Multivariate analysis unravels genetic diversity and relationship between agronomic traits, protein, and dietary fiber in yardlong bean (Vigna unguiculata subsp. sesquipedalis Verdc.)
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Abstract. Widyawan MB, Hasanah A, Taryono, Alam T, Sayekti RS, Pramana AAC, Wulandari RA. 2020. Multivariate analysis unravels genetic diversity and relationship between agronomic traits, protein, and dietary fiber in yardlong bean (Vigna unguiculata subsp. sesquipedalis Verdc.). Biodiversitas 21: 5662-5671. Yardlong bean (Vigna unguiculata subsp. sesquipedalis Verdc.) is a vegetable legume that possesses the highest amount of protein and total dietary fiber (TDF) content among other vegetables. However, protein and TDF content diversity are found among yardlong bean genotypes. Genetic improvement to produce cultivars with high yield and improved nutritional content is required. This study aimed to analyze genetic diversity and identify the relationship between important agronomic traits from 21 yardlong bean genotypes assessed by multivariate statistical analysis. The experiment was arranged in alpha design with three replicates, each consist of six blocks. Six pod-related important agronomic traits, protein, and TDF content were observed in this study. Total pod weight, pod weight, and pod length were found to be negatively correlated with protein and TDF content based on correlation and principal component analysis (PCA). Although negatively correlated, several variables possess a large and positive direct effect on protein and TDF content, such as the number of pod, pod weight, and days to flower. D2 analysis and Tocher’s method classify genotypes used in this study into five clusters with diverse traits value. Information from this study laid a foundation for the improvement of important agronomic traits as well as nutritional content in yardlong bean.
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References
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Sanwal SK, Singh B, Singh B, Mann A. 2015. Multivariate analysis and its implication in breeding of desired plant type in garden pea (Pisum sativum). Indian J Agric Sci 85: 1298-1302.
Singh RK, Chaudary BD. 1985. Biometrical Methods In Quantitative Genetics Analysis. Kalyani Publishers, New Delhi.
Voss-Fels KP, Stahl A, Hickey LT. 2019. Q&A: modern crop breeding for future food security. BMC Biol 17: 1-7.
Vu VQ. 2020. A biplot based on ggplot2. https://github.com/vqv/ggbiplot.
Wills RBH, Wong AWK, Scriven FM, Greenfield H. 1994. Nutrient composition of Chinese vegetables. J Agric Food Chem 32: 413-416.
Xu P, Wu X, Wang B, Hu T, Lu Z, Liu Z, Qin D, Wang S, Li G. 2013. QTL mapping and epistatic interaction analysis in asparagus bean for severa; characterized and novel horticulturally important traits. BMC Genet 14: 1-10.
Akaike H. 1974. A new look at the statistical model identification. IEEE Trans Auto Cont 19: 716-723.
AOAC International. 1997. Official Methods of Analysis 16th ed. AOAC, Rockville.
Ayana A, Bekele E. 1999. Multivariate analysis of morphological variation in sorghum (Sorghum bicolor (L.) Moench) germplasm from Ethiopia and Eritrea. Genet Res Crop Evol 46: 273-284.
Castañeda-Pérez E, Jiménez-Morales K, Quintal-Novelo C, Moo-Puc R, Chel-Guerrero L, Betancur-Ancona D. 2019. Enzymatic protein hydrolysates and ultrafiltered peptide fractions from Cowpea Vigna unguiculata L bean with in vitro antidiabetic potential. J Iran Chem Soc 16: 1773-1781.
Chen C, Hu J, Liu W. 2007. Estimates of genetic parameters for fresh pod quality characters in asparagus bean (Vigna unguiculata (L.) ssp. sesquipedalis Verd.). J Jianghan Univ 2007(03).
Da Silva AR. 2017. Biotools: tools for biometry and applied statistics in agricultural science. Retrieved on April 13, 2020 from https://cran.r-project.org/web/packages/biotools/index.html.
De Mendiburu, F. 2020. Agricolae: statistical procedures for agricultural research. https://cran.r-project.org/web/packages/agricolae/index.html.
Fuller S, Beck E, Salman H, Tapsell L. 2016. New horizons for the study of dietary fiber and health: a review. Plant Food Hum Nut 71: 1-12.
Gaur, PM, Singh MK, Samineni S, Sajja SB, Jukanti AJ, Kamatam S, Varshney RK. 2016. Inheritance of protein content and its relationships with seed size, grain yield, and other traits in chickpea. Euphytica 209: 253-260.
Hair JF, Black WC, Babin BJ, Anderson RE. 2014. Multivariate Data Analysis. 7th Edition. Pearson Education Limited, London.
Haque MA, Timilsena YP, Adhikari B. 2016. Food proteins, structure, and function. Ref Mod Food Sci Elsevier. DOI: 10.1016/B978-0-08-100596-5.03057-2.
Hong Z, Fries A, Wu HX. 2014. High negative genetic correlations between growth traits and wood properties suggest incorporating multiple traits selection including economic weights for the future Scots pine breeding program. Ann For Sci 71: 463-472.
Hosni H, Periklis D, Baourkais G. 2017. Consumers attitude towards healthy food: organic and functional foods. Int J Food Bev Man Bus Mod 2: 85-99.
Kaiser HF. 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23: 187-200.
Kassambra A, Mundt F. 2020. Factoextra: extract and visualize the results of multivariate data analyses. https://cran.r-project.org/web/packages/factoextra/index.html.
