A major QTL responsible for self-incompatibility was stably identified in two F populations. Through fine mapping and qRT-PCR analysis, ARK3 emerged as the most promising candidate gene, playing a pivotal role in regulating self-incompatibility in Brassica oleracea. Self-incompatibility (SI) is a common phenomenon in Brassica oleracea species, which can maintain genetic diversity but will also limit seed production. Although the S locus has been extensively studied in Arabidopsis and some Brassicaceae crops, map-based cloning of self-incompatibility genes has not been conducted in Brassica oleracea, such as cauliflower and broccoli. In the present study, we identified a major co-localized QTL on chromosome C6 that control SI in two F populations derived from intervarietal crosses: broccoli × cauliflower (CL_F) and cauliflower × Chinese kale (CJ_F). Subsequently, this QTL was narrowed down to 168.5 Kb through fine mapping using 3,429 F progenies and 12 available KASP markers. Within this 168.5 Kb region, BolC6t39084H, a homologue of Arabidopsis ARK3, could be a candidate gene that plays a key role in regulating SI in B. oleracea species. This finding can pave the way for an in-depth understanding of the molecular mechanisms underlying SI, and will contribute to the seed production of B. oleracea vegetables.

Two novel and one known genomic regions associated with R-gene resistance to Phytophthora sojae were identified by genome-wide association analysis and linkage analysis in soybean. Phytophthora root and stem rot (PRR) caused by Phytophthora sojae is a severe disease that causes substantial economic losses in soybean [Glycine max (L.) Merr.]. The primary approach for successful disease management of PRR is using R-gene-mediated resistance. Based on the phenotypic evaluation of 376 cultivated soybean accessions for the R-gene type resistance to P. sojae (isolate 2457), a genome-wide association analysis identified two regions on chromosomes 3 and 8. The most significant genomic region (20.7-21.3 Mbp) on chromosome 8 was a novel resistance locus where no Rps gene was previously reported. Instead, multiple copies of the UDP-glycosyltransferase superfamily protein-coding gene, associated with disease resistance, were annotated in this new locus. Another genomic region on chromosome 3 was a well-known Rps cluster. Using the Daepung × Ilpumgeomjeong RIL population, a linkage analysis confirmed these two resistance loci and identified a resistance locus on chromosome 2. A unique feature of the resistance in Ilpumgeomjeong was discovered when phenotypic distribution was projected upon eight groups of RILs carrying different combinations of resistance alleles for the three loci. Interestingly, the seven groups carrying at least one resistance allele statistically differed from the other with none, regardless of the number of resistance alleles. This suggests that the respective three different resistance genes can confer resistance to P. sojae isolate 2457. Deployment of the three regions via marker-assisted selection will facilitate effectively improving resistance to particular P. sojae isolates in soybean breeding programs.

