Skip to main content
SpringerLink
Log in

Association Mapping of Genetic Resources: Achievements and Future Perspectives

  • Chapter
  • First Online:
Genomics of Plant Genetic Resources

Abstract

Association mapping studies in plants contribute to not only detecting the genetic basis of variation in physiological, developmental, and morphological traits (e.g., flowering time, plant height, grain quality, and nutrient content) but also bringing together researchers to assemble core collections and develop genetic platforms for genotyping, phenotyping, analysis, and interpretation. The establishment of the unified mixed model greatly facilitated association mapping studies in plants and further methodology work in general. Association mapping is well positioned to exploit the advances in next generation genomic technologies and high-throughput phenotyping. Genome-wide association studies (GWAS) are expected to increase dramatically once genome sequences of all major crop species are obtained. Moving forward, researchers in plant genetics and related disciplines need to develop improved genetic designs and computational tools to address several challenges such as missing heritability, new gene identification, genotyping-by-sequencing, and rare alleles. In this chapter, we describe major progress in understanding population structure, advancements in design and implementation of association mapping, and summarize examples of association mapping in maize, rice, Arabidopsis, wheat, barley, soybean, and sorghum. Finally, major opportunities with potential implications in plant genetics are discussed.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abe A, Kosugi S, Yoshida K et al (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol 30:174–178

    CAS  PubMed  Google Scholar 

  • Akhunov E, Akhunova A, Anderson O et al (2010) Nucleotide diversity maps reveal variation in diversity among wheat genomes and chromosomes. BMC Genomics 11:702

    CAS  PubMed Central  PubMed  Google Scholar 

  • Ali M, Rajewski J, Baenziger P et al (2008) Assessment of genetic diversity and relationship among a collection of US sweet sorghum germplasm by SSR markers. Mol Breed 21:497–509

    CAS  Google Scholar 

  • Atwell S, Huang YS, Vilhjalmsson BJ et al (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465:627–631

    CAS  PubMed Central  PubMed  Google Scholar 

  • Aulchenko YS, Koning DJ de, Haley C (2007) Genome wide rapid association using mixed model and regression: a fast and simple method for genome wide pedigree-based quantitative trait loci association analysis. Genetics 177:577–585

    CAS  PubMed Central  PubMed  Google Scholar 

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

    Google Scholar 

  • Brachi B, Faure N, Horton M et al (2010) Linkage and association mapping of Arabidopsis thaliana flowering time in nature. PLoS Genet 6:e1000940

    PubMed Central  PubMed  Google Scholar 

  • Bradbury PJ, Zhang Z, Kroon DE et al (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    CAS  PubMed  Google Scholar 

  • Brenchley RC, Spannagl M, Pfeifer M et al (2012) Analysis of the bread wheat genome using whole-genome shotgun sequencing. Nature 491:705–710

    CAS  PubMed Central  PubMed  Google Scholar 

  • Breseghello F, Sorrells ME (2006a) Association analysis as a strategy for improvement of quantitative traits in plants. Crop Sci 46:1323–1330

    Google Scholar 

  • Breseghello F, Sorrells ME (2006b) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177

    Google Scholar 

  • Brown PJ, Rooney WL, Franks C, Kresovich S (2008) Efficient mapping of plant height quantitative trait loci in a sorghum association population with introgressed dwarfing genes. Genetics 180:629–637

    PubMed Central  PubMed  Google Scholar 

  • Browning B, Browning S (2008) Haplotypic analysis of wellcome trust case control consortium data. Hum Genet 123:273–280

    PubMed Central  PubMed  Google Scholar 

  • Buckler ES, Gaut BS, McMullen MD (2006) Molecular and functional diversity of maize. Curr Opin Plant Biol 9:172–176

    CAS  PubMed  Google Scholar 

  • Buckler ES, Holland JB, Bradbury PJ et al (2009) The genetic architecture of maize flowering time. Science 325:714–718

    CAS  PubMed  Google Scholar 

  • Caldwell KS, Russell J, Langridge P, Powell W (2006) Extreme population-dependent linkage disequilibrium detected in an inbreeding plant species, Hordeum vulgare. Genetics 172:557–567

    CAS  PubMed Central  PubMed  Google Scholar 

  • Camus-Kulandaivelu L, Veyrieras JB, Madur D et al (2006) Maize adaptation to temperate climate: relationship between population structure and polymorphism in the Dwarf8 gene. Genetics 172:2449–2463

    CAS  PubMed Central  PubMed  Google Scholar 

  • Cao J, Schneeberger K, Ossowski S et al (2011) Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nat Genet 43:956–963

    CAS  PubMed  Google Scholar 

  • Casa AM, Pressoir G, Brown PJ et al (2008) Community resources and strategies for association mapping in sorghum. Crop Sci 48:30–40

    Google Scholar 

  • Cavanagh CR, Chao S, Wang S, Huang BE, Stephen S, Kiani S et al (2013) Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proc Natl Acad Sci USA 110:8057–8062

    Google Scholar 

  • Chao S, Dubcovsky J, Dvorak J et al (2010) Population-and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L.). BMC Genomics 11:727

