Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat.

KEY MESSAGE: The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

When do autopolyploids need poly-sequencing data?

The sequencing depth required to genotype autopolyploid populations is a very controversial topic. Different studies have adopted variable depth values without a clear guide on the optimal sequencing depth value. Many studies suggest high depth thresholds for different ploidies that may not be practical and substantially increase the overall genotyping cost for different projects. However, such conservative thresholds may not be required to achieve the most common research goals. In fact, some recent reports in the field of quantitative genetics found that much lower sequencing depth thresholds could achieve the same accuracy as high depth thresholds. In this manuscript, I discuss when researchers need to use stringent sequencing depth thresholds and when they can use more relaxed ones. I support my argument by calculating the probabilities of sampling different homologues at a given sequencing depth. I also discuss the uses and the uncertainty in calculating a continuous allelic dosage as the proportion of sequencing reads that hold the alternative allele, which is becoming a common method now in quantitative genetics to replace discrete dosage estimation.

MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics.

BACKGROUND: Meta-analysis describes a category of statistical methods that aim at combining the results of multiple studies to increase statistical power by exploiting summary statistics. Different industries that use genomic prediction do not share their raw data due to logistic or privacy restrictions, which can limit the size of their reference populations and creates a need for a practical meta-analysis method. RESULTS: We developed a meta-analysis, named MetaGS, that duplicates the results of multi-trait best linear unbiased prediction (mBLUP) analysis without accessing raw data. MetaGS exploits the correlations among different populations to produce more accurate population-specific single nucleotide polymorphism (SNP) effects. The method improves SNP effect estimations for a given population depending on its relations to other populations. MetaGS was tested on milk, fat and protein yield data of Australian Holstein and Jersey cattle and it generated very similar genomic estimated breeding values to those produced using the mBLUP method for all traits in both breeds. One of the major difficulties when combining SNP effects across populations is the use of different variants for the populations, which limits the applications of meta-analysis in practice. We solved this issue by developing a method to impute missing summary statistics without using raw data. Our results showed that imputing summary statistics can be done with high accuracy (r > 0.9) even when more than 70% of the SNPs were missing with a minimal effect on prediction accuracy. CONCLUSIONS: We demonstrated that MetaGS can replace the mBLUP model when raw data cannot be shared, which can lead to more flexible collaborations compared to the single-trait BLUP model.

Integrating genomic selection with a genotype plus genotype x environment (GGE) model improves prediction accuracy and computational efficiency.

Genotype-by-environment interaction (GEI) is one of the major factors affecting the prediction accuracy of genomic selection (GS) models. Standard models have low power to model complex GEI, and they fail to predict phenotypes in unobserved environments. Here, we developed a novel prediction model that account for GEI, named 3GS, that combines genotype plus genotype × environment (GGE) analysis with GS. The model calculates the principal components (PCs) of the environmental phenotypes using GGE analysis and predict the performance of these PCs using standard GS models before converting the GEBVs of these PCs (pcGEBVs) back to the original phenotypes. We demonstrated three advantages of the new model. First, 3GS showed significantly higher prediction accuracy primarily for deviated environments that have low to negative correlations to other environments. Second, 3GS can predict new genotypes in unobserved environments with high accuracy. Third, the computational complexity of 3GS increases linearly with increasing the number of environments and the population size, unlike the standard models that exhibit exponential increase, making it hundreds of times faster than the standard models for large data sets. 3GS can improve prediction accuracy with minimal resources in modern breeding programmes in which massive populations get multi-environment phenotypes with high-throughput techniques.

Population-dependent reproducible deviation from natural bread wheat genome in synthetic hexaploid wheat.

