Genotype calling and haplotype inference from low coverage sequence data in heterozygous plant genome using HetMap

Area of Science:

• Genomics
• Bioinformatics
• Plant Science

Background:

• Existing genotyping methods struggle with complex heterozygous plant genomes.
• Model species have numerous software solutions, but non-model plants require improved tools.

Purpose of the Study:

• To develop a novel pipeline (HetMap) for accurate variant calling and genotype imputation in heterozygous plant genomes.
• To enhance the analysis of low-coverage sequence data for plant populations.

Main Methods:

• Developed HetMap pipeline for variant calling and genotype imputation.
• Applied HetMap to large hybrid rice (1495 samples) and wild rice (446 samples) populations.
• Validated HetMap accuracy using high-coverage sequencing data from selected accessions.

Main Results:

• HetMap demonstrated significant improvements in heterozygous genotype inference accuracy (13.65% in hybrid rice, 26.05% in wild rice).
• Achieved higher overall accuracy compared to existing software packages.
• Enhanced genome-wide association study power for wild rice awn length phenotype.

Conclusions:

• HetMap is a powerful tool for analyzing heterozygous plant genome sequence data.
• The pipeline offers high accuracy even with low sequence coverage and small populations.
• Facilitates discovery of new phenotype-associated regions in complex plant genomes.