Skip to main content
SpringerLink
Log in

Association mapping in an elite maize breeding population

  • Original Paper
  • Published:
Theoretical and Applied Genetics Aims and scope Submit manuscript

Abstract

Association mapping (AM) is a powerful approach to dissect the genetic architecture of quantitative traits. The main goal of our study was to empirically compare several statistical methods of AM using data of an elite maize breeding program with respect to QTL detection power and possibility to correct for population stratification. These models were based on the inclusion of cofactors (Model A), cofactors and population effect (Model B), and SNP effects nested within populations (Model C). A total of 930 testcross progenies of an elite maize breeding population were field-evaluated for grain yield and grain moisture in multi-location trials and fingerprinted with 425 SNP markers. For grain yield, population stratification was effectively controlled by Model A. For grain moisture with a high ratio of variance among versus within populations, Model B should be applied in order to avoid potential false positives. Model C revealed large differences among allele substitution effects for trait-associated SNPs across multiple plant breeding populations. This heterogeneous SNP allele substitution effects have a severe impact for genomic selection studies, where SNP effects are often assumed to be independent of the genetic background.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Bernardo R, Yu J (2007) Prospects for genome-wide selection for quantitative traits in maize. Crop Sci 47:1082–1090

    Article  Google Scholar 

  • Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L (2006) Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 113:206–224

    Article  PubMed  CAS  Google Scholar 

  • Calus MPL, Meuwissen THE, De Roos APW, Veerkamp RF (2008) Accuracy of genomic selection using different methods to define haplotypes. Genetics 178:553–561

    Article  PubMed  CAS  Google Scholar 

  • De Los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182:375–385

    Article  PubMed  Google Scholar 

  • Dellaporta SL, Wood J, Hicks JB (1983) A plant DNA mini-preparation version II. Plant Mol Biol Rep 1:19–21

    Article  CAS  Google Scholar 

  • Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman House, London

    Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  • Frascaroli E, Cane` MA, Pe` ME, Pea G, Morgante M, Landi P (2009) QTL detection in maize testcross progenies as affected by related and unrelated testers. Theor Appl Genet 118:993–1004

    Article  PubMed  Google Scholar 

  • Gianola D, Fernando RL, Stella A (2006) Genomic assisted prediction of genetic value with semi-parametric procedures. Genetics 173:1761–1776

    Article  PubMed  CAS  Google Scholar 

  • Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325–338

    Google Scholar 

  • Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389–2397

    PubMed  CAS  Google Scholar 

  • Habier D, Totir LR, Fernando RL (2010) A Two-stage approximation for analysis of mixture genetic models in large pedigrees. Genetics 185:655–670

    Article  PubMed  CAS  Google Scholar 

  • Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38:226–231

    Article  Google Scholar 

  • Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

    Google Scholar 

  • Jansen RC, Stam P (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics 136:1447–1455

    PubMed  CAS  Google Scholar 

  • Kosambi D (1944) The estimation of map distance from recombination values. Ann Eugen 12:172–175

    Article  Google Scholar 

  • Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, Durrant C, Mott R (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5:e1000551

    Article  PubMed  Google Scholar 

  • Lu Y, Zhang S, Shah T, Xie C, Hao Z, Li X, Farkhari M, Ribaut J-M, Cao M, Rong T, Xu Y (2010) Joint linkage–linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize. Proc Natl Acad Sci USA 107:19585–19590

    Article  PubMed  CAS  Google Scholar 

  • Luan T, Woolliams JA, Lien S, Kent M, Svendsen M, Meuwissen TH (2009) The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183:1119–1126

    Article  PubMed  Google Scholar 

  • Maurer HP, Melchinger AE, Frisch M (2008) Population genetic simulation and data analysis with Plabsoft. Euphytica 161:133–139

    Article  Google Scholar 

  • McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li HH, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C, Brown P, Browne C, Eller M, Guill K, Harjes C, Kroon D, Lepak N, Mitchell SE, Peterson B, Pressoir G, Romero S, Rosas MO, Salvo S, Yates H, Hanson M, Jones E, Smith S, Glaubitz JC, Goodman M, Ware D, Holland JB, Buckler ES (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740

    Article  PubMed  CAS  Google Scholar 

  • Melchinger AE, Utz HF, Schön CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and larger bias in estimates of QTL effects. Genetics 149:383–403

    PubMed  CAS  Google Scholar 

  • Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    PubMed  CAS  Google Scholar 

  • Mihaljevic M, Friedrich UH, Melchinger AE (2004) Congruency of quantitative trait loci detected for agronomic traits in testcrosses of five populations of European maize. Crop Sci 44:114–124

    Article  CAS  Google Scholar 

  • Myles S, Peiffer J, Brown PJ, Ersoz ES, Zhang Z, Costich DE, Buckler ES (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202

    Article  PubMed  CAS  Google Scholar 

  • R Development Core Team (2010) R: A language and environment for statistical computing, R Foundation for Statistical Computing. http://www.R-project.org

  • Rafalski JA (2002) Novel genetic mapping tools in plants: SNPs and LD-based approaches. Plant Sci 162:329–333

