Association mapping to discover significant marker-trait associations for resistance against fusarium wilt variant 2 in pigeonpea [Cajanus cajan (L.) Millspaugh] using SSR markers

Single trait versus principal component based association analysis for flowering related traits in pigeonpea

Pigeonpea, a tropical photosensitive crop, harbors significant diversity for days to flowering, but little is known about the genes that govern these differences. Our goal in the current study was to use genome wide association strategy to discover the loci that regulate days to flowering in pigeonpea. A single trait as well as a principal component based association study was conducted on a diverse collection of 142 pigeonpea lines for days to first and fifty percent of flowering over 3 years, besides plant height and number of seeds per pod. The analysis used seven association mapping models (GLM, MLM, MLMM, CMLM, EMLM, FarmCPU and SUPER) and further comparison revealed that FarmCPU is more robust in controlling both false positives and negatives as it incorporates multiple markers as covariates to eliminate confounding between testing marker and kinship. Cumulatively, a set of 22 SNPs were found to be associated with either days to first flowering (DOF), days to fifty percent flowering (DFF) or both, of which 15 were unique to trait based, 4 to PC based GWAS while 3 were shared by both. Because PC1 represents DOF, DFF and plant height (PH), four SNPs found associated to PC1 can be inferred as pleiotropic. A window of ± 2 kb of associated SNPs was aligned with available transcriptome data generated for transition from vegetative to reproductive phase in pigeonpea. Annotation analysis of these regions revealed presence of genes which might be involved in floral induction like Cytochrome p450 like Tata box binding protein, Auxin response factors, Pin like genes, F box protein, U box domain protein, chromatin remodelling complex protein, RNA methyltransferase. In summary, it appears that auxin responsive genes could be involved in regulating DOF and DFF as majority of the associated loci contained genes which are component of auxin signaling pathways in their vicinity. Overall, our findings indicates that the use of principal component analysis in GWAS is statistically more robust in terms of identifying genes and FarmCPU is a better choice compared to the other aforementioned models in dealing with both false positive and negative associations and thus can be used for traits with complex inheritance.

Identification of significant marker-trait associations for Fusarium wilt resistance in a genetically diverse core collection of safflower using AFLP and SSR markers

Safflower (Carthamus tinctorius L.), an oilseed crop, is severely affected by Fusarium oxysporum f. sp. carthami (Foc), a fungus causing Fusarium wilt (FW) resulting in up to 80% yield loss. In the present study, we used a panel of 84 diverse accessions from the composite core collection to perform association mapping for FW-resistance. Hydroponics-based screening resulted in categorization of 84 accessions as 31 immune, 19 highly resistant, 9 moderately resistant, 4 moderately susceptible, and 21 highly susceptible. Genotyping with a combination of 155 AFLP and 144 SSR markers revealed substantial genetic differentiation and structure analysis identified three main subpopulations (K = 3) with nearly 35% of admixtures in the panel. Kinship analysis at individual and population level revealed absence of or weak relatedness between the accessions. Association mapping with General Linear Model and Mixed Linear Model identified 4 marker-trait associations (MTAs) significantly linked with the FW-resistance trait. Of these, 3 robust MTAs identified in both the models exhibited phenotypic variance ranging from 4.09 to 6.45%. Locus-128 showing a low P-value and high phenotypic variance was identified as a promising marker-trait association that will facilitate marker-assisted breeding for FW-resistance in safflower.

Genomic regions associated with resistance to Fusarium wilt in castor identified through linkage and association mapping approaches

Fusarium wilt, caused by Fusarium oxysporum f. sp. ricini, is the most destructive disease in castor. Host plant resistance is the best strategy for the management of wilt. Identification of molecular markers linked to wilt resistance will enhance the efficiency and effectiveness of breeding for wilt resistance. In the present study, genomic regions linked to wilt resistance were mapped using a bi-parental population of 185 F₆-RILs and a genetically diverse panel of 300 germplasm accessions. Quantitative trait loci (QTL) analysis performed using a linkage map consisting of 1090 SNP markers identified a major QTL on chromosome 7 with an LOD score of 18.7, which explained 44% of the phenotypic variance. The association mapping performed using genotypic data from 3465 SNP loci revealed 69 significant associations (p < 1 × 10^-4) for wilt resistance. The phenotypic variance explained by the individual SNPs ranged from 0.063 to 0.210. The QTL detected in the bi-parental mapping population was not identified in the association analysis. Thus, the results of this study indicate the possibility of vast gene diversity for Fusarium wilt resistance in castor.

Breeding, Genetics, and Genomics Approaches for Improving Fusarium Wilt Resistance in Major Grain Legumes

Fusarium wilt (FW) disease is the key constraint to grain legume production worldwide. The projected climate change is likely to exacerbate the current scenario. Of the various plant protection measures, genetic improvement of the disease resistance of crop cultivars remains the most economic, straightforward and environmental-friendly option to mitigate the risk. We begin with a brief recap of the classical genetic efforts that provided first insights into the genetic determinants controlling plant response to different races of FW pathogen in grain legumes. Subsequent technological breakthroughs like sequencing technologies have enhanced our understanding of the genetic basis of both plant resistance and pathogenicity. We present noteworthy examples of targeted improvement of plant resistance using genomics-assisted approaches. In parallel, modern functional genomic tools like RNA-seq are playing a greater role in illuminating the various aspects of plant-pathogen interaction. Further, proteomics and metabolomics have also been leveraged in recent years to reveal molecular players and various signaling pathways and complex networks participating in host-pathogen interaction. Finally, we present a perspective on the challenges and limitations of high-throughput phenotyping and emerging breeding approaches to expeditiously develop FW-resistant cultivars under the changing climate.

