Advancing Crop Improvement Through GWAS and Beyond in Mung Bean

Overview

Journal Front Plant Sci

Date 2025 Jan 2

PMID 39744597

Authors

Syed Riaz Ahmed

Muhammad Jawad Asghar

Amjad Hameed

Maria Ghaffar

Muhammad Shahid

Affiliations

Soon will be listed here.

Abstract

Accessing the underlying genetics of complex traits, especially in small grain pulses is an important breeding objective for crop improvement. Genome-wide association studies (GWAS) analyze thousands of genetic variants across several genomes to identify links with specific traits. This approach has discovered many strong associations between genes and traits, and the number of associated variants is expected to continue to increase as GWAS sample sizes increase. GWAS has a range of applications like understanding the genetic architecture associated with phenotype, estimating genetic correlation and heritability, developing genetic maps based on novel identified quantitative trait loci (QTLs)/genes, and developing hypotheses related to specific traits in the next generation. So far, several causative alleles have been identified using GWAS which had not been previously detected using QTL mapping. GWAS has already been successfully applied in mung bean () to identify SNPs/alleles that are used in breeding programs for enhancing yield and improvement against biotic and abiotic factors. In this review, we summarize the recently used advanced genetic tools, the concept of GWAS and its improvement in combination with structural variants, the significance of combining high-throughput phenotyping and genome editing with GWAS, and also highlights the genetic discoveries made with GWAS. Overall, this review explains the significance of GWAS with other advanced tools in the future, concluding with an overview of the current and future applications of GWAS with some recommendations.

Citing Articles

Genotyping-by-sequencing derived SNP markers reveal genetic diversity and population structure of germplasm.

Altaf M, Cavagnaro P, Kokten K, Ali A, Morales A, Tatar M Front Plant Sci. 2025; 16:1530585.

PMID: 39980483 PMC: 11840758. DOI: 10.3389/fpls.2025.1530585.

An overview of heat stress in Chickpea ( L.): effects, mechanisms and diverse molecular breeding approaches for enhancing resilience and productivity.

Naveed M, Aslam M, Ahmed S, Tan D, De Mastro F, Tariq M Mol Breed. 2025; 45(2):18.

PMID: 39850651 PMC: 11751345. DOI: 10.1007/s11032-025-01538-4.

References

Speed D, Balding D . SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat Genet. 2018; 51(2):277-284. PMC: 6485398. DOI: 10.1038/s41588-018-0279-5. View

Zhang W, Zhao Y, Yang H, Liu Y, Zhang Y, Zhang Z . Comparison analysis of bioactive metabolites in soybean, pea, mung bean, and common beans: reveal the potential variations of their antioxidant property. Food Chem. 2024; 457:140137. DOI: 10.1016/j.foodchem.2024.140137. View

Lin Y, Chen H, Yeh P, Anand S, Lin J, Li J . Demographic history and distinct selection signatures of two domestication genes in mungbean. Plant Physiol. 2023; 193(2):1197-1212. DOI: 10.1093/plphys/kiad356. View

Yuan J, Kaur D, Zhou Z, Nagle M, Kiddle N, Doshi N . Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator. Plant Phenomics. 2022; 2022:9893639. PMC: 9394117. DOI: 10.34133/2022/9893639. View

Thabet S, Sallam A, Moursi Y, Karam M, Alqudah A . Genetic factors controlling nTiO nanoparticles stress tolerance in barley (Hordeum vulgare) during seed germination and seedling development. Funct Plant Biol. 2021; 48(12):1288-1301. DOI: 10.1071/FP21129. View

Benner C, Spencer C, Havulinna A, Salomaa V, Ripatti S, Pirinen M . FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016; 32(10):1493-501. PMC: 4866522. DOI: 10.1093/bioinformatics/btw018. View

Han X, Li L, Chen H, Liu L, Sun L, Wang X . Resequencing of 558 Chinese mungbean landraces identifies genetic loci associated with key agronomic traits. Front Plant Sci. 2022; 13:1043784. PMC: 9597495. DOI: 10.3389/fpls.2022.1043784. View

Howie B, Marchini J, Stephens M . Genotype imputation with thousands of genomes. G3 (Bethesda). 2012; 1(6):457-70. PMC: 3276165. DOI: 10.1534/g3.111.001198. View

Bayer P, Golicz A, Tirnaz S, Chan C, Edwards D, Batley J . Variation in abundance of predicted resistance genes in the Brassica oleracea pangenome. Plant Biotechnol J. 2018; 17(4):789-800. PMC: 6419861. DOI: 10.1111/pbi.13015. View

10.

Zhang H, Zhang Y, Yin H . Genome Editing with mRNA Encoding ZFN, TALEN, and Cas9. Mol Ther. 2019; 27(4):735-746. PMC: 6453514. DOI: 10.1016/j.ymthe.2019.01.014. View

11.

Rahaman M, Chen D, Gillani Z, Klukas C, Chen M . Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front Plant Sci. 2015; 6:619. PMC: 4530591. DOI: 10.3389/fpls.2015.00619. View

12.

Kichaev G, Yang W, Lindstrom S, Hormozdiari F, Eskin E, Price A . Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 2014; 10(10):e1004722. PMC: 4214605. DOI: 10.1371/journal.pgen.1004722. View

13.

Sunitha S, Rock C . CRISPR/Cas9-mediated targeted mutagenesis of TAS4 and MYBA7 loci in grapevine rootstock 101-14. Transgenic Res. 2020; 29(3):355-367. PMC: 7283210. DOI: 10.1007/s11248-020-00196-w. View

14.

Wu X, Sun T, Xu W, Sun Y, Wang B, Wang Y . Unraveling the Genetic Architecture of Two Complex, Stomata-Related Drought-Responsive Traits by High-Throughput Physiological Phenotyping and GWAS in Cowpea ( L. Walp). Front Genet. 2021; 12:743758. PMC: 8581254. DOI: 10.3389/fgene.2021.743758. View

15.

Maalouf F, Abou-Khater L, Babiker Z, Jighly A, Alsamman A, Hu J . Genetic Dissection of Heat Stress Tolerance in Faba Bean ( L.) Using GWAS. Plants (Basel). 2022; 11(9). PMC: 9103424. DOI: 10.3390/plants11091108. View

16.

Wu J, Wang L, Fu J, Chen J, Wei S, Zhang S . Resequencing of 683 common bean genotypes identifies yield component trait associations across a north-south cline. Nat Genet. 2019; 52(1):118-125. DOI: 10.1038/s41588-019-0546-0. View

17.

Bayer P, Golicz A, Scheben A, Batley J, Edwards D . Plant pan-genomes are the new reference. Nat Plants. 2020; 6(8):914-920. DOI: 10.1038/s41477-020-0733-0. View

18.

Sokolkova A, Burlyaeva M, Valiannikova T, Vishnyakova M, Schafleitner R, Lee C . Genome-wide association study in accessions of the mini-core collection of mungbean (Vigna radiata) from the World Vegetable Gene Bank (Taiwan). BMC Plant Biol. 2020; 20(Suppl 1):363. PMC: 7556912. DOI: 10.1186/s12870-020-02579-x. View

19.

Bird K, VanBuren R, Puzey J, Edger P . The causes and consequences of subgenome dominance in hybrids and recent polyploids. New Phytol. 2018; 220(1):87-93. DOI: 10.1111/nph.15256. View

20.

de Leeuw C, Mooij J, Heskes T, Posthuma D . MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015; 11(4):e1004219. PMC: 4401657. DOI: 10.1371/journal.pcbi.1004219. View