Sequence elimination is one of the main mechanisms that increases the divergence among homoeologous chromosomes after allopolyploidization to enhance the stability of recently established lineages, but it can cause a loss of some economically important genes. Synthetic hexaploid wheat (SHW) is an important source of genetic variation to the natural hexaploid wheat (NHW) genepool that has low genetic diversity. Here, we investigated the change between SHW and NHW genomes by utilizing a large germplasm set of primary synthetics and synthetic derivatives. Reproducible segment elimination (RSE) was declared if a large chromosomal chunk (>5 cM) produced no aligned reads in more than five SHWs. RSE in five genomic regions was the major source of variation between SHW and NHW. One RSE eliminated almost the complete short arm of chromosome 1B, which contains major genes for flour quality, disease resistance and different enzymes. The occurrence of RSE was highly dependent on the choice of diploid and tetraploid parental lines, their ancestral subpopulation and admixture, e.g. SHWs derived from Triticum dicoccon or from one of two Aegilops tauschii subpopulations were almost free of RSE, while highly admixed parents had higher RSE rates. The rate of RSE in synthetic derivatives was almost double that in primary synthetics. Genome-wide association analysis detected four loci with minor effects on the occurrence of RSE, indicating that both parental lines and genetic factors were affecting the occurrence of RSE. Therefore, pre-pre-breeding strategies should be applied before introducing SHW into pre-breeding programs to ensure genomic stability and avoid undesirable gene loss.

The Role of Microsatellites in Streptophyta Gene Evolution.

Microsatellites form hotspot regions for recombination. In this research, we investigated whether genic microsatellites can be responsible for generating new genes by enhancing crossover between gene containing microsatellites and other genomic regions. We tested our hypothesis on 33,531 UniGene entries containing microsatellites. Each sequence was divided into microsatellites upstream and downstream fragments, and each pair of sequences was compared to study the microsatellites effect. The candidate pairs of genes are supposed to share a high similar fragment in one side of the microsatellites, while the other fragments should be completely different. This in silico approach detected 448 valid pairs of sequences in which both of them showed semi-resemblance nature. The synteny analysis for the detected sequences against 55 plant genomes indicated low representation of them across plant kingdom. Our results will add a body of knowledge toward understanding the role of microsatellites in gene evolution.

Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments.

KEY MESSAGE: We identified 27 stable loci associated with agronomic traits in spring wheat using genome-wide association analysis, some of which confirmed previously reported studies. GWAS peaks identified in regions where no QTL for grain yield per se has been mapped to date, provide new opportunities for gene discovery and creation of new cultivars with desirable alleles for improving yield and yield stability in wheat. We undertook large-scale genetic analysis to determine marker-trait associations (MTAs) underlying agronomic and physiological performance in spring wheat using genome-wide association studies (GWAS). Field trials were conducted at seven sites in three countries (Sudan, Egypt, and Syria) over 2-3 years in each country. Twenty-five agronomic and physiological traits were measured on 188 wheat genotypes. After correcting for population structure and relatedness, a total of 245 MTAs distributed over 66 loci were associated with agronomic traits in individual and mean performance across environments respectively; some of which confirmed previously reported loci. Of these, 27 loci were significantly associated with days to heading, thousand kernel weight, grain yield, spike length, and leaf rolling for mean performance across environments. Despite strong QTL by environment interactions, eight of the loci on chromosomes 1A, 1D, 5A, 5D, 6B, 7A, and 7B had pleiotropic effects on days to heading and yield components (TKW, SM-2, and SNS). The winter-type alleles at the homoeologous VRN1 loci significantly increased days to heading and grain yield in optimal environments, but decreased grain yield in heat prone environments. Top 20 high-yielding genotypes, ranked by additive main effects and multiplicative interaction (AMMI), had low kinship relationship and possessed 4-5 favorable alleles for GY MTAs except two genotypes, Shadi-4 and Qafzah-11/Bashiq-1-2. This indicated different yield stability mechanisms due to potentially favorable rare alleles that are uncharacterized. Our results will enable wheat breeders to effectively introgress several desirable alleles into locally adapted germplasm in developing wheat varieties with high yield stability and enhanced heat tolerance.

Genome-wide DArT and SNP scan for QTL associated with resistance to stripe rust (Puccinia striiformis f. sp. tritici) in elite ICARDA wheat (Triticum aestivum L.) germplasm.