The landscape of microsatellites in the enset (Ensete ventricosum) genome and web-based marker resource development

Ensete ventricosum (Musaceae, enset) is an Ethiopian food security crop. To realize the potential of enset for rural livelihoods, further knowledge of enset diversity, genetics and genomics is required to support breeding programs and conservation. This study was conducted to explore the enset genome to develop molecular markers, genomics resources, and characterize enset landraces while giving insight into the organization of the genome. We identified 233 microsatellites (simple sequence repeats, SSRs) per Mbp in the enset genome, representing 0.28% of the genome. Mono- and di-nucleotide repeats motifs were found in a higher proportion than other classes of SSR-motifs. In total, 154,586 non-redundant enset microsatellite markers (EMM) were identified and 40 selected for primer development. Marker validation by PCR and low-cost agarose gel electrophoresis revealed that 92.5% were polymorphic, showing a high PIC (Polymorphism Information Content; 0.87) and expected heterozygosity (He = 0.79-0.82). In silico analysis of genomes of closely related species showed 46.86% of the markers were transferable among enset species and 1.90% were transferable to Musa. The SSRs are robust (with basic PCR methods and agarose gel electrophoresis), informative, and applicable in measuring enset diversity, genotyping, selection and potentially breeding. Enset SSRs are available in a web-based database at https://enset-project.org/EnMom@base.html (or https://enset.aau.edu.et/index.html , downloadable from Figshare).

Genomics-assisted breeding for pigeonpea improvement

The review outlines advances in pigeonpea genomics, breeding and seed delivery systems to achieve yield gains at farmers' field. Pigeonpea is a nutritious and stress-tolerant grain legume crop of tropical and subtropical regions. Decades of breeding efforts in pigeonpea have resulted in development of a number of high-yielding cultivars. Of late, the development of CMS-based hybrid technology has allowed the exploitation of heterosis for yield enhancement in this crop. Despite these positive developments, the actual on-farm yield of pigeonpea is still well below its potential productivity. Growing needs for high and sustainable pigeonpea yields motivate scientists to improve the breeding efficiency to deliver a steady stream of cultivars that will provide yield benefits under both ideal and stressed environments. To achieve this objective in the shortest possible time, it is imperative that various crop breeding activities are integrated with appropriate new genomics technologies. In this context, the last decade has seen a remarkable rise in the generation of important genomic resources such as genome-wide markers, high-throughput genotyping assays, saturated genome maps, marker/gene-trait associations, whole-genome sequence and germplasm resequencing data. In some cases, marker/gene-trait associations are being employed in pigeonpea breeding programs to improve the valuable yield and market-preferred traits. Embracing new breeding tools like genomic selection and speed breeding is likely to improve genetic gains. Breeding high-yielding pigeonpea cultivars with key adaptation traits also calls for a renewed focus on systematic selection and utilization of targeted genetic resources. Of equal importance is to overcome the difficulties being faced by seed industry to take the new cultivars to the doorstep of farmers.

Validation of QTLs for plant ideotype, earliness and growth habit traits in pigeonpea (Cajanus cajan Millsp.)

Pigeonpea productivity is greatly constrained by poor plant ideotype of existing Indian cultivars. Enhancing pigeonpea yield demands a renewed focus on restructuring the ideal plant type by using more efficient approaches like genomic tools. Therefore, the present study aims to identify and validate a set of QTLs/gene(s) presumably associated with various plant ideotype traits in pigeonpea. A total of 133 pigeonpea germplasms were evaluated along with four checks in the augmented design for various ideotype traits i.e. initiation of flowering (IF), days to 50% flowering (DFF), days to maturity (DM), plant height (PH), primary branches (PB), seeds per pod (SP) and pod length (PL). We observed significant genetic diversity in the germplasm lines for these traits. The genetic control of IF, DFF, DM and PH renders these traits suitable for detection of marker trait associations. By using residual maximum likelihood algorithm, we obtained appropriate variance-covariance structures for modeling heterogeneity, correlation of genetic effects and non-genetic residual effects. The estimates of genetic correlations indicated a strong association among earliness traits. The best linear unbiased prediction values were calculated for individual traits, and association analysis was performed in a panel of 95 diverse genotypes with 19 genic SSRs. Out of five QTL-flanking SSRs used here for validation, only ASSR295 could show significant association with FDR and Bonferroni corrections, and accounted for 15.4% IF, 14.2% DFF and 16.2% DM of phenotypic variance (PV). Remaining SSR markers (ASSR1486, ASSR206 and ASSR408) could not qualify false discovery rate (FDR) and Bonferroni criteria, hence declared as false positives. Additionally, we identified two highly significant SSR markers, ASSR8 and ASSR390 on LG 1 and LG 2, respectively. The SSR marker ASSR8 explained up to 22 and 11% PV for earliness traits and PB respectively, whereas ASSR390 controlled up to 17% PV for earliness traits. The validation and identification of new QTLs in pigeonpea across diverse genetic backgrounds brightens the prospects for marker-assisted selection to improve yield gains in pigeonpea.