High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is...

Area of Science:

• Genomics and Animal Breeding
• Quantitative Genetics
• Bioinformatics

Background:

• Accurate genotype imputation is crucial for genomic selection and breeding programs.
• Low-density SNP panels are cost-effective but may compromise imputation accuracy.
• Optimizing SNP selection methods is key to maximizing imputation performance from low-density panels.

Purpose of the Study:

• To quantify the accuracy of imputing medium-density single nucleotide polymorphism (SNP) genotypes from low-density panels.
• To evaluate the impact of different SNP selection methods on imputation accuracy across five sheep breeds.
• To determine the optimal panel density and SNP selection strategy for accurate genotype imputation.

Main Methods:

• Four SNP selection methods (random, genomic blocks, equidistant, combined MAF/LD/distance) were evaluated.
• SNP selection was based on genomic characteristics like minor allele frequency (MAF) and linkage disequilibrium (LD).
• Imputation accuracy was assessed using mean animal allele concordance rates across different breeds and reference population structures.

Main Results:

• Imputation accuracy improved with increasing panel density, with diminishing returns.
• SNP selection method significantly impacted imputation accuracy, especially for panels under 9,000 SNPs.
• The most accurate SNP selection method for panels <9,000 SNPs utilized MAF and LD patterns within genomic blocks.
• Within-breed SNP selection and imputation yielded higher accuracy than multi-breed approaches.
• Mean animal allele concordance rates ranged from 0.89 to 0.97 across breeds.

Conclusions:

• Accurate medium-density genotype imputation is feasible using low-density panels (≥6,000 SNPs).
• SNP selection based on MAF and LD within genomic blocks is recommended for panels <9,000 SNPs.
• Breed-specific SNP selection and imputation strategies enhance accuracy in sheep populations.