Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-23T23:44:04.725Z Has data issue: false hasContentIssue false
  • English
  • Français

NAM population – a novel genetic resource for soybean improvement: development and characterization for yield and attributing traits

Published online by Cambridge University Press:  06 December 2019

Shivakumar Maranna Open the ORCID record for Shivakumar Maranna [Opens in a new window] ,
Giriraj Kumawat ,
Vennampally Nataraj ,
C. Gireesh Open the ORCID record for C. Gireesh [Opens in a new window] ,
Sanjay Gupta ,
Gyanesh K. Satpute ,
Milind B. Ratnaparkhe  and
Devendra P. Yadav
Shivakumar Maranna*
Affiliation:
ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, India
Giriraj Kumawat
Affiliation:
ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, India
Vennampally Nataraj
Affiliation:
ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, India
C. Gireesh
Affiliation:
ICAR-Indian Institute of Rice Research, Hyderabad, India
Sanjay Gupta
Affiliation:
ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, India
Gyanesh K. Satpute
Affiliation:
ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, India
Milind B. Ratnaparkhe
Affiliation:
ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, India
Devendra P. Yadav
Affiliation:
ICAR-Indian Institute of Soybean Research, Indore, Madhya Pradesh, India
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Nested association mapping (NAM) captures the best features of both linkage and association mapping and enables the high power and high resolution of quantitative trait locus mapping through joint linkage-association analysis. In the current study, NAM population was developed by hybridizing JS 335, a popular variety of central India with 20 diverse soybean genotypes. The parents used in the study have various traits of economic importance such as drought and water-logging tolerance, bacterial pustule and yellow mosaic virus resistance, wider adaptability, resistance to mechanical damage and higher yield potential. High variability in the F2 populations of 20 crosses for grain yield and days to maturity indicated scope for development of high-yielding varieties. Genetic variability studies, correlation, regression, principal component analysis (PCA) and genetic diversity analyses were carried out in 900 NAM-recombinant inbred lines (RILs) derived from 11 crosses. Correlation and regression analysis indicated a significant positive effect of biomass, pods/plant, harvest index, branches/plant, nodes/plant and plant height on grain yield. Genetic diversity analysis grouped 900 NAM-RILs into 10 clusters. PCA revealed first two principal components to explain 63.78% of total variation mostly contributed by grain yield, biomass and number of pods. The inbred lines developed in this study will serve as an elite soybean genetic resource in understanding the genetic architecture underlying different traits of economic significance.

