle software with SNP panels and a mitochondrial sequence reference in 516 animals.
• Performed genome-wide association study (GWAS) for milk, fat, and protein yields using imputed genotypes from 9515 animals.
• Utilized DR2 statistic to select highly accurate imputed sites for GWAS.

Main Results:

• Overall imputation accuracy (R² ) was 0.454, with low accuracy attributed to low MAF and D-loop variants.
• The Beagle DR2 statistic proved a reasonable proxy for selecting highly imputed sites.
• No significant mitochondrial SNP effects were found for milk production traits in the GWAS.

Conclusions:

• Mitochondrial genotype imputation accuracy from SNP panels to sequence was generally low.
• The DR2 statistic aids in selecting sites with higher empirical imputation accuracy.
• Larger reference populations and improved SNP panels are recommended for better imputation accuracy, especially for less common variants and D-loop regions.