KEY MESSAGE: Identified DArT and SNP markers including a first reported QTL on 3AS, validated large effect APR on 3BS. The different genes can be used to incorporate stripe resistance in cultivated varieties. Stripe rust [yellow rust, caused by Puccinia striiformis f. sp. tritici (Pst)] is a serious disease in wheat (Triticum aestivum). This study employed genome-wide association mapping (GWAM) to identify markers linked to stripe rust resistance genes using Diversity Arrays Technology (DArT(®)) and single-nucleotide polymorphism (SNP) Infinium 9K assays in 200 ICARDA wheat genotypes, phenotyped for seedling and adult plant resistance in two sites over two growing seasons in Syria. Only 25.8 % of the genotypes showed resistance at seedling stage while about 33 and 44 % showed moderate resistance and resistance response, respectively. Mixed-linear model adjusted for false discovery rate at p < 0.05 identified 12 DArT and 29 SNP markers on chromosome arms 3AS, 3AL, 1AL, 2AL, 2BS, 2BL, 3BS, 3BL, 5BL, 6AL, and 7DS significantly linked to Pst resistance genes. Of these, the locus on 3AS has not been previously reported to confer resistance to stripe rust in wheat. The QTL on 3AS, 3AL, 1AL, 2AL, and 2BS were effective at seedling and adult plant growth stages while those on 3BS, 3BL, 5BL, 6AL and 7DS were effective at adult plant stage. The 3BS QTL was validated in Cham-6 × Cham-8 recombinant inbred line population; composite interval analysis identified a stripe resistance QTL flanked by the DArT marker, wPt-798970, contributed by Cham-6 parent which accounted for 31.2 % of the phenotypic variation. The DArT marker "wPt-798970" lies 1.6 cM away from the 3BS QTL detected within GWAM. Epistatic interactions were also investigated; only the QTL on 1AL, 3AS and 6AL exhibited interactions with other loci. These results suggest that GWAM can be an effective approach for identifying and improving resistance to stripe rust in wheat.

Molecular evaluation of orphan Afghan common wheat (Triticum aestivum L.) landraces collected by Dr. Kihara using single nucleotide polymorphic markers.

BACKGROUND: Landraces are an important source of genetic diversity in common wheat, but archival collections of Afghan wheat landraces remain poorly characterised. The recent development of array based marker systems, particularly single nucleotide polymorphism (SNP) markers, provide an excellent tool for examining the genetic diversity of local populations. Here we used SNP analysis to demonstrate the importance of Afghan wheat landraces and found tremendous genetic diversity and province-specific characteristics unique to this geographic region. RESULTS: A total of 446 Afghan wheat landraces were analysed using genotype by sequencing (GBS) arrays containing ~10 K unique markers. Pair-wise genetic distance analyses revealed significant genetic distances between landraces, particularly among those collected from distanced provinces. From these analyses, we were able to divide the landraces into 14 major classes, with the greatest degree of diversity evident among landraces isolated from Badakhshan province. Population-based analyses revealed an additional 15 sub-populations within our germplasm, and significant correlations were evident in both the provincial and botanical varieties. Genetic distance analysis was used to identify differences among provinces, with the strongest correlations seen between landraces from Herat and Ghor province, followed closely by those between Badakhshan and Takhar provinces. This result closely resembles existing agro-climatic zones within Afghanistan, as well as the wheat varieties commonly cultivated within these regions. Molecular variance analysis showed a higher proportion of intra-province variation among landraces compared with variation among all landraces as a whole. CONCLUSION: The SNP analyses presented here highlight the importance and genetic diversity of Afghan wheat landraces. Furthermore, these data strongly refute a previous analysis that suggested low genetic diverse within this germplasm. Ongoing analyses include phenotypic characterisation of these landraces to identify functional traits associated with individual genotypes. Taken together, these analyses can be used to help improve wheat cultivation in Afghanistan, while providing insights into the evolution and selective pressures underlying these distinct landraces.

Boosting genome-wide association power and genomic prediction accuracy for date palm fruit traits with advanced statistics.

