Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-10T20:18:22.810Z Has data issue: false hasContentIssue false
  • English
  • Français

Genome-wide marker-trait association analysis in a core set of Dolichos bean germplasm

Published online by Cambridge University Press:  27 July 2018

P. V. Vaijayanthi ,
S. Ramesh ,
M. B. Gowda ,
A. M. Rao  and
C. M. Keerthi
P. V. Vaijayanthi*
Affiliation:
Department of Genetics and Plant Breeding, University of Agricultural Sciences (UAS), Bengaluru 560 065, Karnataka, India
S. Ramesh
Affiliation:
Department of Genetics and Plant Breeding, University of Agricultural Sciences (UAS), Bengaluru 560 065, Karnataka, India
M. B. Gowda
Affiliation:
AICRP on Pigeon pea, ZARS, UAS, Bengaluru 560 065, Karnataka, India
A. M. Rao
Affiliation:
Department of Genetics and Plant Breeding, University of Agricultural Sciences (UAS), Bengaluru 560 065, Karnataka, India
C. M. Keerthi
Affiliation:
Department of Genetics and Plant Breeding, University of Agricultural Sciences (UAS), Bengaluru 560 065, Karnataka, India
*
*Corresponding author. E-mail: pvvaijayanthi@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Association mapping (AM), an alternative method of quantitative trait loci (QTL) discovery, exploits historic linkage disequilibrium (LD) present in natural populations. AM is effective in self-pollinated crops such as Dolichos bean as LD extends over longer genomic distance driven-by low rate of recombination and thereby requiring fewer markers for exploring marker-traits associations. A core set of Dolichos bean germplasm consisting of 64 accessions was evaluated for nine quantitative traits (QTs) during 2014 and 2015 rainy seasons and genotyped using 234 simple sequence repeats (SSR) markers. Substantial diversity was observed among the core set accessions at loci controlling QTs and 95 of the 234 SSR markers were found polymorphic. The structure analysis and low magnitude of fixation indices suggested weak population structure, which in-turn indicated the low possibility of false discovery rates in the marker-QTs association. The marker allele's scores were regressed onto phenotypes at nine QTs following general linear model and mixed linear model for exploring marker-QTs associations. Significantly higher number of SSR markers was found associated with genomic regions controlling nine QTs. A few of the markers such as KT Dolichos (KTD) 200 for days to 50% flowering, KTD 273 for fresh pod yield per plant and KTD 130 for fresh pods per plant explained ≥10% of the trait variations. The study could also identify a few SSR markers such as KTD 273, KTD 271 and KTD 130 linked to multiple traits. These linked SSR markers are suggested for validation for their use in marker-assisted Dolichos bean improvement programmes.

Keywords

association mappingdiversityGWASLab labMLM
Type
Research Article
Information
Plant Genetic Resources , Volume 17 , Issue 1 , February 2019 , pp. 1 - 11
Copyright
Copyright © NIAB 2018 