Using QTL mapping and GWAS, two candidate genes (Zm00001d051039 and Zm00001d051147) were consistently identified across the three different environments and BLUP values. GWAS analysis identified the candidate gene, Zm00001d044845. These genes were subsequently validated to exhibit a significant association with maize gray leaf spot (GLS) resistance. Gray leaf spot (GLS) is a major foliar disease of maize (Zea mays L.) that causes significant yield losses worldwide. Understanding the genetic mechanisms underlying gray leaf spot resistance is crucial for breeding high-yielding and disease-resistant varieties. In this study, eight tropical and subtropical germplasms were crossed with the temperate germplasm Ye107 to develop a nested association mapping (NAM) population comprising 1,653 F2:8 RILs, consisting of eight recombinant inbred line (RIL) subpopulations, using the single-seed descent method. The NAM population was evaluated for GLS resistance in three different environments, and genotyping by sequencing of the NAM population generated 593,719 high-quality single-nucleotide polymorphisms (SNPs). Linkage analysis and genome-wide association studies (GWASs) were conducted to identify candidate genes regulating GLS resistance in maize. Both analyses identified 25 QTLs and 149 SNPs that were significantly associated with GLS resistance. Candidate genes were screened 20 Kb upstream and downstream of the significant SNPs, and three novel candidate genes (Zm00001d051039, Zm00001d051147, and Zm00001d044845) were identified. Zm00001d051039 and Zm00001d051147 were located on chromosome 4 and co-localized in both linkage (qGLS4-1 and qGLS4-2) and GWAS analyses. SNP-138,153,206 was located 0.499 kb downstream of the candidate gene Zm00001d051039, which encodes the protein IN2-1 homolog B, a homolog of glutathione S-transferase (GST). GSTs and protein IN2-1 homolog B scavenge reactive oxygen species under various stress conditions, and GSTs are believed to protect plants from a wide range of biotic and abiotic stresses by detoxifying reactive electrophilic compounds. Zm00001d051147 encodes a probable beta-1,4-xylosyltransferase involved in the biosynthesis of xylan in the cell wall, enhancing resistance. SNP-145,813,215 was located 2.69 kb downstream of the candidate gene. SNP-5,043,412 was consistently identified in three different environments and BLUP values and was located 8.788 kb downstream of the candidate gene Zm00001d044845 on chromosome 9. Zm00001d044845 encodes the U-box domain-containing protein 4 (PUB4), which is involved in regulating plant immunity. qRT-PCR analysis showed that the relative expression levels of the three candidate genes were significantly upregulated in the leaves of the TML139 (resistant) parent, indicating that these three candidate genes could be associated with resistance to GLS. The findings of this study are significant for marker-assisted breeding aimed at enhancing resistance to GLS in maize and lay the foundation for further elucidation of the genetic mechanisms underlying resistance to gray leaf spot in maize and breeding of new disease-resistant varieties.

Robust QTLs conferring resistance to bacterial leaf streak in wheat were mapped on chromosomes 3B and 5A from the variety Boost and on chromosome 7D from the synthetic wheat line W-7984. Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa poses a significant threat to global wheat production. High levels of BLS resistance are rare in hexaploid wheat. Here, we screened 101 diverse wheat genotypes under greenhouse conditions to identify new sources of BLS resistance. Five lines showed good levels of resistance including the wheat variety Boost and the synthetic hexaploid wheat line W-7984. Recombinant inbred populations derived from the cross of Boost × ND830 (BoostND population) and W-7984 × Opata 85 (ITMI population) were subsequently evaluated in greenhouse and field experiments to investigate the genetic basis of resistance. QTLs on chromosomes 3B, 5A, and 5B were identified in the BoostND population. The 3B and 5A QTLs were significant in all environments, but the 3B QTL was the strongest under greenhouse conditions explaining 38% of the phenotypic variation, and the 5A QTL was the most significant in the field explaining up to 29% of the variation. In the ITMI population, a QTL on chromosome 7D explained as much as 46% of the phenotypic variation in the greenhouse and 18% in the field. BLS severity in both populations was negatively correlated with days to heading, and some QTLs for these traits overlapped, which explained the tendency of later maturing lines to have relatively higher levels of BLS resistance. Markers associated with the QTLs were converted to KASP markers, which will aid in the deployment of the QTLs into elite lines for the development of BLS-resistant wheat varieties.

