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

Abstract

This study developed a new genotyping method that can accurately infer heterozygous genotype information from the complex plant genome sequence data, which helped discover new alleles in the association studies. Many software packages and pipelines had been developed to handle the sequence data of the model species. However, Genotyping from complex heterozygous plant genome needs further improvement on the previous methods. Here we present a new pipeline available at https://github.com/Ncgrhg/HetMapv1 ) for variant calling and missing genotype imputation from low coverage sequence data for heterozygous plant genomes. To check the performance of the HetMap on the real sequence data, HetMap was applied to both the F₁ hybrid rice population, which consists of 1495 samples and the wild rice population with 446 samples. The high coverage sequence data of four hybrid rice accessions and two wild rice accessions, which were also included in low coverage sequence data, were used to validate the accuracy of genotype inference. The validation results showed that HetMap archieved significant improvement in heterozygous genotype inference accuracy (13.65% for hybrid rice, 26.05% for wild rice) and total accuracy compared with similar software packages. The application of the new genotype with the genome-wide association study also showed improvement of association power in wild rice awn length phenotype. It could archive high genotype inference accuracy in low sequence coverage in a small population with both the natural and constructed recombination population. HetMap provided a powerful tool for the heterozygous plant genome sequence data analysis, which may help to discover new phenotype regions for the plant species with the complex heterozygous genome.