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

Adebisi, AA and Bosch, CH (2004) Lablab purpureus (L.) sweet. In: Grubben, GJH and Denton, OA (eds) Plant Resources of Tropical Africa (PROTA), No. 2, Vegetables. Wageningen, The Netherlands: PROTA Foundation, pp. 343–348.Google Scholar
Agrama, HA, Eizenga, GC and Yan, W (2007) Association mapping of yield and its components in rice cultivars. Molecular Breeding 19: 341–356.Google Scholar
Allard, RW (1999) Principles of Plant Breeding, 2nd edn. New York, USA: John Wiley and Sons, INC, p. 81.Google Scholar
Basavarajappa, PS and Byregowda, M (2000) Genetic divergence among field bean (Lablab purpureus L. Sweet) cultivars of Southern Karnataka. Indian Journal of Plant Genetic Resources 3: 134–137.Google Scholar
Blair, MW, Diaz, LM, Buendia, HF and Duque, MC (2009) Genetic diversity, seed size associations and population structure of a core collection of common beans (Phaseolus vulgaris L.). Theoretical and Applied Genetics 119: 955–972.Google Scholar
Borah, P and Shadeque, A (1992) Studies on genetic variability of common Dolichos bean. Indian Journal of Horticulture 49: 270–273.Google Scholar
Borba, TCO, Brondani, RPV, Breseghello, FA, Coelho, SG, Mendonca, JA, Rangel, PHN and Brondani, C (2010) Association mapping for yield and grain quality traits in rice (Oryza sativa L.). Genetics and Molecular Biology 33: 515–524.Google Scholar
Bradbury, P, Zhang, Z, Kroon, DE, Terry, MC, Yogesh, R and Buckler, ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23: 2633–2635.Google Scholar
Braulio, JS, Axel, D, Raja, R and Sylvie, C (2013) Genetic characterization of a core collection of flax (Linum usitatissimuml.) suitable for association mapping studies and evidence of divergent selection between fiber and linseed types. BMC Plant Biology 13: 78.Google Scholar
Breseghello, F and Sorrells, ME (2006) Association analysis as a strategy for improvement of quantitative traits in plants. Crop Science 46: 1323–1330.Google Scholar
Burton, G and De Vane, EH (1953) Estimating heritability in tall Festuca (Festuca arundinaceae) from replicated clonal material. Agronomy Journal 45: 478–481.Google Scholar
Byregowda, M, Gireesh, G, Ramesh, S, Mahadevu, P and Keerthi, CM (2015) Descriptors of dolichos bean (Lablab purpureus L.). Journal of Food Legumes 28: 203–214.Google Scholar
Campbell, BT, Baenziger, PS, Gill, KS, Eskridge, KM, Budak, H, Erayman, M and Yen, Y (2003) Identification of QTLs and environmental interactions associated with agronomic traits on chromosome 3A of wheat. Crop Science 43: 1493–1505.Google Scholar
Doerge, RW (2002) Mapping and analysis of quantitative trait loci in experimental populations. Nature Reviews Genetics 3: 43–52.Google Scholar
Doyle, JJ and Doyle, JL (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bulletin 19: 11–15.Google Scholar
Ebert, AW (2014) Potential of underutilized traditional vegetables and legume crops to contribute to food and nutritional security, income and more sustainable production systems. Sustainability 6: 319–335.Google Scholar
Engle, LM and Altoveris, NC (2000) Collection, conservation and utilization of indigenous vegetables. In: Proceedings of a workshop, Asian Vegetable Research and Development Centre (AVRDC), Shanhua, Taiwan, 16–18 August 1999, pp. 142.Google Scholar
Evanno, GG, Regnaut, S and Goudet, J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Molecular Ecology 14: 2611–2620.Google Scholar
Excoffier, L, Smouse, PE and Quattro, JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131: 479–491.Google Scholar
Excoffier, L, Laval, G and Schneider, S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1: 47–50.Google Scholar
Falush, D, Stephens, M and Pritchard, JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 1567–1587.Google Scholar
Falush, D, Stephens, M and Pritchard, JK (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles. Molecular Ecology 7: 574–578.Google Scholar
Federer, WT (1956) Augmented (or hoonuiaku) designs. Hawaii Plant Research 2: 191–208.Google Scholar
