Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population

Area of Science:

• Genomics
• Animal Breeding
• Bioinformatics

Background:

• Whole-genome sequence (WGS) data optimizes genome-wide association studies (GWAS) and genomic predictions.
• Sequencing thousands of individuals is costly; genotype imputation offers a less expensive alternative.
• This study investigates imputation accuracy and provides insights for designing genotype imputation strategies.

Purpose of the Study:

• To assess the accuracy of imputing single nucleotide polymorphism (SNP) data to whole-genome sequence (WGS) data.
• To identify key factors influencing genotype imputation accuracy in livestock.
• To offer guidance on designing effective genotype imputation protocols.

Main Methods:

• Genotyping 450 chickens using a 600K SNP array.
• Whole-genome re-sequencing of 24 key individuals.
• Evaluating imputation accuracy using Beagle and FImpute software.
• Analyzing the impact of sequencing depth, reference population size, and individual selection methods.

Main Results:

• Imputation accuracy increased with higher SNP density (600K vs 60K) and sequencing depth.
• FImpute generally outperformed Beagle in imputation accuracy.
• Optimal selection of reference individuals significantly improved imputation accuracy compared to random selection.
• Increasing reference population size enhanced accuracy for FImpute, while Beagle showed peak accuracy at six-fold coverage.

Conclusions:

• Genotype imputation accuracy is influenced by sequencing cost, reference population size, imputation algorithms, marker density, and population structure.
• Increasing sequencing cost generally leads to higher imputation accuracy.
• An optimal imputation strategy requires comprehensive consideration of these factors to enhance genomic predictions and GWAS in livestock.