Keywords

early maturity and water-logging toleranceF2 populationgenetic advanceharvest indexmapping populationNAM-RILs
Type
Research Article
Information
Plant Genetic Resources , Volume 17 , Issue 6 , December 2019 , pp. 545 - 553
Copyright
Copyright © NIAB 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aravind, J, Sankar, MS, Dhammaprakash, PW and Kaur, V (2018) augmentedRCBD: Analysis of Augmented Randomised Complete Block Designs. R package version 0.1.0, https://aravind-j.github.io/augmentedRCBD/.Google Scholar
Arunachalam, V (2017) Quantitative genetics for quality experimentation. Viva Books Private Limited, New Delhi. p. 154. ISBN: 978-81-309-1802-0.Google Scholar
Bajgain, P, Rouse, MN, Tsilo, TJ, Macharia, GK, Bhavani, S, Jin, Y and Anderson, JA (2016) Nested association mapping of stem rust resistance in wheat using genotyping by sequencing. PLoS ONE 11: e0155760.CrossRefGoogle ScholarPubMed
Bandillo, N, Raghavan, C, Pauline, AM, Sevilla, MAL, Irish, TL, Christine, JDE, Tung, C, Susan, M, Michael, T, Ramil, M, Singh, RK, Gregorio, G, Redona, G and Leung, H (2013) Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 6: 1.CrossRefGoogle ScholarPubMed
Besufikad, EG (2018) Genetic variability, heritability and expected genetic advance in soybean [Glycine max (L.) Merrill] genotypes. Agriculture, Forestry and Fisheries 7: 108–112.Google Scholar
Bouchet, S, Olatoye, MO, Marla, SR, Perumal, R, Tesso, T, Yu, J, Tuinstra, M and Morris, GP (2017) Increased power to dissect adaptive traits in global sorghum diversity using a nested association mapping population. Genetics 206: 573–585.CrossRefGoogle ScholarPubMed
Buckler, ES, Holland, JB, Bradbury, PJ, Acharya, CB, Brown, PJ, Browne, C, Ersoz, E, Flint-Garcia, S, Gracia, A, Galubitz, JC, Goodman, MM, Harjes, C, Guill, K, Kroon, DE, Lasson, S, Lepak, LK, Li, H, Mitchell, SE, Pressoir, G, Peiffer, JA, Rosas, MO, Rocheford, TR, Romay, MC, Romero, S, Salvo, S, Villeda, HS, Silva, HS, Sun, Q, Tian, F, Upadyayula, N, ware, D, Yates, H, Yu, J, Zhang, Z, Kresovich, S and Mc Mullen, MD (2009) The genetic architecture of maize flowering time. Science 325: 714–718.CrossRefGoogle ScholarPubMed
Buezoa, J, Sanz-Saezb, A, Jose, FM, David, S, Iker, A and Raquel, E (2019) Drought tolerance response of high-yielding soybean varieties to mild drought: physiological and photochemical adjustments. Physiologia Plantarum 166: 88–104.CrossRefGoogle Scholar
Chaudhary, DN and Singh, BB (1974) Heterosis in soybean. Indian Journal of Genetics and Plant Breeding 34: 69–74.Google Scholar
Cook, JP, McMullen, MD, Holland, JB, Tian, F, Bradbury, P, Ross-Ibarra, J, Buckler, ES and Flint-Gracia, SA (2012) Genetic architecture of maize kernel composition in the nested association mapping and inbred association panels. Plant Physiology 158: 824–834.CrossRefGoogle ScholarPubMed
Diers, BW, Specht, J, Katy, MR, Cregan, P, Song, Q, Ramasubramanian, V, Graef, G, Nelson, R, Schapaugh, W, Wang, D, Shannon, G, Mchale, L, Kantartzi, SK, Xavier, A, Mian, R, Stupar, RM, Michno, JM, An, YQC, Gottel, W, Ward, R, Fox, C, Lipka, AE, Hyten, D, Cary, T and Beavis, WD (2018) Genetic architecture of soybean yield and agronomic traits. G3: Genes, Genomes, Genetics 8: 3367–3375.CrossRefGoogle ScholarPubMed
Fragoso, CA, Moreno, M, Wang, Z, Heffelfinger, C, Arbelaez, LJ, Aguirre, JA, Franco, N, Romero, LE, Labadie, K, Zhao, H, Dellaporta, SL and Lorieux, M (2017) Genetic architecture of a rice nested association mapping population. G3: Genes, Genomes, Genetics 7: 1913–1926.CrossRefGoogle ScholarPubMed
Gadag, RN and Upadhyaya, HD (1995) Heterosis in soybean (Glycine max (l.) Merrill). Indian Journal of Genetics and Plant Breeding 55: 308–314.Google Scholar