Fuller, DQ (2003) African crops in prehistoric South Asia: a critical review. In: Neumann, K, Butler, A and Kahlheber, S (eds) Food, Fuel, Fields – Progress in African Archaeobotany. Köln, Germany: Heinrich-Barth-Institute, Africa Prehistorica 15: 239–271.Google Scholar
Gupta, P, Rustgi, S and Kulwal, P (2005) Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Molecular Biology 57: 461–485.Google Scholar
Holland, JB (2007) Genetic architecture of complex traits in plants. Current Opinion in Plant Biology 10: 156–161.Google Scholar
Jadhav, AA, Rayate, SJ, Mhase, LB, Thudi, M, Chitikineni, A, Harer, PN, Jadhav, AS, Varshney, RK and Kulwal, PL (2015) Marker-trait association study for protein content in chickpea (Cicer arietinum L.). Journal of Genetics 94: 279–286.Google Scholar
Kim, KW, Chung, HK, Cho, GT, Ma, KH, Chandrabalan, D, Gwag, JG, Kim, TS, Cho, EG and Park, YJ (2007) Power core: a programme applying the advanced M strategy with a heuristic search for establishing core sets. Bioinformatics 23: 2155–2162.Google Scholar
Koppolu, R, Upadhyaya, HD, Dwivedi, SL, Hoisington, DA and Vershney, RK (2010) Genetic relationships among seven sections of genus Arachis studied by using SSR markers. BMC Plant Biology 10: 15–21.Google Scholar
Kottapalli, KR, Burow, MD, Burow, G, Burke, J and Puppala, N (2007) Molecular characterization of the U.S. Peanut mini core collection using microsatellite markers. Crop Research 47: 1718–1727.Google Scholar
Kukade, SA and Tidke, JA (2014) Reproductive biology of Dolichos lablab L. (Fabaceae). Indian Journal of Plant Science 3: 22–25.Google Scholar
Kumar, J, Choundary, AK, Solanki, RK and Pratap, A (2011) Towards marker-assisted selection in pulses: a review. Plant Breeding 130: 297–313.Google Scholar
Laurentin, H (2009) Data analysis for molecular characterization of plant genetic resources. Genetic Resources and Crop Evolution 56: 277–292.Google Scholar
Liu, K and Muse, SV (2005) Power marker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21: 2128–2129.Google Scholar
Liu, L, Wang, L, Yao, J, Zhang, Y and Zhao, C (2010) Association mapping of six agronomic traits on chromosome 4A of wheat (Triticum aestivum L.). Molecular Plant Breeding 1: 10–11.Google Scholar
Macky, I and Powell, W (2007) Methods for linkage disequilibrium in crops. Trends in Plant Science 292: 57–63.Google Scholar
Miller, PA and Rawlings, JO (1967) Breakup of initial linkage blocks in cotton Gossypium hirsutum L. Crop Science 11: 695–698.Google Scholar
Morgante, M, Hanafey, M and Powell, W (2002) Microsatellites are preferentially associated with non-repetitive DNA in plant genomes. Nature Genetics 30: 194–200.Google Scholar
Murray, SC, Rooney, WL, Hamblin, MT, Mitchell, SE and Kresovich, S (2009) Sweet sorghum genetic diversity and association mapping for brix and height. Plant Genome 2: 48–62.Google Scholar
Nei, M (1973) Analysis of gene diversity in subdivided populations. Proceedings of National Academy of Science 70: 3321–3323.Google Scholar
Nemli, S, Asciogul, TK, Kay, HB, Kahraman, A, Siyok, DE and Tanyolac, B (2014) Association mapping for five agronomic traits in the common bean (Phaseolus vulgaris L.). Journal of the Science of Food and Agriculture 94: 3141–3151.Google Scholar
Neumann, K, Kobiljski, B, Dencic, S, Vershney, RK and Borner, A (2011) Genome-wide association mapping: a case study in bread wheat (Triticum aestivum L.). Molecular Breeding 27: 37–58.Google Scholar
Nordborg, M and Donnelly, P (1997) The coalescent process with setting genetics. Trends in Genetics 146: 1185–1195.Google Scholar
Nordborg, M and Tavare, S (2002) Linkage disequilibrium: what history has to tell us. Trends in Genetics 18: 83–90.Google Scholar
Pitchard, JK, Stephens, M and Donnelly, P (2000) Inference of population structure using multi-locus genotype data. Genetics 155: 945–959.Google Scholar
