Association Studies in Polyploid Wheat Using Imputation-Based Analyses of Genotyping-By-Sequencing Data

High-density genotyping based on complexity-reduced sequencing allows for bias-free analysis of genetic variation in complex genomes. However, high levels of missing data reduce the utility of these approaches to perform genome-wide association studies. Here we used the reference sequence of the wheat genome for mapping data generated using the genotyping-by-sequencing (GBS) approach and performing association scans in a diverse panel of winter wheat cultivars. The high-quality 90K SNP genotyping dataset was used as a framework for testing the efficiency of various approaches for imputation of missing data in a GBS dataset. The power of association scans has been increased after genotype imputation. Out of nearly 70,000 SNPs, 814 showed significant association with 30 traits at FDR < 0.01. The previously identified markers showing an association with the pseudo-black chaff, plant height, stem melanism phenotypes and stem rust genes are confirmed in our results. Our results demonstrate that the availability of the reference genome sequence and high-density haplotypic data allows for efficient utilization of GBS data for genome-wide association study in polyploid wheat.