A genome-wide association study reveals region associated with seed protein content in cowpea

Authors: CHEN, YILIN - University Of Arkansas; XIONG, HAIZHENG - University Of Arkansas; RAVELOMBOLA, WALTRAM - Texas A&M Agrilife; BHATTARAI, GEHENDRA - University Of Arkansas; BARICKMAN, CASEY - Mississippi State University; ALATAWI, IBTISAM - University Of Arkansas; MAKAWA PHIRI, THERESA - University Of Arkansas; CHIWINA, KENANI - University Of Arkansas; Mou, Beiquan; Tallury, Shyamalrau - Shyam; SHI, AINONG - University Of Arkansas

Submitted to: Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/17/2023
Publication Date: 7/20/2023
Citation: Chen, Y., Xiong, H., Ravelombola, W., Bhattarai, G., Barickman, C., Alatawi, I., Phiri, T.M., Chiwina, K., Mou, B., Tallury, S., Shi, A. 2023. A genome-wide association study reveals region associated with seed protein content in cowpea. Plants. 12(14). Article 2705. https://doi.org/10.3390/plants12142705.
DOI: https://doi.org/10.3390/plants12142705

Interpretive Summary: Cowpea (Vigna unguiculata L. Walp.) has 22 chromosomes and is a protein-rich crop that complements staple cereals for humans and as fodder for livestock. It is widely grown in Africa and other developing countries as the primary source of protein in the diet, therefore it is necessary to identify the protein related genes to improve cowpea breeding. In the current study, we conducted a genetic study of 161 cowpea varieties (151 from USDA and ten from University of Arkansas) with a wide range of seed protein contents (21.8%~28.9%) to identify DNA markers associated with protein content for future breeding work. A total of seven DNA markers were identified by five different methods of analyses at the same spot on chromosome 8 for seed protein content. This chromosome spot was associated with a gene family playing a critical function of protein content increase and nutritional quality improvement. In this study, we were able to predict the protein content in cowpea through genetic analyses. These findings have practical implications for breeders seeking to predict the selection accuracy of complex traits such as seed protein content in cowpea. Moreover, this approach may be implemented early in the cowpea breeding process to expedite the breeding cycle.

Technical Abstract: Cowpea (Vigna unguiculata L. Walp. 2n = 2x = 22) is a protein-rich crop that complements staple cereals for humans and as fodder for livestock. It is widely grown in Africa and other developing countries as the primary source of protein in the diet, therefore it is necessary to identify the protein related loci to improve cowpea breeding. In the current study, we conducted a genome-wide association study (GWAS) by 161 cowpea accessions (151 USDA germplasm plus ten Arkansas breeding lines) with a wide range of seed protein contents (21.8%~28.9%) with 110,155 high-quality whole genome single nucleotide polymorphism (SNP) to identify markers associated with protein content then performed genomic prediction (GP) for future breeding. Total seven significant SNP markers were identified by five GWAS models single marker regression (SMR), general linear model (GLM), mixed linear model (MLM), fixed and Random Model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) then located at the same locus on chromosome 8 for seed protein content. This locus was associated with the gene: Vigun08g039200 which was annotated as the protein of thioredoxin superfamily playing a critical function of protein content increase and nutritional quality improvement. In this study, a genomic prediction (GP) approach was employed to assess the accuracy of predicting seed protein content in cowpea. The GP was conducted using cross-prediction with five models, namely ridge regression best linear unbiased prediction (rrBLUP), Bayesian ridge regression (BRR), Bayesian A (BA), Bayesian B (BB), and Bayesian least absolute shrinkage and selection operator (BL), applied to seven random whole genome marker sets with different densities (10k, 5k, 2k, 1k, 500, 200 and 7), as well as significant markers identified through GWAS. The accuracies of GP varied between 42.9% and 52.1% across the seven SNPs considered, depending on the model used. These findings have practical implications for breeders seeking to predict the selection accuracy of complex traits such as seed protein content in cow