Heap: A SNPs Detection Tool for NGS Data with Special Reference to GWAS and Genomic Prediction

DNA polymorphisms such as SNPs and INDELs among inbred lines or individuals are employed to identify loci associated with phenotypes or to predict phenotypes by genotypes. Recent years, those genome-wide association studies (GWAS) and genomic prediction (GP) studies are performed with next generation sequencing (NGS) data for comprehensive identification of genes associated with phenotypes and high-throughput breeding with effective selection strategies. The prediction accuracy of GWAS and GP strongly depends on the quality and quantity of polymorphisms. In recent GWAS and GP cases, DNA polymorphisms are often determined by mapping of NGS reads to the reference genomes and consequent variant calls. In those steps, the quality and quantity of DNA polymorphisms should be assured for the effectiveness of following GWAS and GP. In this study, we have developed a computational tool, Heap, designed for variant detection from results of NGS mapping. Heap detects larger number of variants taking advantage of the information whether the samples are inbred (homozygosity assumption) or not. For data portability to GWAS/GP, Heap outputs variant information in vcf, beagle and PED/MAP format files that are compatible with existing GWAS/GP tools. In this presentation, we explain the algorisms and demonstrate performance of Heap.
This work is partially supported by JST, CREST and Research Funding for Computational Software Supporting Program from Meiji University.