The date palm is economically vital in the Middle East and North Africa, providing essential fibres, vitamins, and carbohydrates. Understanding the genetic architecture of its traits remains complex due to the tree's perennial nature and long generation times. This study aims to address these complexities by employing advanced genome-wide association (GWAS) and genomic prediction models using previously published data involving fruit acid content, sugar content, dimension, and colour traits. The multivariate GWAS model identified seven QTL, including five novel associations, that shed light on the genetic control of these traits. Furthermore, the research evaluates different genomic prediction models that considered genotype by environment and genotype by trait interactions. While colour- traits demonstrate strong predictive power, other traits display moderate accuracies across different models and scenarios aligned with the expectations when using small reference populations. When designing the cross-validation to predict new individuals, the accuracy of the best multi-trait model was significantly higher than all single-trait models for dimension traits, but not for the remaining traits, which showed similar performances. However, the cross-validation strategy that masked random phenotypic records (i.e., mimicking the unbalanced phenotypic records) showed significantly higher accuracy for all traits except acid contents. The findings underscore the importance of understanding genetic architecture for informed breeding strategies. The research emphasises the need for larger population sizes and multivariate models to enhance gene tagging power and predictive accuracy to advance date palm breeding programs. These findings support more targeted breeding in date palm, improving productivity and resilience to various environments.

Identification of novel genes associated with herbicide tolerance in Lentil (Lens culinaris ssp. culinaris Medik.).

Weeds pose a major constraint in lentil cultivation, leading to decrease farmers' revenues by reducing the yield and increasing the management costs. The development of herbicide tolerant cultivars is essential to increase lentil yield. Even though herbicide tolerant lines have been identified in lentils, breeding efforts are still limited and lack proper validation. Marker assisted selection (MAS) can increase selection accuracy at early generations. Total 292 lentil accessions were evaluated under different dosages of two herbicides, metribuzin and imazethapyr, during two seasons at Marchouch, Morocco and Terbol, Lebanon. Highly significant differences among accessions were observed for days to flowering (DF) and maturity (DM), plant height (PH), biological yield (BY), seed yield (SY), number of pods per plant (NP), as well as the reduction indices (RI) for PH, BY, SY and NP. A total of 10,271 SNPs markers uniformly distributed along the lentil genome were assayed using Multispecies Pulse SNP chip developed at Agriculture Victoria, Melbourne. Meta-GWAS analysis was used to detect marker-trait associations, which detected 125 SNPs markers associated with different traits and clustered in 85 unique quantitative trait loci. These findings provide valuable insights for initiating MAS programs aiming to enhance herbicide tolerance in lentil crop.

Two major chromosome evolution events with unrivaled conserved gene content in pomegranate.

Pomegranate has a unique evolutionary history given that different cultivars have eight or nine bivalent chromosomes with possible crossability between the two classes. Therefore, it is important to study chromosome evolution in pomegranate to understand the dynamics of its population. Here, we de novo assembled the Azerbaijani cultivar "Azerbaijan guloyshasi" (AG2017; 2n = 16) and re-sequenced six cultivars to track the evolution of pomegranate and to compare it with previously published de novo assembled and re-sequenced cultivars. High synteny was observed between AG2017, Bhagawa (2n = 16), Tunisia (2n = 16), and Dabenzi (2n = 18), but these four cultivars diverged from the cultivar Taishanhong (2n = 18) with several rearrangements indicating the presence of two major chromosome evolution events. Major presence/absence variations were not observed as >99% of the five genomes aligned across the cultivars, while >99% of the pan-genic content was represented by Tunisia and Taishanhong only. We also revisited the divergence between soft- and hard-seeded cultivars with less structured population genomic data, compared to previous studies, to refine the selected genomic regions and detect global migration routes for pomegranate. We reported a unique admixture between soft- and hard-seeded cultivars that can be exploited to improve the diversity, quality, and adaptability of local pomegranate varieties around the world. Our study adds body knowledge to understanding the evolution of the pomegranate genome and its implications for the population structure of global pomegranate diversity, as well as planning breeding programs aiming to develop improved cultivars.

Adaptability and Stability of Faba Bean (Vicia faba L.) Accessions under Diverse Environments and Herbicide Treatments.

