Genome-wide association mapping for pre-harvest sprouting in European winter wheat detects novel resistance QTL, pleiotropic effects, and structural variation in multiple genomes

Linkage and association analysis to identify wheat pre-harvest sprouting resistance genetic regions and develop KASP markers

Pre-harvest sprouting (PHS) of wheat (Triticum aestivum L.) is one of the complex traits that result in rainfall-dependent reductions in grain production and quality worldwide. Breeding new varieties and germplasm with PHS resistance is of great importance to reduce this problem. However, research on markers and genes related to PHS resistance is limited, especially in marker-assisted selection (MAS) wheat breeding. To this end, we studied PHS resistance in recombinant inbred line (RIL) population and in 171 wheat germplasm accessions in different environments and genotyped using the wheat Infinium 50 K/660 K SNP array. Quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) identified 59 loci controlling PHS. Upon comparison with previously reported QTL affecting PHS, 16 were found to be new QTL, and the remaining 43 loci were co-localized with QTL from previous studies. We also pinpointed 12 candidate genes within these QTL intervals that share functional similarities with genes previously known to influence PHS resistance. In addition, we developed and validated two kompetitive allele-specific PCR (KASP) markers within the chromosome 7B region identified by linkage analysis. These QTL, candidate genes, and the KASP marker identified in this study have the potential to improve PHS resistance of wheat, and they may enhance our understanding of the genetic basis of PHS resistance, thus being useful for MAS breeding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01526-0.

Genetic variation for tolerance to pre-harvest sprouting in mungbean (Vigna radiata) genotypes

Pre-harvest sprouting (PHS) is one of the important abiotic stresses in mungbean which significantly reduces yield and quality of the produce. This study was conducted to evaluate the genetic variability for tolerance to pre-harvest sprouting in diverse mungbean genotypes while simultaneously deciphering the association of yield contributing traits with PHS. Eighty-three diverse mungbean genotypes (23 released varieties, 23 advanced breeding lines and 37 exotic germplasm lines) were investigated for tolerance to PHS, water imbibition capacities by pods, pod and seed physical traits. Wide variation in PHS was recorded which ranged between 17.8% to 81% (mean value 54.34%). Germplasm lines exhibited higher tolerance to PHS than the high-yielding released varieties. Correlation analysis revealed PHS to be positively associated with water imbibition capacity by pods (r = 0.21) and germinated pod % (r = 0.78). Pod length (r = -0.13) and seeds per pod (r = -0.13) were negatively influencing PHS. Positive associations between PHS and water imbibition capacity by pods, germinated pod % and 100-seed weight was further confirmed by multivariate analysis. Small-seeded genotypes having 100-seed weight <3 g exhibited higher tolerance to PHS compared to bold-seeded genotypes having 100-seed weight more than 3.5 g. Fresh seed germination among the selected PHS tolerant and susceptible genotypes ranged from 42% (M 204) to 98% (Pusa 1131). A positive association (r = 0.79) was recorded between fresh seed germination and PHS. Genotypes M 1255, M 145, M 422, M 1421 identified as potential genetic donors against PHS could be utilized in mungbean breeding programs.

Assessing Falling Number Stability Increases the Genomic Prediction Ability of Pre-Harvest Sprouting Resistance in Common Winter Wheat

Pre-harvest sprouting (PHS) resistance is a complex trait, and many genes influencing the germination process of winter wheat have already been described. In the light of interannual climate variation, breeding for PHS resistance will remain mandatory for wheat breeders. Several tests and traits are used to assess PHS resistance, i.e., sprouting scores, germination index, and falling number (FN), but the variation of these traits is highly dependent on the weather conditions during field trials. Here, we present a method to assess falling number stability (FNS) employing an after-ripening period and the wetting of the kernels to improve trait variation and thus trait heritability. Different genome-based prediction scenarios within and across two subsequent seasons based on overall 400 breeding lines were applied to assess the predictive abilities of the different traits. Based on FNS, the genome-based prediction of the breeding values of wheat breeding material showed higher correlations across seasons (r=0.505-0.548) compared to those obtained for other traits for PHS assessment (r=0.216-0.501). By weighting PHS-associated quantitative trait loci (QTL) in the prediction model, the average predictive abilities for FNS increased from 0.585 to 0.648 within the season 2014/2015 and from 0.649 to 0.714 within the season 2015/2016. We found that markers in the Phs-A1 region on chromosome 4A had the highest effect on the predictive abilities for FNS, confirming the influence of this QTL in wheat breeding material, whereas the dwarfing genes Rht-B1 and Rht-D1 and the wheat-rye translocated chromosome T1RS.1BL exhibited effects, which are well-known, on FN per se exclusively.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.