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Kenani Chiwina

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Chiwina K, Xiong H, Bhattarai G, Dickson RW, Phiri TM, Chen Y, Alatawi I, Dean D, Joshi NK, Chen Y, Riaz A, Gepts P, Brick M, Byrne PF, Schwartz H, Ogg JB, Otto K, Fall A, Gilbert J, Shi A. Genome-Wide Association Study and Genomic Prediction of Fusarium Wilt Resistance in Common Bean Core Collection. Int J Mol Sci 2023;24:15300. [PMID: 37894980 PMCID: PMC10607830 DOI: 10.3390/ijms242015300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/29/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open

Affiliation(s)

Kenani Chiwina Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
Haizheng Xiong Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
Gehendra Bhattarai Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
Ryan William Dickson Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
Theresa Makawa Phiri Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
Yilin Chen Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
Ibtisam Alatawi Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
Derek Dean Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)
Neelendra K. Joshi Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA;
Yuyan Chen Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA;
Awais Riaz Department of Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA;
Paul Gepts Department of Plant Sciences, University of California, 1 Shields Avenue, Davis, CA 95616, USA;
Mark Brick Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
Patrick F. Byrne Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
Howard Schwartz Department of Agricultural Biology, Colorado State University, Fort Collins, CO 80523, USA; (H.S.); (K.O.)
James B. Ogg Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
Kristin Otto Department of Agricultural Biology, Colorado State University, Fort Collins, CO 80523, USA; (H.S.); (K.O.)
Amy Fall Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
Jeremy Gilbert Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA; (M.B.); (P.F.B.); (J.B.O.); (A.F.); (J.G.)
Ainong Shi Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA; (K.C.); (G.B.); (R.W.D.); (T.M.P.); (Y.C.); (I.A.); (D.D.)

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Chen Y, Xiong H, Ravelombola W, Bhattarai G, Barickman C, Alatawi I, Phiri TM, Chiwina K, Mou B, Tallury S, Shi A. A Genome-Wide Association Study Reveals Region Associated with Seed Protein Content in Cowpea. Plants (Basel) 2023;12:2705. [PMID: 37514320 PMCID: PMC10383739 DOI: 10.3390/plants12142705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/16/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]

Abstract

Cowpea (Vigna unguiculata L. Walp., 2n = 2x = 22) is a protein-rich crop that complements staple cereals for humans and serves 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) on 161 cowpea accessions (151 USDA germplasm plus 10 Arkansas breeding lines) with a wide range of seed protein contents (21.8~28.9%) with 110,155 high-quality whole-genome single-nucleotide polymorphisms (SNPs) to identify markers associated with protein content, then performed genomic prediction (GP) for future breeding. A total of seven significant SNP markers were identified using five GWAS models (single-marker regression (SMR), the 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), which are 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 the thioredoxin superfamily, playing a critical function for 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 (10 k, 5 k, 2 k, 1 k, 500, 200, and 7), as well as significant markers identified through GWAS. The accuracies of the GP varied between 42.9% and 52.1% across the seven SNPs considered, depending on the model used. These findings not only have the potential to expedite the breeding cycle through early prediction of individual performance prior to phenotyping, but also offer practical implications for cowpea breeding programs striving to enhance seed protein content and nutritional quality.

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