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Genome-wide association mapping for grain yield components and root traits in Brassica juncea (L.) Czern & Coss.

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Abstract

We used a Diversity Fixed Foundation Set comprising 48 inbred lines of Brassica juncea and representing all the adaption zones of the crop for association mapping. Extensive phenotypic variations were observed for all the grain yield components and root traits under both irrigated and restricted moisture conditions. The genotypes differed in their responses to moisture stress. Trait averages declined numerically under restricted moisture conditions when compared to a standard irrigation schedule. Canonical analysis demonstrated the importance of primary branches and seed size as the traits of significance for drought susceptibility index. Microsatellite markers (158), representing all the 18 chromosomes, were used to assess the population structure, linkage disequilibrium (LD) in the association panel and marker–trait associations (MTA's). A comparison of four association models [general linear model/GLM(Q-matrix/Q), mixed linear model: MLM(Q+kinship matrix/K), GLM (principal components/PC) and MLM(PC + K)] showed that GLM(PC) and MLM(PC + K), incorporating principal components and kinship matrix, were the best models. Maximum proportions of significant results were observed in the models GLM(PC) and MLM(PC + K). MLM was preferred as there were fewer false positives than GLM. Thirteen significant associations were detected between the molecular markers and agronomic traits. Of these, seven were identified under normal moisture conditions, and six under restricted moisture conditions. Marker–trait associations included four markers associated with grain yield, three with seed size, two with secondary branches and one marker each with plant height, root diameter and root length. A single marker SB1822-1, was repeatedly detected for seed size and grain yield, and was localized at 17.5 cM (centiMorgans) on chromosome B3. Marker SB3872-3 revealed a significant effect under normal moisture conditions on seed size (R 2% = 15.16) at 60.9 cM on chromosome B5 during the first year. Among the favorable alleles, SB1822-1 had the average positive phenotypic effect for seed size and grain yield. Marker cnu316-3 had maximum positive phenotypic effects on grain yield.

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References

  • Afifi A, Clark V, May S (2004) Computer-aided multivariate analysis, 4th edn. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  • Banga SS, Banga SK (2009) Crop improvement strategies in rapeseed-mustard. In: Hegde DM (ed) vegetable oil scenario: approaches to meet the growing demands. ISOR, Hyderabad, pp 13–35

    Google Scholar 

  • Bañuelos GS, Dhillon KS, Banga SS (2013) Oilseed Brassicas. In: Singh BP (ed) Biofuel crops: production, physiology and genetics. CABI, Wallingford, pp 339–368

    Chapter  Google Scholar 

  • Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    Article  CAS  PubMed  Google Scholar 

  • Cai D, Xiao Y, Yang W, Ye W, Wang B, Younas M, Wu J, Liu K (2013) Association mapping of six grain yield-related traits in rapeseed (Brassica napus L.). Theor Appl Genet 27(1):85–96

    Google Scholar 

  • Chauhan JS, Tyagi MK, Kumar A, Nashaat NI, Singh M, Singh NB, Jakhar ML, Welham SJ (2007) Drought effects on grain yield and its components in Indian mustard (Brassica juncea L.). Plant Breeding 126:399–402

    Article  Google Scholar 

  • Cockram J, White J, Zuluaga DL, Smith D et al (2010) Genome-wide association mapping to candidate polymorphism resolution in the unsequenced barley genome. Proc Natl Acad Sci USA 107:21611–21616

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Ding G, Zhao Z, Liao Y, Hu Y, Shi L, Long Y, Xu F (2012) Quantitative trait loci for seed grain yield and grain yield-related traits, and their responses to reduced phosphorus supply in Brassica napus. Ann Bot 109:747–759

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13–15

    Google Scholar 

  • Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587

    PubMed Central  CAS  PubMed  Google Scholar 

  • Fischer RA, Maurer R (1978) Drought resistance in spring wheat cultivars. I. Grain grain yield responses. Aust J Agric Res 29:897–912

    Article  Google Scholar 

  • Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374

    Article  CAS  PubMed  Google Scholar 

  • Gao H, Williamson S, Bustamante CD (2007) A Markov chain Monte Carlo approach for joint inference of population structure and inbreeding rates from multi-locus genotype data. Genetics 176(3):1635–1651

    Article  PubMed Central  PubMed  Google Scholar 

  • Hammer Ø, Harper DAT, Ryan PD (2001) Past: paleontological statistics software package for education and data analysis. Palaeontol Electron 4(1):4–9

    Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New York

    Book  Google Scholar 

  • Hosseini SM, Hassibi P (2011) Effects of water deficit stress on several quantitative and qualitative characteristics of canola (Brassica napus L.) cultivars. Not Sci Biol 3(3):120–125

    Google Scholar 

  • Hotelling H (1936) Relations between two sets of variables. Biometrika 28:321–377

    Article  Google Scholar 

  • Jannink J-L, Bink MCAM, Jansen RC (2001) Using complex plant pedigrees to map valuable genes. Trends Plant Sci 6:337–342