Gesteira, GDS, Bruzi, AT, Zito, RK, Vanoli, F and Arantes, NE (2018) Selection of early soybean inbred lines using multiple indices. Crop Science 58: 2494–2502.CrossRefGoogle Scholar
Gireesh, C, Husain, SM, Shivakumar, M, Satpute, G, Kumawat, G, Arya, M, Agarwal, DK and Bhatia, VS (2015) Integrating principal component score strategy with power core method for development of core collection in Indian soybean germplasm. Plant Genetic Resources: Characterization and Utilization 15: 230–238.CrossRefGoogle Scholar
Hayes, HK, Immer, FF and Smith, DC (1955) Heterosis in hybrids In: Hayes, HK and Immer, FF (eds) Methods of Plant Breeding II. New York: McGraw-Hill, p. 551.Google Scholar
Huang, BE, George, AW, Forrest, KL, Kilian, A, Hayden, MJ, Morell, MK and Cavanagh, CR (2012) A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotechnology Journal 10: 826–839.CrossRefGoogle ScholarPubMed
ICAR (2009) Handbook of Agriculture. New Delhi: Indian Council of Agricultural Research, pp. 1143–1150.Google Scholar
Johnson, HW, Robinson, HF and Comstock, RE (1955) Estimates of genetic and environmental variability in soybeans. Agronomy Journal 47: 314–318.CrossRefGoogle Scholar
Jordan, DE, Mace, AC, Hunt, C and Henzell, R (2011) Exploring and exploiting genetic variation from unadapted sorghum germplasm in a breeding program. Crop Science 51: 1444–1457.CrossRefGoogle Scholar
Karikari, B, Chen, S, Xiao, Y, Chang, F, Zhou, Y, Kong, J, Akhter Bhat, J and Zhao, T (2019) Utilization of interspecific high-density genetic map of RIL population for the QTL detection and candidate gene mining for 100-seed weight in soybean. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2019.10:1001.CrossRefGoogle Scholar
Kassambara, A and Mundt, F (2017) Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.5. https://CRAN.R-project.org/package=factoextra.Google Scholar
Kong, F, Nan, H, Cao, D, Li, Y, Wu, F, Wang, J et al. (2014) A new dominant gene E9 conditions early flowering and maturity in soybean. Crop Science 54: 2529–2535.CrossRefGoogle Scholar
Kover, PX, Valdar, W, Trakalo, J, Scarcelli, N, Ehrenreich, IM, Purugganan, MD, Durrant, C and Mott, R (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genetics 5: e1000551.CrossRefGoogle ScholarPubMed
Le, S, Josse, J and Husson, F (2008) Factominer: an R package for multivariate analysis. Journal of Statistical Software 25: 1–18.CrossRefGoogle Scholar
Li, D, Pfeiffer, TW and Cornelius, PL (2007) Soybean QTL for yield and yield components associated with Glycine soja alleles. Crop Science 48: 571–581.CrossRefGoogle Scholar
Li, S, Cao, Y, He, J, Zhao, T and Gai, J (2017) Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure. Theoretical and Applied Genetics 130: 2297–2314.CrossRefGoogle Scholar
Li, D, Zhao, X, Han, Y, Li, W and Xie, F (2019) Genome-wide association mapping for seed protein and oil contents using a large panel of soybean accessions. Genomics 111: 90–95. https://doi.org/10.1016/j.ygeno.2018.01.004.CrossRefGoogle ScholarPubMed
Lush, JL (1940). Intra-sire correlations or regressions of offspring on dam as a method of estimating heritability of characteristics. Proceedings of the American Society of Animal Nutrition 1940: 293–301.Google Scholar
Mackay, IJ, Basler, PB, Barber, T, Alison, RB, Cockram, J, Gosman, N, Andy, JG, Horsnell, R, Howells, R, Donal, MO, Gemma, AR and Phil, JH (2014) An eight-parent multiparent advanced generation inter-cross population for winter-sown wheat: creation, properties and validation. G3 4: 1603–1610.CrossRefGoogle Scholar
McMullen, MD, Kresovich, S, Villeda, HS, Bradbury, P, Li, H, Sun, Q et al. (2009) Genetic properties of the maize nested association mapping population. Science 325: 737–740.CrossRefGoogle ScholarPubMed
Nidhi, D, Avinashe, HA and Shrivastava, AN (2018) Principal component analysis in advanced genotypes of soybean [Glycine max (L.) Merrill] over seasons. Plant Archives 18: 501–506.Google Scholar