    CAS  PubMed Central  PubMed  Google Scholar 

  • Chia JM, Song CBradburyPJetal (2012) Maize HapMap2 identifies extant variation from a genome in flux. Nat Genet 44:803–807

    CAS  PubMed  Google Scholar 

  • Chutimanitsakun Y, Nipper RW, Cuesta-Marcos A et al (2011) Construction and application for QTL analysis of a restriction site associated DNA (RAD) linkage map in barley. BMC Genomics 12:4

    CAS  PubMed Central  PubMed  Google Scholar 

  • Clark RM, Schweikert G, Toomajian C et al (2007) Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science 317:338–342

    CAS  PubMed  Google Scholar 

  • Cockram J, White J, Leigh F et al (2008) Association mapping of partitioning loci in barley. BMC Genet 9:16

    PubMed Central  PubMed  Google Scholar 

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

    CAS  PubMed Central  PubMed  Google Scholar 

  • Crainiceanu CM, Ruppert D (2004) Likelihood ratio tests in linear mixed models with one variance component. J R Stat Soc B 66:165–185

    Google Scholar 

  • Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004

    CAS  PubMed  Google Scholar 

  • Devlin B, Roeder K, Wasserman L (2001) Genomic Control, a New Approach to Genetic-Based Association Studies. Theor Popul Biol 60:155–166

    CAS  PubMed  Google Scholar 

  • Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379

    CAS  PubMed Central  PubMed  Google Scholar 

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

    CAS  Google Scholar 

  • Flint-Garcia SA, Thuillet AC, Yu J et al (2005) Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J 44:1054–1064

    CAS  PubMed  Google Scholar 

  • Gilmour AR, Gogel BJ, Cullis BR et al (2002) ASReml user guide release 1.0. VSN International Ltd, Hemel Hempstead

    Google Scholar 

  • Gore MA, Chia J-M, Elshire RJ et al (2009) A first-generation haplotype map of maize. Science 326:1115–1117

    CAS  PubMed  Google Scholar 

  • Hamblin MT, Mitchell SE, White GM et al (2004) Comparative population genetics of the panicoid grasses: sequence polymorphism, linkage disequilibrium and selection in a diverse sample of Sorghum bicolor. Genetics 167:471–483

    CAS  PubMed Central  PubMed  Google Scholar 

  • Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620

    Google Scholar 

  • Harjes CE, Rocheford TR, Bai L et al (2008) Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science 319:330–333

    CAS  PubMed Central  PubMed  Google Scholar 

  • Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12

    CAS  Google Scholar 

  • Henderson CR (1975) Comparison of alternative sire evaluation methods. J Anim Sci 41:760–770

    Google Scholar 

  • Huang X, Feng Q, Qian Q et al (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076

    CAS  Google Scholar 

  • Huang X, Wei X, Sang T et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967

    CAS  PubMed  Google Scholar 

  • Huang X, Zhao Y, Wei X et al (2011) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44:32–39

    CAS  PubMed  Google Scholar 

  • Huang X, Kurata N, Wei X. et al (2012) A map of rice genome variation reveals the origin of cultivated rice. Nature 490:497–501

    CAS  Google Scholar 

  • Hyten DL, Cannon SB, Song Q et al (2010) High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence. BMC Genomics 11:38

    PubMed Central  PubMed  Google Scholar 

  • Hyten DL, Choi I-Y, Song Q, Shoemaker RC (2007) Highly variable patterns of linkage disequilibrium in multiple soybean populations. Genetics 175:1937–1944

    CAS  PubMed Central  PubMed  Google Scholar 

  • Kang HM, Zaitlen NA, Wade CM et al (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723

    PubMed Central  PubMed  Google Scholar 

  • Kang HM, Sul JH, Service SK et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–354

    CAS  PubMed Central  PubMed  Google Scholar 

  • Kariya T, Kurata H (2004) Generalized least squares estimators. Generalized least squares. John Wiley & Sons Ltd, pp 25–66

    Google Scholar 

  • Konishi S, Izawa T, Lin SY et al (2006) An SNP caused loss of seed shattering during rice domestication. science 312:1392–1396

    CAS  PubMed  Google Scholar 

  • Kump KL, Bradbury PJ, Wisser RJ et al (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:163–168

    CAS  PubMed  Google Scholar 

  • Laird PW (2003) The power and the promise of DNA methylation markers. Nat Rev Cancer 3:253–266

    CAS  PubMed  Google Scholar 

  • Letta T, Maccaferri M, Badebo A et al (2013) Searching for novel sources of field resistance to Ug99 and Ethiopian stem rust races in durum wheat via association mapping. Theor Appl Genet 126:1237–1256

    PubMed  Google Scholar 

  • Lipka AE, Tian F, Wang Q et al (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399

    CAS  PubMed  Google Scholar 

  • Li X, Zhu C, Yeh C-T et al (2012) Genic and non-genic contributions to natural variation of quantitative traits in maize. Genome Res 22:2436–2444