Key message A major quantitative trait locus (qGW12) for grain shape and weight has been isolated in rice, corresponding to LOC_Os12g17900/OsPUB23, and its encoded protein interacts with OsMADS1. Grain shape in rice is an important trait that influences both yield and quality. The primary determinants of grain shape are quantitative trait loci (QTLs) inherited from natural variation in crops. In recent years, much attention has been paid to the molecular role of QTLs in regulating grain shape and weight. In this study, we report the cloning and characterization of qGW12, a major QTL regulating grain shape and weight in rice, using a series of chromosome fragment substitution lines (CSSLs) derived from Oryza sativa indica cultivar 9311 (acceptor) and Oryza rufipogon Griff (donor). One CSSL line, Q187, harboring the introgression of qGW12, exhibited a significant decrease in grain-shape-related traits (including grain length and width) and thousand-grain weight compared to the cultivar 9311. Subsequent backcrossing of Q187 with 9311 resulted in the generation of secondary segregating populations, which were used to fine-map qGW12 to a 24-kb region between markers Seq-44 and Seq-48. Our data indicated that qGW12 encodes a previously unreported U-box type E3 ubiquitin ligase, designated OsPUB23, which exhibited E3 ubiquitin ligase activity. Overexpression of OsPUB23 in rice resulted in higher plant yield than the wild type due to an increase in grain size and weight. Conversely, loss of OsPUB23 function resulted in the opposite tendency. Yeast two-hybrid screening and split luciferase complementation assays revealed that OsPUB23 interacts with OsMADS1. The functional characterization of OsPUB23 provides new genetic resources for improving of grain yield and quality in crops.

A powdery mildew (Pm) resistance locus PmRc1 was identified and transferred from Roegneria ciliaris into wheat. Two compensative translocation lines carrying PmRc1 were developed. Powdery mildew (Pm), caused by the biotrophic fungal pathogen Blumeria graminis f.sp. tritici (Bgt), is a global destructive disease of bread wheat (Triticum aestivum L.). Identifying and utilizing new Pm resistance gene(s) is the most fundamental work for disease control. Roegneria ciliaris (2n = 4 x= 28, genome SSYY) is a wild relative species of cultivated wheat. In this work, we evaluated wheat-R. ciliaris disomic chromosome addition lines for Pm resistance in multiple years. The introduction of R. ciliaris chromosome 1S into wheat enhanced resistance. The resistance locus on 1S was designated as PmRc1. To cytologically map PmRc1, we induced structural rearrangements using ion irradiation and increasing homoeologous chromosomal recombination. The identified 43 1S translocation or deletion lines were used to construct 1S cytological bin map by marker analysis using 111 molecular markers. Based on the Pm resistance of the characterized structural rearrangement lines, the PmRc1 locus was cytologically mapped to bin 1SS-8 of 1S short arm, flanked by markers CMH93-2 and CMH114-1. Two compensatory chromosomal translocation lines (T1SS 1BL and T1SS-1AS 1AL) carrying PmRc1 were developed and assessed for their agronomic traits. Translocation chromosome T1SS 1BL had enhanced Pm resistance accompanied by negative effects on grain number and single plant yield. Translocation chromosome T1SS-1AS 1AL had enhanced Pm resistance and increased spikelet number per spike, without any obvious negative effect on other tested traits. Thus, T1SS-1AS 1AL is recommended preferentially used in wheat breeding for Pm resistance.

Approaches for constructing training sets in genomic selection are proposed to efficiently identify top-performing genotypes from a breeding population. Identifying superior genotypes from a candidate population is a key objective in plant breeding programs. This study evaluates various methods for the training set optimization in genomic selection, with the goal of enhancing efficiency in discovering top-performing genotypes from a breeding population. Additionally, two approaches, inspired by classical optimal design criteria, are proposed to expand the search space for the best genotypes and compared with methods focusing on maximizing accuracy in breeding value prediction. Evaluation metrics such as normalized discounted cumulative gain, Spearman's rank correlation, and Pearson's correlation are employed to assess performance in both simulation studies and real trait analyses. Overall, for candidate populations lacking a strong subpopulation structure, a ridge regression-based method, referred to as is recommended. For candidate populations with a strong subpopulation structure, a heuristic-based version of generalized coefficient of determination and a D-optimality-like method that maximizes overall genomic variation are preferred approaches for the primary objective of plant breeding. For populations with a large number of candidates, a proposed ranking method ( ) can first be used to down-scale the candidate population, after which a heuristic-based method is employed to identify the best genotypes. Notably, the proposed has been verified to be equivalent to the original version, known as , but its implementation is much more computationally efficient.