The adaptability and stability of 37 faba bean (Vicia faba L.) accessions with different levels of tolerance to metribuzin or imazethapyr was assessed across 12 season-location-herbicide experiments. Significant Genotype x environment (GE) interaction was found for the days to flowering (DFLR), plant height (PLHT) and grain yield (GY). Performance and stability of the accessions regarding PLHT and GY were assessed using four different stability parameters: cultivar superiority, static stability, Wricke's eco-valence and Finlay and Wilkinson's regression model. The stability parameters ranked these genotypes differently suggesting that PLHT and GY stability should be assessed not only on a single or a few stability parameters but on a combination of them. GGE biplot analysis indicated that the environments representing metribuzin treatment at Marchouch 2014-2015 and the non-treated treatment at Terbol 2018-2019 are the ideal environments for evaluating faba bean genotypes. GGE biplots showed herbicide tolerant accession IG12983 with simultaneous average PLHT, GY and stability across the environments. The performance of other tolerant accessions, namely IG13945, IG13906, IG106453, FB2648, and FB1216 was less stable but superior under specific mega environments. Therefore, utilizing these accessions in faba bean breeding programs would help broaden the adaptability to diverse locations-season-herbicide treatments.

Genome-Wide Association Study of Root-Lesion Nematodes Pratylenchus Species and Crown Rot Fusarium culmorum in Bread Wheat.

Triticum aestivum L., also known as common wheat, is affected by many biotic stresses. Root diseases are the most difficult to tackle due to the complexity of phenotypic evaluation and the lack of resistant sources compared to other biotic stress factors. Soil-borne pathogens such as the root-lesion nematodes caused by the Pratylenchus species and crown rot caused by various Fusarium species are major wheat root diseases, causing substantial yield losses globally. A set of 189 advanced spring bread wheat lines obtained from the International Maize and Wheat Improvement Center (CIMMYT) were genotyped with 4056 single nucleotide polymorphisms (SNP) markers and screened for root-lesion nematodes and crown rot resistance. Population structure revealed that the genotypes could be divided into five subpopulations. Genome-Wide Association Studies were carried out for both resistances to Pratylenchus and Fusarium species. Based on our results, 11 different SNPs on chromosomes 1A, 1B, 2A, 3A, 4A, 5B, and 5D were significantly associated with root-lesion nematode resistance. Seven markers demonstrated association with P. neglectus, while the remaining four were linked to P. thornei resistance. In the case of crown rot, eight different markers on chromosomes 1A, 2B, 3A, 4B, 5B, and 7D were associated with Fusarium crown rot resistance. Identification and screening of root diseases is a challenging task; therefore, the newly identified resistant sources/genotypes could be exploited by breeders to be incorporated in breeding programs. The use of the identified markers in marker-assisted selection could enhance the selection process and cultivar development with root-lesion nematode and crown rot resistance.

Genetic Dissection of Heat Stress Tolerance in Faba Bean (Vicia faba L.) Using GWAS.

Heat waves are expected to become more frequent and intense, which will impact faba bean cultivation globally. Conventional breeding methods are effective but take considerable time to achieve breeding goals, and, therefore, the identification of molecular markers associated with key genes controlling heat tolerance can facilitate and accelerate efficient variety development. We phenotyped 134 accessions in six open field experiments during summer seasons at Terbol, Lebanon, at Hudeiba, Sudan, and at Central Ferry, WA, USA from 2015 to 2018. These accessions were genotyped using genotyping by sequencing (GBS), and 10,794 high quality single nucleotide polymorphisms (SNPs) were discovered. These accessions were clustered in one diverse large group, although several discrete groups may exist surrounding it. Fifteen lines belonging to different botanical groups were identified as tolerant to heat. SNPs associated with heat tolerance using single-trait (ST) and multi-trait (MT) genome-wide association studies (GWASs) showed 9 and 11 significant associations, respectively. Through the annotation of the discovered significant SNPs, we found that SNPs from transcription factor helix-loop-helix bHLH143-like S-adenosylmethionine carrier, putative pentatricopeptide repeat-containing protein At5g08310, protein NLP8-like, and photosystem II reaction center PSB28 proteins are associated with heat tolerance.

Genomic regions associated with herbicide tolerance in a worldwide faba bean (Vicia faba L.) collection.