    Article  CAS  PubMed  Google Scholar 

  • Kaur P, Banga S, Kumar N, Gupta S, Banga SS (2014) Polyphyletic origin of Brassica juncea with B. rapa and B. nigra participating as cytoplasm donor parents in independent hybridization events. Am J Bot 101(7):1157–1166. doi:10.3732/ajb.1400232

    Article  PubMed  Google Scholar 

  • Kim H, Choi SR, Bae J (2009) Sequenced BAC anchored reference genetic map that reconciles the ten individual chromosomes of Brassica rapa. BMC Genom 10:432

    Article  Google Scholar 

  • Kraakman ATW, Niks RF, van den Berg PMMM, Stam P, van Eeuwijk FA (2004) Linkage disequilibrium of grain yield and grain yield stability in modern spring barley cultivars. Genetics 168:435–446

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Lowe AJ, Moule C, Trick M, Edwards K (2004) Efficient large-scale development of microsatellites for marker and mapping applications in Brassica crop species. Theo Appl Genet 108:1103–1112

    Article  CAS  Google Scholar 

  • Malosetti M, Linden CG, Vosman B, Eeuwijk FA (2007) A mixed model approach to association mapping using pedigree information with an illustration to resistance for Phytophthorainfestans in potato. Genetics 175:879–889

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Pasam RK, Sharma R, Malosetti M, van Eeuwijk FA, Haseneyer G, Kilian B, Graner A (2012) Genome-wide association studies for agronomical traits in a worldwide spring barley collection. BMC Plant Biol 12:16

    Article  PubMed Central  PubMed  Google Scholar 

  • Patterson N, Price AL, Reich D (2006) Population structure and EIGENANALYSIS. PLoS Genet 2:2074–2093

    Article  CAS  Google Scholar 

  • Prakash S, Wu XM, Bhat SR (2011) History, evolution, and domestication of Brassica crops. In: Janick J (ed) Plant breeding reviews, vol 35. Wiley, Hoboken

    Google Scholar 

  • Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909

    Article  CAS  PubMed  Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genet 155:945–959

    CAS  Google Scholar 

  • Qi L, Mao L, Sun C, Pu Y, Fu T, Ma C, Shen J, Tu J, Yi B, Wen J (2014) Interpreting the genetic basis of silique traits in Brassica napus using a joint QTL network. Plant Breed 133:52–60

    Article  CAS  Google Scholar 

  • Qu YY, Mu P, Zhang HL, Chen CY, Gao YM, Tian Y et al (2008) Mapping QTL of root morphological traits at different growth stages in rice. Genetica 133:187–200

    Article  PubMed  Google Scholar 

  • R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/

  • Ramchiary N, Padmaja KL, Sharma S, Gupta V, Sodhi VS, Mukhopadhyay A, Arumugam N, Pental D, Pradhan AK (2007) Mapping of grain yield in influencing QTL in Brassica juncea: implications for breeding of a major oilseed crop of dryland areas. Theo Appl Genet 115:807–817

    Article  CAS  Google Scholar 

  • Shi J, Li R, Qiu D, Long CJY, Morgan C, Bancroft I, Zhao J, Meng J (2009) Unraveling the complex trait of crop grain yield with quantitative trait loci mapping in Brassica napus. Genetics 182:851–861

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Singh BP, Chowdhury RK (1983) Correlation and path coefficient analysis of seed grain yield and oil content in mustard (Brassica juncea). Can J Genet Cytol 25:312–317

    Article  Google Scholar 

  • Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc Ser B 64:479–498

    Article  Google Scholar 

  • Storey JD, Tibshirani R (2003) R: statistical significance for genome-wide experiments. Proc Natl Acad Sci USA 100:9440–9445

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Verbeke G, Molenberghs G (2000) Linear mixed models for longitudinal data. Springer, New York

    Google Scholar 

  • Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping accounting for multiple levels of relatedness. Nat Genet 38:203–208

    Article  CAS  PubMed  Google Scholar 

  • Zhang H, Flottmann S, Milroy SP (2011) Grain yield formation of canola (Brassica napus L) and associated traits in the high rainfall zone. 17th Australian Research Assembly on Brassicas (ARAB), Wagga Wagga, NSW, pp 93–98

  • Zhou Q, Fu D, Mason AS, Zeng Y, Zhao C, Huang Y (2014) In silico integration of quantitative trait loci for seed grain yield and grain yield-related traits in Brassica napus. Mol Breeding 33:881–894

    Article  Google Scholar 

  • Zondervan KT, Cardon RL (2004) The complex interplay among factors that influence allelic association. Nat Rev Genet 5:89–101

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Indian Council of Agricultural Research for supporting this work under ICAR National Professor Project “Broadening the genetic base of Indian mustard (Brassica juncea) through alien introgressions and germplasm enhancement” awarded to S.S.B.

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  1. Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India

    Javed Akhatar & Surinder S. Banga

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  1. Javed Akhatar
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  2. Surinder S. Banga
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Correspondence to Surinder S. Banga.

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Akhatar, J., Banga, S.S. Genome-wide association mapping for grain yield components and root traits in Brassica juncea (L.) Czern & Coss.. Mol Breeding 35, 48 (2015). https://doi.org/10.1007/s11032-015-0230-8

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