Genetic gain in Nordic spring barley varieties was estimated to 1.07% per year. Additionally, genomic predictive ability for yield was 0.61 in a population of breeding lines. Barley is one of the most important crops in Europe and meeting the growing demand for food and feed requires continuous increase in yield. Genomic prediction (GP) has the potential to be a cost-efficient tool in breeding for complex traits; however, the rate of yield improvement in current barley varieties is unknown. This study therefore investigated historical and current genetic gains in spring barley and how accounting for row-type population stratification in a breeding population influences GP results. The genetic gain in yield was estimated using historical data from field trials from 2014 to 2022, with 22-60 market varieties grown yearly. The genetic gain was estimated to 1.07% per year for all varieties, serving as a reference point for future breeding progress. To analyse the potential of using GP in spring barley a population of 375 breeding lines of two-row and six-row barley were tested in multi-environment trials in 2019-2022. The genetic diversity of the row-types was examined and used as a factor in the predictions, and the potential to predict untested locations using yield data from other locations was explored. This resulted in an overall predictive ability of 0.61 for yield (kg/ha), with 0.57 and 0.19 for the separate two-row and the six-row breeding lines, respectively. Together this displays the potential of implementing GP in breeding programs and the genetic gain in spring barley market varieties developed through GP will help in quantifying the benefit of GP over conventional breeding in the future.

Two closely linked novel loci, qKRN2-1 and qKRN2-2, associated with kernel row number were fine-mapped on chromosome 2, and a key candidate gene for qKRN2-1 was identified through expression analysis. Kernel row number (KRN) is a crucial factor influencing maize yield and serves as a significant target for maize breeding. The use of wild progenitor species can aid in identifying the essential traits for domestication and breeding. In this study, teosinte (MT1) served as the donor parent, the inbred maize line of Mo17 was used as the recurrent parent, we identified a major quantitative trait locus (QTL) for KRN, designated qKRN2, into two closely linked loci, qKRN2-1 and qKRN2-2. Here, fine mapping was performed to investigate two QTLs, qKRN2-1 and qKRN2-2, within a genomic range of 272 kb and 775 kb, respectively. This was achieved using a progeny test strategy in an advanced backcross population, with the two QTLs explaining 33.49% and 35.30% of the phenotypic variance. Molecular marker-assisted selection resulted in the development of two nearly isogenic lines (NILs), qKRN2-1 and qKRN2-2, which differed only in the segment containing the QTL. Notably, the maize (Mo17) alleles increased the KRN relative to teosinte by approximately 1.4 and 1.2 rows for qKRN2-1 and qKRN2-2, respectively. Zm00001d002989 encodes a cytokinin oxidase/dehydrogenase and its expression in the immature ears exhibited significant differences among the qKRN2-1 NILs. In situ hybridization localized Zm00001d002989 to the primordia of the inflorescence meristem and spikelet pair meristems, is predicted to be the causal gene of qKRN2-1. The findings of this study deepen our understanding of the genetic basis of KRN and hold significant potential for improving maize grain yields.

