Capturing Wheat Phenotypes at the Genome Level

Abstract

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Keywords: CRISPR/Cas9; QTL cloning; Wheat; abiotic-stress tolerance; disease resistance; genome-wide association; genomic selection; quantitative trait locus mapping.

Figure 1

Overview of the parallel progress in the analysis of the wheat genome and high throughput phenotyping. The top panel provides the timeline for wheat-genome studies, opening up of the next generation omics; the image on the far right is the modeling of wheat granule-bound starch synthase, using Phyre 2 (Kelley et al., 2015). The lower panel emphasizes the progress of both field-based phenomics (image of drone with spectral-recording equipment, kindly provided by S. Kant, Agriculture Victoria, Grains Innovation Park, Horsham, VIC, Australia) and laboratory-based high-throughput analyses (part of Figure 6 of Banerjee et al., 2020), showing false-color composite from hyperspectral data of wheat leaves, kindly provided by S. Kant.

Figure 2

Aligning genome maps for SNP, DArTseq and/or GBS markers with IWGSC RefSeq 1.0 using PRETZEL https://plantinformatics.io. The right most map provides the location of a QTL for the emergence of additional seminal roots (midpoint = 26.6 cM; Golan et al., 2018) from a Svevo x Zavitan map based on a 90K SNP chip. The second map (from the right) is the durum genome sequence for 1B, available at the URGI with the SNPs annotations included. The second map from the left is the IWGSC RefSeq 1.0 with the HC ver1.1 gene annotation, the 90k SNP annotation, the LC ver1.1 gene annotations and the SSR annotations included. The left most map is the genome sequence for the 1RS.1BL sequence from wheat cv Aikan58 (Ru et al., 2020) with three sources of gene annotations included. The red dots identify the gene models predicted to be located in the QTL identified in the Svevo x Zavitan QTL.