Application of multi‐locus GWAS for the detection of bruchid resistance loci in cowpea (Vigna unguiculata)

Abstract

Identification of genes associated with bruchid resistance variations in cowpea accessions would help breeders to generate new cowpea cultivars with improved resistance and quality. In this work, 107 cowpea collections from various areas in six countries were phenotyped for their responses to Callosobruchus maculatus and genotyped with Single Nucleotide Polymorphism (SNP) markers. Six multi-locus models Genome Wide Association Study (mrMLM, FASTmrMLM, pKWmEB, pLARmEB, FASTmrEMMA and ISISEM-BLASSO) were used to associate the genotype data to phenotypic cowpea resistance traits: Percentage of Bruchid Emergence (PBE); Percentage of Weight Loss (PWL); Median Development Period (MDP); Dobie Susceptibility Index (DSI); Number of Egg Laid (NEL) and Mean Number of Hole (MNH).Out of 14 QTNs, three were associated with more than one trait and were associated with 11 candidate genes located within 10-30 kb of the QTNs. These candidate genes exhibit functionalities associated with cowpea resistance mechanisms. All these results could contribute to the gene networks in resistant cowpea varieties.The result of this study could also increase our knowledge of genetic resistance of cowpea to bruchids and could be useful for molecular breeding.

Single and multi‐trait genome‐wide association studies identify genomic regions associated with phenological traits in cowpea

“…Although GWAS has been used to investigate the genetic control of important traits in cowpea, including seed size, flowering time, black seed coat color, root architecture, pod length, and insect resistance (Burridge et al., 2017; Herniter et al., 2018; Kpoviessi et al., 2022; Lo et al., 2019; Muñoz‐Amatriaín et al., 2021; Paudel et al., 2021; Xu et al., 2017), this is the first study to use single and multi‐trait approaches to dissect the genetic architecture of phenological traits. The integration of single and multiple trait association analysis allowed the detection of new genomic regions involved in relevant phenological traits.…”

Genome-wide association mapping of bruchid resistance loci in soybean

“…Bruchids have high fecundity and the ability to re-infest, in addition to causing irreversible damage to soybeans (4); therefore, bruchid damage to soybean grain can lead to 100% yield losses (1), (3). To minimize yield losses to bruchids, farmers have been employing various management strategies including insecticides, botanically active plants, and cultural practices (5), (6). However, the majority of farmers tend to use insecticides for bruchid management (5).…”

“…This knowledge gap makes it difficult to implement marker-assisted selection (MAS) to combat the devastating impact of bruchids in Uganda and some parts of Sub-Saharan Africa that share the same genetic material. However, several studies have been carried out in other legumes aimed to determine quantitative trait loci (QTL) regions conferring bruchid resistance in cowpea using both bi-parental (13) and diverse (6), (12) populations and in common bean using bi-parental (14) and diverse (5) populations.…”

“…However, the use of bi-parental populations to identify QTLs has many drawbacks, including low resolution for QTL detection because only two parents contribute to the genetic information present in the mapping population, restricting its potential application in molecular plant breeding (6), (12), (15). To address this issue, genome-wide association studies (GWASs), a more robust contemporary mapping technique for identifying genomic regions and candidate genes linked with important traits in both self-pollinated and cross-pollinated crops, have been widely used (12).…”

“…To address this issue, genome-wide association studies (GWASs), a more robust contemporary mapping technique for identifying genomic regions and candidate genes linked with important traits in both self-pollinated and cross-pollinated crops, have been widely used (12). GWAS uses collections of lines with diverse genetic backgrounds and high historical recombination, resulting in more powerful QTL detection and better resolution (6), (12), (16). In soybean, GWAS has been used to identify genomic regions associated with resistance to many insect pests, including beet armyworm, potato leaf hopper, soybean looper, Mexican bean beetle, velvet caterpillar, and soybean aphid (17).…”