The gene regulating fruit pedicel length in wax gourd was finely mapped to a 211 kb region on chromosome 8. The major gene, Bch08G017310 (BhGA2ox3), was identified through forward genetics. Fruit pedicel length (FPL) is a crucial trait in wax gourd (Benincasa hispida) that affects fruit development and cultivation management. However, the key regulatory genes and mechanisms of FPL in wax gourds remain poorly understood. In this study, we constructed an F population using wax gourd plants with long fruit pedicels (GF-7-1-1) and short fruit pedicels (YSB-1-1-2) as parents. Through BSA-seq, we initially localised the FPL candidate gene to an 8.4 Mb region on chromosome 8, which was further narrowed down to a 1.1 Mb region via linkage analysis. A large F population of 2163 individuals was used to screen for recombinants, and the locus was ultimately narrowed to within a 211 kb (62,299,856-62,511,174 bp) region. Sequence and expression analyses showed that Bch08G017310 (named BhGA2ox3) is a strong candidate gene for FPL in wax gourds. It encodes gibberellin (GA) 2-beta-dioxygenase, a member of the GA 2-oxidase (GA2ox) family. Cytology showed that GA treatment significantly elongated the fruit pedicels and enlarged the cells in the plants with short fruit pedicels. Ectopic expression of BhGA2ox3 showed that BhGA2ox3 overexpression in Arabidopsis thaliana resulted in significantly shorter fruit pedicels. This study lays a theoretical foundation for the regulatory mechanism of FPL in wax gourds and molecular breeding.

Genomic resources, alongside the tools and expertise required to leverage them, are essential for the effective improvement of globally significant millet crop species. Millets are essential for global food security and nutrition, particularly in sub-Saharan Africa and South Asia. They are crucial in promoting nutrition, climate resilience, economic development, and cultural heritage. Despite their critical role, millets have historically received less investment in developing genomic resources than major cereals like wheat, maize, and rice. However, recent advancements in genomics, particularly next-generation sequencing technologies, offer unprecedented opportunities for rapid improvement in millet crops. This review paper provides an overview of the status of genomic resources in millets and in harnessing the recent opportunities in artificial intelligence to address challenges in millet crop improvement to boost productivity, nutrition, and end quality. It emphasizes the significance of genomics in tackling global food security issues and underscores the necessity for innovative breeding strategies to translate genomics and AI into effective breeding strategies for millets.

We revealed the neglected genetic relationships of resistance for six major wheat diseases and established a haploblock-based catalogue with novel forms of resistance by multi-trait haplotype characterisation. Genetic potential to improve multiple disease resistance was highlighted through haplotype stacking simulations. Wheat production is threatened by numerous fungal diseases, but the potential to breed for multiple disease resistance (MDR) mechanisms is yet to be explored. Here, significant global genetic correlations and underlying local genomic regions were identified in the Vavilov wheat diversity panel for six major fungal diseases, including biotrophic leaf rust (LR), yellow rust (YR), stem rust (SR), hemibiotrophic crown rot (CR), and necrotrophic tan spot (TS) and Septoria nodorum blotch (SNB). By adopting haplotype-based local genomic estimated breeding values, derived from an integrated set of 34,899 SNP and DArT markers, we established a novel haplotype catalogue for resistance to the six diseases in over 20 field experiments across Australia and Ethiopia. Haploblocks with high variances of haplotype effects in all environments were identified for three rusts, and pleiotropic haploblocks were identified for at least two diseases, with four haploblocks affecting all six diseases. Through simulation, we demonstrated that stacking optimal haplotypes for one disease could improve resistance substantially, but indirectly affected resistance for other five diseases, which varied depending on the genetic correlation with the non-target disease trait. On the other hand, our simulation results combining beneficial haplotypes for all diseases increased resistance to LR, YR, SR, CR, TS, and SNB, by up to 48.1%, 35.2%, 29.1%, 12.8%, 18.8%, and 32.8%, respectively. Overall, our results highlight the genetic potential to improve MDR in wheat. The haploblock-based catalogue with novel forms of resistance provides a useful resource to guide desirable haplotype stacking for breeding future wheat cultivars with MDR.

