Genome-wide association analysis of bean fly resistance and agro-morphological traits in common bean

Genome-wide association study of cassava brown streak disease resistance in cassava germplasm conserved in South America

Cassava (Manihot esculenta Crantz) is a vital carbohydrate source for over 800 million people globally, yet its production in East Africa is severely affected by cassava brown streak disease (CBSD). Genebanks, through ex-situ conservation, play a pivotal role in preserving crop diversity, providing crucial resources for breeding resilient and disease-resistant crops. This study genotyped 234 South American cassava accessions conserved at the CIAT genebank, previously phenotyped for CBSD resistance by an independent group, to perform a genome-wide association analysis (GWAS) to identify genetic variants associated with CBSD resistance. Our GWAS identified 35 single nucleotide polymorphism (SNP) markers distributed across various chromosomes, associated with disease severity or the presence/absence of viral infection. Markers were annotated within or near genes previously identified with functions related to pathogen recognition and immune response activation. Using the SNP candidates, we screened the world's largest cassava collection for accessions with a higher frequency of favorable genotypes, proposing 35 accessions with potential resistance to CBSD. Our results provide insights into the genetics of CBSD resistance and highlight the importance of genetic resources to equip breeders with the raw materials needed to develop new crop varieties resistant to pests and diseases.

Genome-Wide Association for Morphological and Agronomic Traits in Phaseolus vulgaris L. Accessions

Exploring genetic resources through genomic analyses has emerged as a powerful strategy to develop common bean (Phaseolus vulgaris L.) cultivars that are both productive and well-adapted to various environments. This study aimed to identify genomic regions linked to morpho-agronomic traits in Mesoamerican and Andean common bean accessions and to elucidate the proteins potentially involved in these traits. We evaluated 109 common bean accessions over three agricultural years, focusing on traits including the grain yield (YDSD), 100-seed weight (SW), number of seeds per pod (SDPD), number of pods per plant (PDPL), first pod insertion height (FPIH), plant height (PLHT), days to flowering (DF), and days to maturity (DPM). Using multilocus methods such as mrMLM, FASTmrMLM, FASTmrEMMA, ISIS EM-BLASSO, and pLARmEB, we identified 36 significant SNPs across all chromosomes (Pv01 to Pv11). Validating these SNPs and candidate genes in segregating populations is crucial for developing more productive common bean cultivars through marker-assisted selection.

Genome-wide association analysis of grain yield and Striga hermonthica and S. asiatica resistance in tropical and sub-tropical maize populations

BACKGROUND

Genetic improvement for Striga hermonthica (Sh) and S. asiatica (Sa) resistance is the most economical and effective control method to enhance the productivity of maize and other major cereal crops. Hence, identification of quantitative trait loci (QTL) associated with Striga resistance and economic traits will guide the pace and precision of resistance breeding in maize. The objective of this study was to undertake a genome-wide association analysis of grain yield and Sh and Sa resistance among tropical and sub-tropical maize populations to identify putative genetic markers and genes for resistance breeding. 126 maize genotypes were evaluated under controlled environment conditions using artificial infestation of Sh and Sa. The test genotypes were profiled for grain yield (GY), Striga emergence counts at 8 (SEC8) and 10 (SEC10) weeks after planting, and Striga damage rate scores at 8 (SDR8) and 10 (SDR10) weeks after planting. Population structure analysis and genome-wide association mapping were undertaken based on 16,000 single nucleotide polymorphism (SNP) markers.

RESULTS

A linkage disequilibrium (LD) analysis in 798,675 marker pairs revealed that 21.52% of pairs were in significant linkage (P < 0.001). Across the chromosomes, the LD between SNPs decayed below a critical level (r2 = 0.1) at a map distance of 0.19 Mbp. The genome-wide association study identified 50 significant loci associated with Sh resistance and 22 significant loci linked to Sa resistance, corresponding to 39 and 19 candidate genes, respectively.

CONCLUSION

The study found non-significant QTL associated with dual resistance to the two examined Striga species Some of the detected genes reportedly conditioned insect and pathogen resistance, plant cell development, variable senescence, and pollen fertility. The markers detected in the present study for Sa resistance were reported for the first time. The gene Zm00001eb219710 was pleiotropic, and conditioned GY and SEC10, while Zm00001eb165170 affected SDR8 and SDR10, and Zm00001eb112030 conditioned SDR8 and SDR10 associated with Sh resistance. The candidate genes may facilitate simultaneous selection for Sh and Sa resistance and grain yield in maize after further validation and introgression in breeding pipelines. Overall, we recommend breeding maize specifically for resistance to each Striga species using germplasm adapted to the endemic region of each parasite.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

High-density DArTSeq SNP markers revealed wide genetic diversity and structured population in common bean (Phaseolus vulgaris L.) germplasm in Ethiopia

INTRODUCTION

Common bean is one of the widely consumed food security crop in Africa, Asia, and South America. Understanding genetic diversity and population structure is crucial for designing breeding strategies.

