Exome sequence genotype imputation in globally diverse hexaploid wheat accessions

Abstract

Imputing genotypes from the 90K SNP chip to exome sequence in wheat was moderately accurate. We investigated the factors that affect imputation and propose several strategies to improve accuracy. Imputing genetic marker genotypes from low to high density has been proposed as a cost-effective strategy to increase the power of downstream analyses (e.g. genome-wide association studies and genomic prediction) for a given budget. However, imputation is often imperfect and its accuracy depends on several factors. Here, we investigate the effects of reference population selection algorithms, marker density and imputation algorithms (Beagle4 and FImpute) on the accuracy of imputation from low SNP density (9K array) to the Infinium 90K single-nucleotide polymorphism (SNP) array for a collection of 837 hexaploid wheat Watkins landrace accessions. Based on these results, we then used the best performing reference selection and imputation algorithms to investigate imputation from 90K to exome sequence for a collection of 246 globally diverse wheat accessions. Accession-to-nearest-entry and genomic relationship-based methods were the best performing selection algorithms, and FImpute resulted in higher accuracy and was more efficient than Beagle4. The accuracy of imputing exome capture SNPs was comparable to imputing from 9 to 90K at approximately 0.71. This relatively low imputation accuracy is in part due to inconsistency between 90K and exome sequence formats. We also found the accuracy of imputation could be substantially improved to 0.82 when choosing an equivalent number of exome SNP, instead of 90K SNPs on the existing array, as the lower density set. We present a number of recommendations to increase the accuracy of exome imputation.

Meta-analysis of GWAS in canola blackleg (Leptosphaeria maculans) disease traits demonstrates increased power from imputed whole-genome sequence

“…We have shown that the accuracy of imputation was > 0.71. This level of accuracy is similar to what has been previously achieved in other crops 54 . However, it is substantially less than in human and other mammals 56 – 58 .…”

“…Imputation has been implemented in several crops, a study by Torkamaneh et al 53 achieved high accuracy when imputing from low-coverage genotyping-by-sequencing (GBS) to WGS in soybean. Similarly, Shi et al 54 examined the potential of exome sequence imputation in wheat. These studies emphasized the need to carefully evaluate imputation algorithms in new datasets as large differences in performance exist.…”

Diversity and Genome Analysis of Australian and Global Oilseed Brassica napus L. Germplasm Using Transcriptomics and Whole Genome Re-sequencing

“…Of the 633 samples genotyped using the GBS-t method, 149 samples were processed for WGR, including one biological replicate ( Supplementary Table S1 and Supplementary Figure S1 ). This included 10 samples comprising both Australian spring types and diverse winter types that were used in experimental development of WGR library preparation, 6 commonly studied European winter types, 87 significant Australian spring types, and 46 samples forming a global diversity panel chosen using a custom pipeline, selecting a representative of each branch clade from a neighbor-joining (NJ) tree, to capture overall diversity ( Shi et al, 2017 ).…”

CropSNPdb: a database of SNP array data for Brassica crops and hexaploid bread wheat

“…Imputation approaches can also be applied to impute whole genomes from SNP arrays using haplotype and pedigree information (Das et al ., ). Although the Brassica 60K and wheat 90K markers are not dense enough to impute whole genomes, imputing from 9 to 90K SNP density in wheat was possible at 70% accuracy with 197 reference samples (Shi et al ., ). CropSNPdb markers can therefore be used as references to impute Brassica 60K and wheat 90K markers from lower density marker sets.…”