The photosynthetic phenotype of trees undergoes changes and interactions that reflect their abilities to exploit light energy. Environmental disturbances and genetic factors have been recognized as influencing these changes and interactions, yet our understanding of the underlying biological mechanisms remains limited, particularly in stochastic environments. Here, we developed a high-dimensional stochastic differential framework (HDSD) for the genome-wide mapping of quantitative trait loci (QTLs) that regulate competition or cooperation in environment-dependent phenotypes. The framework incorporates random disturbances into system mapping, a dynamic model that views multiple traits as a system. Not only does this framework describe how QTLs regulate a single phenotype, but also how they regulate multiple phenotypes and how they interact with each other to influence phenotypic variations. To validate the proposed model, we conducted mapping experiments using chlorophyll fluorescence phenotype data from Populus simonii. Through this analysis, we identified several significant QTLs that may play a crucial role in photosynthesis in stochastic environments, in which 76 significant QTLs have already been reported to encode proteins or enzymes involved in photosynthesis through functional annotation. The constructed genetic regulatory network allows for a more comprehensive analysis of the internal genetic interactions of the photosynthesis process by visualizing the relationships between SNPs. This study shows a new way to understand the genetic mechanisms that govern the photosynthetic phenotype of trees, focusing on how environmental stochasticity and genetic variation interact to shape their light energy utilization strategies.

The QTLs and candidate genes governing the multifoliolate phenotype were identified by combining linkage mapping with BSR-seq, revealing a possible interplay between genetics and the environment in soybean leaf development. Soybean, as a legume, is typified by trifoliolate leaves. Although multifoliolate leaves (compound leaves with more than three leaflets each) have been reported in soybean, including sporadic appearances in the first compound leaves in a recombinant inbred line (RIL) population from a cross between cultivated soybean C08 and wild soybean W05 from this study, the genetic basis of this phenomenon is still unclear. Here, we integrated quantitative trait locus (QTL) mapping with bulked segregant RNA sequencing (BSR-seq) to identify the genetic loci associated with the multifoliolate phenotype in soybean. Using linkage mapping, ten QTLs related to the multifoliolate trait were identified. Among these, a significant and major QTL, qMF-2-1 on chromosome 2 and consistently detected across biological replicates, explained more than 10% of the phenotypic variation. Together with BSR-seq analyses, which analyzed the RILs with the highest multifoliolate frequencies and those with the lowest frequencies as two distinct bulks, two candidate genes were identified: Glyma.06G204300 encoding the transcription factor TCP5, and Glyma.06G204400 encoding LONGIFOLIA 2 (LNG2). Transcriptome analyses revealed that stress-responsive genes were significantly differentially expressed between high-multifoliolate occurrence lines and low occurrence ones, indicating environmental factors probably influence the appearance of multifoliolate leaves in soybean through stress-responsive genes. Hence, this study offers new insights into the genetic mechanism behind the multifoliolate phenotype in soybean.

A major locus for spike compactness and length was mapped on chromosome 7H and its pleiotropic effects, candidate genes and transcriptional regulatory network were analyzed. Spike compactness (SC) and length (SL) are important traits of barley (Hordeum vulgare L.) due to their close association with grain yield. In this study, a major SC and SL locus QSc/Sl.cib-7H was primarily identified on chromosome 7H by bulked segregant analysis, and further fine mapped to a recombination cold spot expanding 244.36-388.09 Mb by developing a secondary population using residual heterozygous lines. This region is much more accurate than previously reported spike compactness loci on chromosome 7H. The strong effects of QSc/Sl.cib-7H on SL and SC were validated in two pair of near isogenic lines (NILs) and diverse genetic backgrounds. QSc/Sl.cib-7H exhibited pleiotropic effects on plant height (PH), thousand grain weight and grain length, and did not significantly influence the spikelet number of main spike (SMS) and grain width. Transcriptome analysis based on NILs showed that regulation of SC and SL might be related to the plant circadian rhythm pathway. The candidate genes were mined by analyzing variants and expression patterns of genes in the target region employing multiple genome and transcriptome data. This study takes a further step towards cloning of QSc/Sl.cib-7H, and the data obtained and the developed molecular markers will facilitate its utilization in barley breeding.