Peiffer, JA, Flint-Garcia, SA, De Leon, N, McMullen, MD, Kaeppler, SM and Buckler, ES (2013) The genetic architecture of maize stalk strength. PLoS ONE 8: E67066.CrossRefGoogle ScholarPubMed
Perez, PT, Silvia, RC and Reid, GP (2009) Evaluation of Soybean [Glycine max (L.) Merr.] F1 Hybrids. Journal of Crop Improvement 23: 11–18.CrossRefGoogle Scholar
Peterson, BG and Carl, P (2018) PerformanceAnalytics: Econometric Tools for Performance and Risk Analysis. R package version 1.5.2. https://CRAN.R-project.org/package=PerformanceAnalytics.Google Scholar
Rana, C, Sharma, R, Tyagi, RK, Chahota, RK, Gautam, NK, Singh, M, Sharma, PN and Ojha, SN (2015) Characterisation of 4274 accessions of common bean (Phaseolus vulgaris L.) germplasm conserved in the Indian gene bank for phenological, morphological and agricultural traits. Euphytica 205: 441–457.CrossRefGoogle Scholar
Ren, Y, Hou, W, Lan, C, Basnet, BR, Singh, RP, Zhu, W, Cheng, X, Cui, D and Chen, F (2017) QTL analysis and nested association mapping for adult plant resistance to powdery mildew in two bread wheat populations. Frontiers in Plant Science 8: 1212.CrossRefGoogle ScholarPubMed
Sharma, MK, Mishra, S and Rana, NS (2009) Genetic divergence in French bean (Phaseolus vulgaris L.) pole type cultivars. Legume Research 32: 220–223.Google Scholar
Shivakumar, M, Basavaraja, GT, Salimath, PM, Patil, PV and Talukdar, A (2011) Identification of rust resistant lines and their genetic variability and character association studies in soybean [Glycine max (L.) Merr.]. Indian Journal of Genetics and Plant Breeding 71: 235–240.Google Scholar
Shivakumar, M, Gireesh, C and Talukdar, A (2016) Efficiency and utility of pollination without emasculation (PWE) method in intra and inter specific hybridization in soybean. Indian Journal of Genetics and Plant Breeding 76: 98–100.CrossRefGoogle Scholar
Shivakumar, M, Kumawat, G, Gireesh, C, Ramesh, SV and Husain, SM (2018) Soybean MAGIC population: a novel resource for genetics and plant breeding. Current science 114: 906–908.CrossRefGoogle Scholar
Shruti, K and Basavaraja, GT (2019) Genetic variability studies on yield and yield component traits of soybean. International Journal of Current Microbiology and Applied Sciences 8: 1269–1274.CrossRefGoogle Scholar
Song, Q, Yan, L, Quingley, C, Jordan, BD, Fickus, E, Schroeder, S, Song, BH, Charles, AYQ, Hyten, D, Nelson, R, Rainey, K, Beavis, WD, Specht, J, Diers, B and Cregan, P (2017) Genetic characterization of the soybean nested association mapping population. The Plant Genome 10. doi:10.3835/plantgenome2016.10.0109.CrossRefGoogle Scholar
Talukdar, A and Shivakumar, M (2012) Pollination without emasculation: an efficient method of hybridization in soybean (Glycine max Merr). Current Science 103: 628–630.Google Scholar
United States Department of Agriculture (USDA) (2019) Foreign Agriculture Service. World Agriculture Production, Table 11. Circular Series WAP 10-19 pp 24.Google Scholar
Wang, J, Chu, S, Zhang, H, Zhu, Y, Cheng, H and Yu, D (2016) Development and application of a novel genome-wide SNP array reveals domestication history in soybean. Scientific Reports 6: 20728.CrossRefGoogle Scholar
Wei, T and Simko, V (2017) R package ‘corrplot’: Visualization of a Correlation Matrix (Version 0.84). Available from https://github.com/taiyun/corrplot.Google Scholar
Supplementary material: File

Maranna et al. supplementary material

Maranna et al. supplementary material 1

Download Maranna et al. supplementary material(File)
File 67 KB
Supplementary material: File

Maranna et al. supplementary material

Maranna et al. supplementary material 2

Download Maranna et al. supplementary material(File)
File 991.3 KB