Development of the Wheat Practical Haplotype Graph database as a resource for genotyping data storage and genotype...

Area of Science:

• Plant genetics
• Bioinformatics
• Genomics

Background:

• High-density genotype data is crucial for bread wheat (Triticum aestivum L.) improvement.
• Efficient storage and imputation of this data are significant challenges in wheat breeding.

Purpose of the Study:

• To enhance the efficiency of high-density genotype data storage and imputation in bread wheat using the Practical Haplotype Graph (PHG) tool.
• To evaluate the imputation accuracy of PHG compared to existing methods like Beagle.

Main Methods:

• Constructed a Wheat PHG database using whole-exome capture sequencing data from 65 diverse wheat accessions.
• Inferred population haplotypes for reference genome intervals based on gene models.
• Imputed missing genotypes in various wheat panels using the Wheat PHG database.

Main Results:

• Achieved 92% imputation accuracy with 0.01× sequence coverage and 96.6% with 0.5× coverage.
• PHG imputation was ~3.5% more accurate than Beagle, with 27% higher accuracy for rare introgressed haplotypes.
• Imputation accuracy with genotyping-by-sequencing (GBS) data improved with parental haplotype inclusion.

Conclusions:

• The PHG tool provides a highly accurate and efficient method for genotype imputation in bread wheat.
• PHG-based imputation facilitates genetic mapping, identifying SNPs that explain a greater proportion of genetic variance for key traits.