A major stable QTL, QGPC.caas-7AL, for grain protein content of wheat, was narrowed down to a 1.82-Mb inter on chromosome 7AL, and four candidate genes were predicated. Wheat grain protein content (GPC) is important for end-use quality. Identification of genetic loci for GPC is helpful to create new varieties with good processing quality and nutrients. Zhongmai 578 (ZM578) and Jimai 22 (JM22) are two elite wheat varieties with different contents of GPC. In the present study, 262 recombinant inbred lines (RILs) derived from a cross between ZM578 and JM22 were used to map the GPC with high-density wheat Illumina iSelect 50 K single-nucleotide polymorphism (SNP) array. Seven quantitative trait loci (QTLs) were identified for GPC on chromosomes 3AS, 3AL, 3BS, 4AL, 5BS, 5DL and 7AL by inclusive composite interval mapping, designated as QGPC.caas-3AS, QGPC.caas-3AL, QGPC.caas-3BS, QGPC.caas-4AL, QGPC.caas-5BS, QGPC.caas-5DL and QGPC.caas-7AL, respectively. Among these, alleles for increasing GPC at QGPC.caas-3AS, QGPC.caas-3BS, QGPC.caas-4AL and QGPC.caas-7AL loci were contributed by ZM578, whereas those at the other three loci were from JM22. The stable QTL QGPC.caas-7AL was fine mapped to a 1.82-Mb physical interval using secondary populations from six heterozygous recombinant plants obtained by selfing a residual RIL. Four genes were predicted as candidates of QGPC.caas-7AL based on sequence polymorphism and expression patterns. The near-isogenic lines (NILs) with the favorable allele at the QGPC.caas-7AL locus increased Farinograph stability time, Extensograph extension area, extensibility and maximum resistance by 19.6%, 6.3%, 6.0% and 20.3%, respectively. Kompetitive allele-specific PCR (KASP) marker for QGPC.caas-7AL was developed and validated in a diverse panel of 166 Chinese wheat cultivars. These results provide further insight into the genetic basis of GPC, and the fine-mapped QGPC.caas-7AL will be an attractive target for map-based cloning and marker-assisted selection in wheat breeding programs.

QKl/Tgw/Gns.yaas-2D associates with KL, TGW, and GNS, and QKl/Tgw.yaas-5A associates with KL and TGW. Significantly pleiotropic and additive effects of these two QTL were validated. The YM5 allele both at QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A was proved to be the best allelic combination for improving yield potential. Kernel length (KL), kernel width (KW), thousand grain weight (TGW), and grain number per spike (GNS) play important roles in the yield improvement of wheat. In this study, one recombinant inbred line (RIL) derived from a cross between Yangmai 5 (YM5) and Yanzhan 1 (YZ1) was used to identify quantitative trait loci (QTL) associated with KL, KW, TGW, and GNS across three years. Two pleiotropic QTL namely QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A were located in two genomic regions on chromosomes 2D and 5A, respectively. Breeder-friendly Kompetitive Allele-Specific PCR (KASP) markers for QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A were developed and validated in a set of 246 wheat cultivars/lines. Analysis of allelic combinations indicated that the YM5 allele both at QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A is probably the best one to promote TGW, GNS, and grain weight per spike. Based on the analysis of gene annotation, sequence variations, expression patterns, and GO enrichment, twenty-five and twenty-four candidate genes of QKl/Tgw/Gns.yaas-2D and QKl/Tgw.yaas-5A, respectively, were identified. These results provide the basis of fine-mapping the target QTL and marker-assisted selection in wheat yield-breeding programs.