MATERIALS

Two hundred and eighty-nine germplasm were recently collected from different regions of Ethiopia and introduced from CIAT to estimate genetic diversity and population structure using 11,480 DArTSeq SNP markers.

RESULTS

The overall mean genetic diversity and polymorphic information content (PIC) were 0.38 and 0.30, respectively, suggested the presence of adequate genetic diversity among the genotypes. Among the geographical regions, landraces collected from Oromia showed the highest diversity (0.39) and PIC (0.30). The highest genetic distance was observed between genotypes collected from SNNPR and CIAT (0.49). In addition, genotypes from CIAT were genetically more related to improved varieties than the landraces which could be due to sharing of parents in the improvement process. The analysis of molecular variance revealed that the largest proportion of variation was due to within the population both in geographical region (63.67%) and breeding status (61.3%) based classification. Model-based structure analysis delineated the 289 common bean genotypes into six hypothetical ancestoral populations.

CONCLUSIONS

The genotypes were not clustered based on geographical regions and they were not the main drivers for the differentiation. This indicated that selection of the parental lines should be based on systematic assessment of the diversity rather than geographical distance. This article provides new insights into the genetic diversity and population structure of common bean for association studies, designing effective collection and conservation for efficient utilization for the improvement of the crop.

Combination of meta-analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean

Increasing seed yield in common bean could help to improve food security and reduce malnutrition globally due to the high nutritional quality of this crop. However, the complex genetic architecture and prevalent genotype by environment interactions for seed yield makes increasing genetic gains challenging. The aim of this study was to identify the most consistent genomic regions related with seed yield components and phenology reported in the last 20 years in common bean. A meta-analysis of quantitative trait locus (QTL) for seed yield components and phenology (MQTL-YC) was performed for 394 QTL reported in 21 independent studies under sufficient water and drought conditions. In total, 58 MQTL-YC over different genetic backgrounds and environments were identified, reducing threefold on average the confidence interval (CI) compared with the CI for the initial QTL. Furthermore, 40 MQTL-YC identified were co-located with 210 SNP peak positions reported via genome-wide association (GWAS), guiding the identification of candidate genes. Comparative genomics among these MQTL-YC with MQTL-YC reported in soybean and pea allowed the identification of 14 orthologous MQTL-YC shared across species. The integration of MQTL-YC, GWAS, and comparative genomics used in this study is useful to uncover and refine the most consistent genomic regions related with seed yield components for their use in plant breeding.

Genetic Augmentation of Legume Crops Using Genomic Resources and Genotyping Platforms for Nutritional Food Security

Recent advances in next generation sequencing (NGS) technologies have led the surge of genomic resources for the improvement legume crops. Advances in high throughput genotyping (HTG) and high throughput phenotyping (HTP) enable legume breeders to improve legume crops more precisely and efficiently. Now, the legume breeder can reshuffle the natural gene combinations of their choice to enhance the genetic potential of crops. These genomic resources are efficiently deployed through molecular breeding approaches for genetic augmentation of important legume crops, such as chickpea, cowpea, pigeonpea, groundnut, common bean, lentil, pea, as well as other underutilized legume crops. In the future, advances in NGS, HTG, and HTP technologies will help in the identification and assembly of superior haplotypes to tailor the legume crop varieties through haplotype-based breeding. This review article focuses on the recent development of genomic resource databases and their deployment in legume molecular breeding programmes to secure global food security.

Genomics Associated Interventions for Heat Stress Tolerance in Cool Season Adapted Grain Legumes

Cool season grain legumes occupy an important place among the agricultural crops and essentially provide multiple benefits including food supply, nutrition security, soil fertility improvement and revenue for farmers all over the world. However, owing to climate change, the average temperature is steadily rising, which negatively affects crop performance and limits their yield. Terminal heat stress that mainly occurred during grain development phases severely harms grain quality and weight in legumes adapted to the cool season, such as lentils, faba beans, chickpeas, field peas, etc. Although, traditional breeding approaches with advanced screening procedures have been employed to identify heat tolerant legume cultivars. Unfortunately, traditional breeding pipelines alone are no longer enough to meet global demands. Genomics-assisted interventions including new-generation sequencing technologies and genotyping platforms have facilitated the development of high-resolution molecular maps, QTL/gene discovery and marker-assisted introgression, thereby improving the efficiency in legumes breeding to develop stress-resilient varieties. Based on the current scenario, we attempted to review the intervention of genomics to decipher different components of tolerance to heat stress and future possibilities of using newly developed genomics-based interventions in cool season adapted grain legumes.