Kumar B, Verma AK, Singh HP, Misra HO, Lal RK. 2008. Genetic divergence and correlations study in Chlorphytum borivilianum. J New Seed 9(4): 321-329.
Kumar K, Prasad Y, Mishra SB, Pandey SS, Kumar R. 2013. Study on genetic variability and correlation and path analysis with grain yield and yield attributing traits in green gram (Vigna radiate L. Wilczek). Bioscan 8: 1551-1555.
Kongjaimun A, Kaga A, Tomooka N, Somta P, Vaughan DA, and Srinives P. 2012. The genetics of domestication of yardlong bean, Vigna unguiculata (L.) Walp. ssp. unguiculata cv.-gr. sesquipedalis. Ann Bot 109: 1185-1200.
Lakshmi VGI, Sreedhar M, Vanisri S, Anantha MS, Rao LVS, Gireesh C. 2019. Multivariate analysis and selection criteria for identification of African rice (Oryza glaberrima) for genetic improvement of indica rice cultivars. Plant Genet Res 17(6): 499-505.
Li Y, Wei YC, Li ZQ, Wang SH, Chung L. 2014. Relationship between progeny growth performance and molecular marker-based genetic distances in Eucommia ulmoides parental genotypes. Genet Mol Res 13: 4736-4746.
Lin CY. 1978. Index selection for genetic improvement of quantitative characters. Theor App Genet 52: 49-56.
Liu Y, Wang Q, Li S, Yue Y, Ma Y, Ren G. 2018. Convenient food made of extruded adzuki bean attenuates inflammation and improves glycemic control in patients with type 2 diabetes: a randomized controlled trial. Ther Clin Risk Man 14: 871-884.
Mahender A, Anandan A, Pradan SK, Pandit E. 2016. Rice grain nutritional traits and their enhancement using relevant genes and QTLs through advanced approaches. Springerplus 5: 2086.
Mellidou I, Krommydas K, Nianiou-Obediat I, Ouzounidou G, Kalivas A, Ganopoulos I. 2020. Exploring morpho-physiological profiles of a collection of tomato (Solanum lycopersicum) germplasm using multivariate statistics. Plant Genet Res. DOI: doi.org/10.1017/S1479262120000088.
Missanjo E, Matsumura J. 2017. Multiple trait selection index for simultaneous improvement of wood properties and growth traits in Pinus kesiya Royle ex Gordon in Malawi. Forests 2017(8): 1-7.
Nooprom K, Santipracha Q. 2015. Effect of varieties on grwth and yield of yardlong bean under Songkhla conditions, Southern Thailand. Mod App Sci 9: 247-251.
Ofori K, Klogo PY. 2005. Optimum time for harvesting yardlong bean (Vigna sesquipedalis) for high yield and quality pods and seeds. J Agric Soc Sci 1: 86-88.
Oliveira GHF, Amaral CB, Silva FAM, Dutra SMF, Marconato MB, Moro GV. 2016. Mixed models and multivariate analysis for selection of superior maize genotypes. Chilean J Agric Res 76: 417-431.
Patel UV, Parmar VK, Patel PB, Malviya AV. 2016. Correlation and path analysis study in cowpea (Vigna unguiculata (L.) Walp). Int J Sci Env Tech 5: 3897-3904.
Pérez-Ramírez IF, Becerril-Ocampo LJ, Reynoso-Camacho R, Herrera MD, Guzmán-Maldonado SH, Cruz-Bravo RK. 2017. Cookies elaborated with oat and common bean flours improved serum markers in diabetic rats. J Sci Food Agric 98(3): 998-1007.
Portis E, Nervo G, Cavallanti F, Barchi L, Lanteri S. 2006. Multivariate analysis of genetic relationships between Italian pepper landraces. Plant Genet Res 6: 2517-2525.
R Core Team. 2017. R: a language and environment for statistical computing. https://www.R-project.org/.
Raivo K. 2019. Pheatmap: Pretty Heatmaps. https://cran.r-project.org/web/packages/pheatmap/index.html.
Rambabu, E, Redy KR, Kamala V, Saidaiah P, Pandravada SR. 2016. Morphological characterization of yardlong bean under exploited vegetable. Int J Sci Nat 7: 344-348.
Rambabu E, Redy KR, Kamala V, Saidaiah P, and Pandravada SR. 2016. Correlation and path analysis for quality, yield, and yield components in yardlong bean (Vigna unguiculata (L.) Walp. ssp. sesquipedalis Verdc.). Env Ecol 34: 1655-1661.
Sanwal SK, Singh B, Singh B, Mann A. 2015. Multivariate analysis and its implication in breeding of desired plant type in garden pea (Pisum sativum). Indian J Agric Sci 85: 1298-1302.
Singh RK, Chaudary BD. 1985. Biometrical Methods In Quantitative Genetics Analysis. Kalyani Publishers, New Delhi.
Voss-Fels KP, Stahl A, Hickey LT. 2019. Q&A: modern crop breeding for future food security. BMC Biol 17: 1-7.
Vu VQ. 2020. A biplot based on ggplot2. https://github.com/vqv/ggbiplot.
Wills RBH, Wong AWK, Scriven FM, Greenfield H. 1994. Nutrient composition of Chinese vegetables. J Agric Food Chem 32: 413-416.
Xu P, Wu X, Wang B, Hu T, Lu Z, Liu Z, Qin D, Wang S, Li G. 2013. QTL mapping and epistatic interaction analysis in asparagus bean for severa; characterized and novel horticulturally important traits. BMC Genet 14: 1-10.
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