    Article  CAS  Google Scholar 

  • Reif JC, Hamrit S, Heckenberger M, Schipprack W, Maurer HP, Bohn M, Melchinger AE (2005) Temporal trend of genetic diversity in European maize germplasm. Theor Appl Genet 111:838–845

    Article  PubMed  Google Scholar 

  • Reif JC, Liu W, Gowda M, Maurer HP, Möhring J, Fischer S, Schechert A, Würschum T (2010) Genetic basis of agronomically important traits in sugar beet (Beta vulgaris L.) investigated with joint linkage association mapping. Theor Appl Genet 121:1489–1499

    Article  PubMed  Google Scholar 

  • SAS Institute Inc (2008) SAS/STAT® 9.2 User’s Guide. Cary, NC, USA

    Google Scholar 

  • Schön CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167:485–498

    Article  PubMed  Google Scholar 

  • Schön CC, Dhillon BS, Utz HF, Melchinger AE (2010) High congruency of QTL positions for heterosis of grain yield in three crosses of maize. Theor Appl Genet 120:321–332

    Article  PubMed  Google Scholar 

  • Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464

    Article  Google Scholar 

  • Searle SR (1971) Linear models. Wiley, New York

    Google Scholar 

  • Smalley MD, Daub JL, Hallauer AR (2004) Estimation of heritability in maize by parent-offspring regression. Maydica 49:221–229

    Google Scholar 

  • Stam P (1993) Construction of integrated genetic linkage maps by means of a new computer package: JoinMap. The Plant J 3:739–744

    Article  CAS  Google Scholar 

  • Stich B, Melchinger AE, Frisch M, Maurer HP, Heckenberger M, Reif JC (2005) Linkage disequilibrium in European elite maize germplasm investigated with SSRs. Theor Appl Genet 111:723–730

    Article  PubMed  Google Scholar 

  • Stich B, Mohring J, Piepho H-P, Heckenberger M, Buckler ES, Melchinger AE (2008) Comparison of mixed-model approaches for association mapping. Genetics 178:1745–1754

    Article  PubMed  Google Scholar 

  • Utz HF, Melchinger AE, Schön CC (2000) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154:1839–1849

    PubMed  Google Scholar 

  • Van Inghelandt D, Reif JC, Dhillon BS, Flament P, Melchinger AE (2011) Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm. Theor Appl Genet. doi: 10.1007/s00122-011-1562-3

  • Van Ooijen JW, Voorrips RE (2001) JoinMap 3.0 software for the calculation of genetic linkage maps. Plant Res Int, Wageningen

    Google Scholar 

  • Wright S (1978) Evolution and genetics of populations, variability within and among natural populations, 4th edn. The University of Chicago Press, Chicago, p 91

    Google Scholar 

  • Wu R, Ma C-X, Casella G (2002) Joint linkage and linkage disequilibrium mapping of quantitative trait loci in natural populations. Genetics 160:779–792

    PubMed  CAS  Google Scholar 

  • Yan JB, Shah T, Warburton M, Buckler ES, McMullen MD, Crouch J (2009) Genetic characterization of a global maize collection using SNP markers. PLoS ONE 4:e8451

    Article  PubMed  Google Scholar 

  • Yu J, Buckler ES (2006) Genetic association mapping and genome organization of maize. Curr Opin Biotech 17:155–160

    Article  PubMed  CAS  Google Scholar 

  • Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551

    Article  PubMed  Google Scholar 

  • Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468

    PubMed  CAS  Google Scholar 

  • Zhang Z, Ersoz E, Lai C-Q, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–363

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We would like to thank Dr. Raffaele Capitanio from Syngenta Seeds for providing the information on phenotypic data. This research was conducted within the Biometric and Bioinformatic Tools for Genomics based Plant Breeding project supported by the German Federal Ministry of Education and Research (BMBF) within the framework of GABI–FUTURE initiative. We thank two anonymous reviewers for their valuable suggestions, which considerably improved the manuscript.

Author information

Authors and Affiliations

  1. State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany

    Wenxin Liu, Manje Gowda, Jana Steinhoff, Hans Peter Maurer, Tobias Würschum, Carl Friedrich Horst Longin & Jochen Christoph Reif

  2. Syngenta Seeds SAS, 12, chemin de l’Hobit, B.P. 27, 31790, Saint-Sauveur, France

    Frédéric Cossic

  3. Crop Genetics and Breeding Department, China Agricultural University, 100193, Beijing, China

    Wenxin Liu

Authors
  1. Wenxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manje Gowda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jana Steinhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hans Peter Maurer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tobias Würschum
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Carl Friedrich Horst Longin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Frédéric Cossic
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jochen Christoph Reif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jochen Christoph Reif.

Additional information

Communicated by J. Yan.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 150 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, W., Gowda, M., Steinhoff, J. et al. Association mapping in an elite maize breeding population. Theor Appl Genet 123, 847–858 (2011). https://doi.org/10.1007/s00122-011-1631-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00122-011-1631-7

Keywords

Search

Navigation