Weeds represent one of the major constraints for faba bean crop. The identification of molecular markers associated with key genes imparting tolerance to herbicides can facilitate and fasten the efficient and effective development of herbicide tolerant cultivars. We phenotyped 140 faba bean genotypes in three open field experiments at two locations in Lebanon and Morocco against three herbicide treatments (T1 metribuzin 250 g ai/ha; T2 imazethapyr 75 g ai/ha; T3 untreated) and one in greenhouse where T1 and T3 were applied. The same set was genotyped using genotyping by sequencing (GBS) which yield 10,794 high quality single nucleotide polymorphisms (SNPs). ADMIXTURE software was used to infer the population structure which revealed two ancestral subpopulations. To identify SNPs associated with phenological and yield related traits under herbicide treatments, Single-trait (ST) and Multi-trait (MT) Genome Wide Association Studies (GWAS) were fitted using GEMMA software, showing 10 and 14 highly significant associations, respectively. Genomic sequences containing herbicide tolerance associated SNPs were aligned against the NCBI database using BLASTX tool using default parameters to annotate candidate genes underlying the causal variants. SNPs from acidic endochitinase, LRR receptor-like serine/threonine-protein kinase RCH1, probable serine/threonine-protein kinase NAK, malate dehydrogenase, photosystem I core protein PsaA and MYB-related protein P-like were significantly associated with herbicide tolerance traits.

Genome-wide association analysis of chickpea germplasms differing for salinity tolerance based on DArTseq markers.

Soil salinity is significant abiotic stress that severely limits global crop production. Chickpea (Cicer arietinum L.) is an important grain legume that plays a substantial role in nutritional food security, especially in the developing world. This study used a chickpea population collected from the International Center for Agricultural Research in the Dry Area (ICARDA) genebank using the focused identification of germplasm strategy. The germplasm included 186 genotypes with broad Asian and African origins and genotyped with 1856 DArTseq markers. We conducted phenotyping for salinity in the field (Arish, Sinai, Egypt) and greenhouse hydroponic experiments at 100 mM NaCl concentration. Based on the performance in both hydroponic and field experiments, we identified seven genotypes from Azerbaijan and Pakistan (IGs: 70782, 70430, 70764, 117703, 6057, 8447, and 70249) as potential sources for high salinity tolerance. Multi-trait genome-wide association analysis (mtGWAS) detected one locus on chromosome Ca4 at 10618070 bp associated with salinity tolerance under hydroponic and field conditions. In addition, we located another locus specific to the hydroponic system on chromosome Ca2 at 30537619 bp. Gene annotation analysis revealed the location of rs5825813 within the Embryogenesis-associated protein (EMB8-like), while the location of rs5825939 is within the Ribosomal Protein Large P0 (RPLP0). Utilizing such markers in practical breeding programs can effectively improve the adaptability of current chickpea cultivars in saline soil. Moreover, researchers can use our markers to facilitate the incorporation of new genes into commercial cultivars.

Major QTL with pleiotropic effects controlling time of leaf budburst and flowering-related traits in walnut (Juglans regia L.).

Breeding studies in walnut (Juglans regia L.) are usually time consuming due to the long juvenile period and therefore, this study aimed to determine markers associated with time of leaf budburst and flowering-related traits by performing a genome-wide association study (GWAS). We investigated genotypic variation and its association with time of leaf budburst and flowering-related traits in 188 walnut accessions. Phenotypic data was obtained from 13 different traits during 3 consecutive years. We used DArT-seq for genotyping with a total of 33,519 (14,761 SNP and 18,758 DArT) markers for genome-wide associations to identify marker underlying these traits. Significant correlations were determined among the 13 different traits. Linkage disequilibrium decayed very quickly in walnut in comparison with other plants. Sixteen quantitative trait loci (QTL) with major effects (R2 between 0.08 and 0.23) were found to be associated with a minimum of two phenotypic traits each. Of these QTL, QTL05 had the maximum number of associated traits (seven). Our study is GWAS for time of leaf budburst and flowering-related traits in Juglans regia L. and has a strong potential to efficiently implement the identified QTL in walnut breeding programs.