Sequencing-based high throughput mutation detection in bread wheat

Abstract

Forward genetic approaches have limited use for agronomic traits that can’t be reliably scored on a single plant basis. Thus, mutants in wheat and other crops are more useful for gene function studies by reverse genetic approach. With a long-term goal to develop a sequence-based mutation detection resource in hexaploid wheat, we conducted a feasibility study to accurately differentiate induced mutations from the homoeologs’ sequence variations present among the three wheat genomes. A reduced representation ApeKI library consisting of 21 Ethylmethane Sulfonate (EMS) induced mutants and two wild type cv. Indian plants was developed using individual barcode adapters and sequenced. A novel bioinformatics pipeline was developed to identify sequence variants using 178,464 wheat unigenes as a reference wheat transcriptome. In total, 14,130 mutational changes [Single Nucleotide Polymorphisms (SNPs) and Insertions/Deletions (INDELs)] and 150,511 homoeologous sequence changes were detected. On an average, 662 SNPs (ranging from 46 to 1,330) and 10 small INDELs (ranging from 0 to 23) were identified for each of the mutants. A mutation frequency of one per 5 Kb was observed with 70 % being transitions and 30 % transversions. The pipeline was tested using the known sequence changes in the three wheat genes. Genes present in the distal regions of the chromosomes were found to be more prone to EMS compared to genes present in the proximal regions. Redefined parameters identified a total of 28,348 mutational changes (1,349/plant). We conclude that sequencing based mutation detection is a valuable method to identify induced mutations at large.