Qiu, LL, Xing, LL, Guo, Y, Wang, J, Jackson, SA and Chang, RZ (2013) A platform for soybean molecular breeding: the utilization of core collections for food security. Plant Molecular Biology 83: 41–50.Google Scholar
Rakshit, A, Rakshit, S, Singh, J, Chopra, SK, Balyan, HS, Gupt, PK and Bhat, SR (2010) Association of AFLP and SSR markers with agronomic and fiber quality traits in Gossypium hirsutum L. Journal of Genetics 89: 155–162.Google Scholar
Risch, N and Merikangas, K (1996) The future of genetic studies of complex human diseases. Science 273: 1516–1517.Google Scholar
Saravanan, S, Shanmugasundaram, P, Senthil, N and Veerabadhiram, P (2013) Comparison of genetic relatedness among Lablab bean (Lablab purpureus L. sweet) genotypes using DNA markers. International Journal of Integrative Biology 14: 23–30.Google Scholar
Smartt, J (1985) Evolution of grain legumes II. Old and new world pulses of lesser economic importance. Experimental Agriculture 21: 1–18.Google Scholar
Soto-Cerda, BJ, Duguid, S, Booker, H, Rowland, G, Diederichsen, A and Cloutier, S (2014) Association mapping of seed quality traits using the Canadian flax (Linum usitatissimum L.) core collection. Theoretical and Applied Genetics 127: 881–896.Google Scholar
Storey, JD and Tibshirani, R (2003) Statistical significance for genome wide studies. Proceedings of National Academy of Science 100: 9440–9445.Google Scholar
Thorsberry, JM, Goodman, MM, Doeblay, J, Kresovich, S, Neilson, D and Buckler, ES (2001) Dwarf 8 polymorphisms associate with variation in flowering time. Nature Genetics 28: 286–289.Google Scholar
Upadhyaya, HD, Gowda, CLL and Sastry, DVSSR (2008) Plant genetic resources management: collection, characterization, conservation and utilization. e-Journal of the SAT Agricultural Research 6: 1–15.Google Scholar
Vaijayanthi, PV, Ramesh, S, Byregowda, M, Mohan Rao, A, Keerthi, CM and Marry Reena, GA (2015a) Genetic variability for morpho-metric traits in dolichos bean (Lablab purpureus L.). Journal of Food Legumes 28: 5–10.Google Scholar
Vaijayanthi, PV, Ramesh, S, Byregowda, M, Mohan Rao, A, Jayarame, Gowda, Ramappa, HK, Keerthi, CM and Rajendra Prasad, BS (2015b) Development and validation of a core set of dolichos bean germplasm. International Journal of Vegetable Science 21: 419–428.Google Scholar
Wang, C, Chen, H, Zhi, L, Yang, W, Li, Y, Wang, H, Li, B, Zhao, M, Chen, and Diao, X (2010) Population genetics of foxtail millet and its wild ancestor. BMC Genetics 11: 90.Google Scholar
Wang, R, Yu, Y, Zhao, J, Shi, Y, Song, Y, Wang, T and Li, Y (2008) Population structure and linkage disequilibrium of a minicore set of maize inbred lines in China. Theoretical and Applied Genetics 117: 1141–1153.Google Scholar
Weir, BS and Cockerham, CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38: 1358–1370.Google Scholar
Wen, W, Mei, H, Feng, F, Yu, S, Huang, Z, Wu, J, Chen, L, Xu, X and Luo, L (2009) Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice (Oryza sativa L.). Theoretical and Applied Genetics 119: 459–470.Google Scholar
Whitt, SR and Buckler, ES (2003) Using natural allelic diversity to evaluate gene function. Methods in Molecular Biology 236: 123–139.Google Scholar
Yu, J and Buckler, ES (2006) Genetic association mapping and genome organization of maize. Current Opinion in Biotechnology 17: 1–6.Google Scholar
Yu, J, Pressoir, G, Briggs, WH, Vroh, BI, Yamasaki, M, Doebley, JF, Mcmullen, M D, Gaut, BS, Nielsen, DM, Holland, JB, Kresovich, S and Buckler, ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Genetics 38: 203–208.Google Scholar
Zhao, K, Aranzana, S, Kim, C, Lister, C, Shindo, C, Tang, C, Toomajian, H, Zheng, C, Dean, P, Marjoram, and Nordborg, M (2007) An Arabidopsis example of association mapping in structured samples. PLoS Genetics 3: 1–4.Google Scholar
Zhu, CS, Gore, M, Buckler, ES and Yu, JM (2008) Status and prospects of association mapping in plants. Plant Genome 1: 5–20.Google Scholar
Supplementary material: File

Vaijayanthi et al. supplementary material

Table S1-S2

Download Vaijayanthi et al. supplementary material(File)
File 27.2 KB