Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat

Soil salinity can impose substantial stress on plant growth and cause significant yield losses. Crop varieties tolerant to salinity stress are needed to sustain yields in saline soils. This requires effective genotyping and phenotyping of germplasm pools to identify novel genes and QTL conferring salt tolerance that can be utilised in crop breeding schemes. We investigated a globally diverse collection of 580 wheat accessions for their growth response to salinity using automated digital phenotyping performed under controlled environmental conditions. The results show that digitally collected plant traits, including digital shoot growth rate and digital senescence rate, can be used as proxy traits for selecting salinity-tolerant accessions. A haplotype-based genome-wide association study was conducted using 58,502 linkage disequilibrium-based haplotype blocks derived from 883,300 genome-wide SNPs and identified 95 QTL for salinity tolerance component traits, of which 54 were novel and 41 overlapped with previously reported QTL. Gene ontology analysis identified a suite of candidate genes for salinity tolerance, some of which are already known to play a role in stress tolerance in other plant species. This study identified wheat accessions that utilise different tolerance mechanisms and which can be used in future studies to investigate the genetic and genic basis of salinity tolerance. Our results suggest salinity tolerance has not arisen from or been bred into accessions from specific regions or groups. Rather, they suggest salinity tolerance is widespread, with small-effect genetic variants contributing to different levels of tolerance in diverse, locally adapted germplasm

Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400–1,900) were measured across 8 years (2012–2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle

Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome

Introgression is a potential source of beneficial genetic diversity. The contribution of introgression to adaptive evolution and improvement of wheat as it was disseminated worldwide remains unknown. We used targeted re-sequencing of 890 diverse accessions of hexaploid and tetraploid wheat to identify wild-relative introgression. Introgression, and selection for improvement and environmental adaptation, each reduced deleterious allele burden. Introgression increased diversity genome wide and in regions harboring major agronomic genes, and contributed alleles explaining a substantial proportion of phenotypic variation. These results suggest that historic gene flow from wild relatives made a substantial contribution to the adaptive diversity of modern bread wheat

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Fine Physical and Genetic Mapping of Powdery Mildew Resistance Gene <i>MlIW172</i> Originating from Wild Emmer (<i>Triticum dicoccoides</i>)

<div><p>Powdery mildew, caused by <i>Blumeria graminis</i> f. sp. <i>tritici</i>, is one of the most important wheat diseases in the world. In this study, a single dominant powdery mildew resistance gene <i>MlIW172</i> was identified in the IW172 wild emmer accession and mapped to the distal region of chromosome arm 7AL (bin7AL-16-0.86-0.90) via molecular marker analysis. <i>MlIW172</i> was closely linked with the RFLP probe <i>Xpsr680</i>-derived STS marker <i>Xmag2185</i> and the EST markers <i>BE405531</i> and <i>BE637476</i>. This suggested that <i>MlIW172</i> might be allelic to the <i>Pm1</i> locus or a new locus closely linked to <i>Pm1</i>. By screening genomic BAC library of durum wheat cv. Langdon and 7AL-specific BAC library of hexaploid wheat cv. Chinese Spring, and after analyzing genome scaffolds of <i>Triticum urartu</i> containing the marker sequences, additional markers were developed to construct a fine genetic linkage map on the <i>MlIW172</i> locus region and to delineate the resistance gene within a 0.48 cM interval. Comparative genetics analyses using ESTs and RFLP probe sequences flanking the <i>MlIW172</i> region against other grass species revealed a general co-linearity in this region with the orthologous genomic regions of rice chromosome 6, <i>Brachypodium</i> chromosome 1, and sorghum chromosome 10. However, orthologous resistance gene-like RGA sequences were only present in wheat and <i>Brachypodium</i>. The BAC contigs and sequence scaffolds that we have developed provide a framework for the physical mapping and map-based cloning of <i>MlIW172</i>.</p></div

Mapped wheat EST markers and ortholgous gene pairs among <i>Brachypodium</i>, rice and sorghum

<p>Mapped wheat EST markers and ortholgous gene pairs among <i>Brachypodium</i>, rice and sorghum</p

Genetic and comparative genomics linkage map of powdery mildew resistance gene <i>MlIW172</i> derived from wild emmer.

<p><b>A: </b><i>MlIW172</i> physical bin map. <i>MlIW172</i> was mapped to the distal bin 7AL16-0.86-0.90. <b>B:</b> Preliminary <i>MlIW172</i> genetic map on wheat chromosome arm 7AL with genetic distances in cM shown on the left, markers shown on the right. <b>C: </b><i>MlIW172</i> high-resolution genetic map on wheat 7AL arm with genetic distances in cM shown on the left, EST-STS, EST-SSR and SSR markers shown on the right. Molecular markers that were previously assigned to the 7A wheat deletion bin map (<b>A</b>) are connected to the physical map with solid lines. The <i>MlIW172</i> locus is in red and underlined. The markers which served as anchors, establishing colinearity between the <i>MlIW172</i> genetic map and the sequences of <i>Brachypodium</i>, rice and sorghum, are connected to the <i>Brachypodium</i> gene with solid lines. <b>D:</b> The <i>MlIW172</i> orthologous genomic region on <i>Brachypodium</i> chromosome 1 (150kb) with orthologous genes shown on the right. The four genes in green represent the RGA cluster. <b>E:</b> The <i>MlIW172</i> orthologous genomic region on rice chromosome 6 (85.8kb) with orthologous genes shown on the right. <b>F:</b> The <i>MlIW172</i> orthologous genomic region on sorghum chromosome 10 (138.9kb) with orthologous genes shown on the right.</p