Advancing crop improvement through GWAS and beyond in mung bean

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 (Vigna radiata) 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.

Keywords: GWAS; QTLs; high-throughput phenotyping; mung bean; structural variants.

Figure 1

(A) Selection of plant population based on the research objective. The plant population should support the hypothesis before the experiment such as if the trait of interest is plant height, then the population be variation for plant height. (B) Phenotypic data should be carefully collected from the targeted plant population. To avoid or minimize human errors during data collection, advanced high-throughput phenotyping tools must be used to collect data. (C) Advanced high-throughput phenotyping processing unit (combinations of different tools like camera and picture analysis software). (D) Genotyping refers to collecting genotypic data using advanced sequencing tools such as WES, WGS, and NGS. (E) Quality control involves different steps with wet laboratory work like DNA switches and genotyping calling and dry laboratory work like SNPs calling, principal components analysis (PCA), and population strata detection. (F) Detection of the causative or trait associated SNPs across different individuals using reference genome alignment, enhancing the resolution and completeness of genotypic data. The SNPs are represented in different colors (red, blue, green, yellow) to indicate varying physical distances from the causal mutation and to illustrate linkage disequilibrium (LD) decay patterns, where SNPs closer to the causal mutation may exhibit complete LD. (G) Using an appropriate model for testing genetic associations for each genetic variant, identification of the QTLs, INDELS, and SNPs associated with a trait of interest.

Figure 2

This illustration explains the steps and tools involved in performing GWAS in mung bean and other crops. The process begins with the collection of a genetically diverse plant population (e.g., bi-parental or mixed populations). Next, field trials are conducted, and phenotypic data for traits of interest is collected using high-throughput phenotyping (HTP) techniques. High-quality DNA is then extracted using Invitrogen kits, followed by sequencing with advanced platforms such as PacBio. Finally, various analytical tools are applied to identify the associated SNPs.

Figure 3

Illustration of different structural variants (SVs) that can be found across crop genomes and responsible for creating genetic variations that lead to genetic diversity. Structural variants (such as deletions, insertions, duplications and inversions) in combination with genome-wide association studies (GWAS) can detect hidden SNPs (associated with traits of interest) that remain undiscovered during GWAS analysis.

Figure 4

Distribution of some of the most important genes across chromosomes associated with different mung bean traits discovered through GWAS. The numbers on each chromosome (in second line) for example on chr1 (1-14), Chr2 (15-22), Chr3 (23-35) represent the number of genes present on the chromosome associated with the above traits ( Supplementary Table 1 ); SC (seed color), BR (bruchid resistance), CP (crude protein), DF (Days to flowering), FW (Fusarium wilt), HC (hypocotyl color), Fe (Iron), LRA (lateral root angle), LDM (leaf drop at maturity), LRT (Leaf related traits), LP (Lectin proteins), LEC (Root length distribution), MYMV (Mung bean yellow mosaic virus), PC (Phosphorus conc.), PCUE (P concentration and P utilization efficiency), P (Phosphorus), PH (Plant height), PC (Pod color), PL (Pod length), K (Potassium), P_S (Quality traits (Protein and starch), SS (Salinity stress), SCL (Seed coat luster), ST (Seed texture), SW (Seed weight), SPP (Seeds per pod), SD (Shoot development), TDW(Total Dry Weight), TPU (Total Phosphorus Uptake), YPP (yield per plant), Zn (Zinc).

Figure 5

A simultaneous representation of GWAS and genome editing. (A) General overview of CRISPR-Cas from gene selection to genome editing. (B) Phenotyping, genotyping, and identification of the causal loci(s)/allele(s) associated with particulate trait. (C) Genome editing of loci/alleles identified by GWAS for further validation of results using gene knockout strategy (D) Genome editing of loci/alleles identified by GWAS for further validation of results using gene HDR and NHEJ strategy (E) Genome editing of loci/alleles identified by GWAS for further validation of results using gene KO, HDR, NHEJ and deaminase strategy (F) CRISPR-Cas most reliable delivery methods (Agrobacterium and Bombardment).