Whole-exome sequencing of selected bread wheat recombinant inbred lines as a useful resource for allele mining and bulked segregant analysis

Abstract

Although wheat (Triticum aestivum L.) is the main staple crop in the world and a major source of carbohydrates and proteins, functional genomics and allele mining are still big challenges. Given the advances in next-generation sequencing (NGS) technologies, the identification of causal variants associated with a target phenotype has become feasible. For these reasons, here, by combining sequence capture and target-enrichment methods with high-throughput NGS re-sequencing, we were able to scan at exome-wide level 46 randomly selected bread wheat individuals from a recombinant inbred line population and to identify and classify a large number of single nucleotide polymorphisms (SNPs). For technical validation of results, eight randomly selected SNPs were converted into Kompetitive Allele-Specific PCR (KASP) markers. This resource was established as an accessible and reusable molecular toolkit for allele data mining. The dataset we are making available could be exploited for novel studies on bread wheat genetics and as a foundation for starting breeding programs aimed at improving different key agronomic traits.

GeCKO: user-friendly workflows for genotyping complex genomes using target enrichment capture. A use case on the large tetraploid durum wheat genome

“…The final filter (04), targeting bi-allelic SNPs compatible with durum wheat’s autogamous reproduction, further narrowed the SNPs down to 56,704. However, their Ts/Tv ratio of 2.22 aligns considerably better with previous studies (e.g., Ts/Tv around 2.11 in bread wheat [ 44 , 45 ]), indicating a significant improvement in overall loci quality (Figs. 7 b vs 8 b) and in mean depth per genotype (Figs.…”

Allelic Variations in Vernalization (Vrn) Genes in Triticum spp.

“…Recent advances in sequencing technologies can now help to untangle vernalization, covering all the gene space of Vrn-1 in different species at one time. Precise genotyping of Vrn-1 genes, by assigning weights to each Vrn allele, might not only improve the accuracy of genomic prediction models but also streamline the varietal selection process [118][119][120][121], identifying the best Vrn allelic combination. This information could also be used to feed phenological predictive models to rapidly identify genetic material with the optimum phenology within different target regions and/or untested advanced breeding lines early in a breeding program.…”

Genomics for Yield and Yield Components in Durum Wheat

“…With the rapid advances in sequencing and bioinformatic technologies, innovative and accelerated strategies based on targeted editing approach will allow a better manipulation of the genes and, consequently, an acceleration in the understanding of the gene interactions existing between the components of the yield [144,145]. These strategies include revealing the biological function of genes by characterizing their corresponding mutants, identifying the gene responsible for an interesting phenotype by gene cloning, and verifying the function of a target gene by transforming the candidate wild-type gene into the corresponding wheat mutant.…”