Flowering time synchronizes reproductive development with favorable environmental conditions to optimize yield. Improved understanding of the genetic control of flowering will help optimize varietal adaptation to future agricultural systems under climate change. Here, we investigate the genetic basis of flowering time in winter wheat (Triticum aestivum L.) using an eight-founder multi-parent advanced generation intercross (MAGIC) population. Flowering time data was collected from field trials across six growing seasons in the United Kingdom, followed by genetic analysis using a combination of linear modelling, simple interval mapping and composite interval mapping, using either single markers or founder haplotype probabilities. We detected 57 quantitative trait loci (QTL) across three growth stages linked to flowering time, of which 17 QTL were identified only when the major photoperiod response locus Ppd-D1 was included as a covariate. Of the 57 loci, ten were identified using all genetic mapping approaches and classified as 'major' QTL, including homoeologous loci on chromosomes 1B and 1D, and 4A and 4B. Additional Earliness per se flowering time QTL were identified, along with growth stage- and year-specific effects. Furthermore, six of the main-effect QTL were found to interact epistatically with Ppd-D1. Finally, we exploited residual heterozygosity in the MAGIC recombinant inbred lines to Mendelize the Earliness per se QTL QFt.niab-5A.03, which was confirmed to modulate flowering time by at least four days. This work provides detailed understanding of the genetic control of phenological variation within varieties relevant to the north-western European wheat genepool, aiding informed manipulation of flowering time in wheat breeding.

Specific combinations of LFY and WAPO1 natural alleles maximize spikelet number per spike in wheat. Spikelet number per spike (SNS) is an important yield component in wheat that determines the maximum number of grains that can be formed in a wheat spike. In wheat, loss-of-function mutations in LEAFY (LFY) or its interacting protein WHEAT ORTHOLOG OF APO1 (WAPO1) significantly reduce SNS by reducing the rate of formation of spikelet meristems. In previous studies, we identified a natural amino acid change in WAPO1 (C47F) that significantly increases SNS in hexaploid wheat. In this study, we searched for natural variants in LFY that were associated with differences in SNS and detected significant effects in the LFY-B region in a nested association mapping population. We generated a large mapping population and confirmed that the LFY-B polymorphism R80S is linked with the differences in SNS, suggesting that LFY-B is the likely causal gene. A haplotype analysis revealed two amino acid changes P34L and R80S, which were both enriched during wheat domestication and breeding suggesting positive selection. We also explored the interactions between the LFY and WAPO1 natural variants for SNS using biparental populations and identified significant interaction, in which the positive effect of the 80S and 34L alleles from LFY-B was only detected in the WAPO-A1 47F background but not in the 47C background. Based on these results, we propose that the allele combination WAPO-A1-47F/LFY-B 34L 80S can be used in wheat breeding programs to maximize SNS and increase grain yield potential in wheat.

Climate change can limit yields of naturally resilient crops, like sorghum, challenging global food security. Agriculture under an erratic climate requires tapping into a reservoir of flexible adaptive loci that can lead to lasting yield stability under multiple abiotic stress conditions. Domesticated in the hot and dry regions of Africa, sorghum is considered a harsh crop, which is adapted to important stress factors closely related to climate change. To investigate the genetic basis of drought stress adaptation in sorghum, we used a multi-environment multi-locus genome-wide association study (MEML-GWAS) in a subset of a diverse sorghum association panel (SAP) phenotyped for performance both under well-watered and water stress conditions. We selected environments in Brazil that foreshadow agriculture where both drought and temperature stresses coincide as in many tropical agricultural frontiers. Drought reduced average grain yield (Gy) by up to 50% and also affected flowering time (Ft) and plant height (Ph). We found 15 markers associated with Gy on all sorghum chromosomes except for chromosomes 7 and 9, in addition to loci associated with phenology traits. Loci associated with Gy strongly interacted with the environment in a complex way, while loci associated with phenology traits were less affected by G × E. Studying environmental covariables potentially underpinning G × E, increases in relative humidity and evapotranspiration favored and disfavored grain yield, respectively. High temperatures influenced G × E and reduced sorghum yields, with a ~ 100 kg ha average decrease in grain yield for each unit increase in maximum temperature between 29 and 38 °C. Extreme G × E for sorghum stress resilience poses an additional challenge to breed crops for moving, erratic weather conditions.