    CAS  PubMed Central  PubMed  Google Scholar 

  • Lippert C, Listgarten J, Liu Y et al (2011) FaST linear mixed models for genome-wide association studies. Nat Methods 8:833–835

    CAS  PubMed  Google Scholar 

  • Liu KJ, Goodman M, Muse S et al (2003) Genetic structure and diversity among maize inbred lines as inferred from DNA microsatellites. Genetics 165:2117–2128

    CAS  PubMed Central  PubMed  Google Scholar 

  • Lu H-Y, Liu X-F, Wei S-P, Zhang Y-M (2011) Epistatic association mapping in homozygous crop cultivars. PLoS ONE 6:e17773

    CAS  PubMed Central  PubMed  Google Scholar 

  • Lukens LN, Zhan S (2007) The plant genome’s methylation status and response to stress: implications for plant improvement. Curr Opin Plant Biol 10:317–322

    CAS  PubMed  Google Scholar 

  • Maccaferri M, Sanguineti MC, Tuberosa R (2005) Analysis of linkage disequilibrium in a collection of elite durum wheat genotypes. Mol Breed 15:271–289

    CAS  Google Scholar 

  • Maccaferri M, Sanguineti MC, Natoli E et al (2006) A panel of elite accessions of durum wheat (Triticum durum Desf.) suitable for association mapping studies. Plant Genet Res 4:79–85

    CAS  Google Scholar 

  • Maccaferri M, Sanguineti MC, Mantovani P et al (2010) Association mapping of leaf rust response in durum wheat. Mol Breed 26:189–228

    CAS  Google Scholar 

  • Maccaferri M, Sanguineti MC, Demontis A et al (2011) Association mapping in durum wheat grown across a broad range of water regimes. J Exp Botany 62:409–438

    CAS  Google Scholar 

  • Manolio TA, Collins FS, Cox NJ et al (2009) Finding the missing heritability of complex diseases. Nature 461:747–753

    CAS  PubMed Central  PubMed  Google Scholar 

  • Mayer KF, Waugh R, Brown JW et al (2012) A physical, genetic and functional sequence assembly of the barley genome. Nature 491:711–716

    CAS  PubMed  Google Scholar 

  • McMullen MD, Kresovich S, Villeda HS et al (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740

    CAS  PubMed  Google Scholar 

  • Morris GP, Ramu P, Deshpande SP et al (2013) Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci USA 110:453–458

    CAS  PubMed Central  PubMed  Google Scholar 

  • McNally KL, Bruskiewich R, Mackill D et al (2006) Sequencing multiple and diverse rice varieties. Connecting whole-genome variation with phenotypes. Plant Physiol 141:26–31

    CAS  PubMed Central  PubMed  Google Scholar 

  • McNally KL, Childs KL, Bohnert R et al (2009) Genome-wide SNP variation reveals relationships among landraces and modern varieties of rice. Proc Natl Acad Sci USA 106:12273–12278

    CAS  PubMed Central  PubMed  Google Scholar 

  • Metzker ML (2010) Applications of next-generation sequencing sequencing technologies—the next generation. Nat Rev Genet 11:31–46

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed Central  PubMed  Google Scholar 

  • Murray SC, Rooney WL, Hamblin MT et al (2009) Sweet sorghum genetic diversity and association mapping for brix and height. Plant Genome 2:48–62

    CAS  Google Scholar 

  • Myles S, Peiffer J, Brown PJ et al (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202

    CAS  PubMed Central  PubMed  Google Scholar 

  • Nelson JC, Wang S, Wu Y et al (2011) Single-nucleotide polymorphism discovery by high-throughput sequencing in sorghum. BMC Genomics 12:352

    CAS  PubMed Central  PubMed  Google Scholar 

  • Ng SB, Turner EH, Robertson PD et al (2009) Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461:272–276

    CAS  PubMed Central  PubMed  Google Scholar 

  • Nordborg M, Hu TT, Ishino Y et al (2005) The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol 3:e196

    PubMed Central  PubMed  Google Scholar 

  • Ott J , Kamatani Y, Lathrop M (2011) Family-based designs for genome-wide association studies. Nat Rev Genet 12:465–474

    Google Scholar 

  • Palaisa KA, Morgante M, Williams M, Rafalski A (2003) Contrasting effects of selection on sequence diversity and linkage disequilibrium at two phytoene synthase loci. Plant Cell 15:1795–1806

    CAS  PubMed Central  PubMed  Google Scholar 

  • Paterson AH, Bowers JE, Bruggmann et al (2009) The Sorghum bicolor genome and the diversification of grasses. Nature 457:551–556

    CAS  PubMed  Google Scholar 

  • Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2:e190

    PubMed Central  PubMed  Google Scholar 

  • Platt A, Horton M, Huang YS et al (2010) The scale of population structure in Arabidopsis thaliana. PLoS Genet 6:e1000843

    PubMed Central  PubMed  Google Scholar 

  • Poland JA, Bradbury PJ, Buckler ES (2010) Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci USA 108:6893–6898

    Google Scholar 

  • Poland JA, Brown PJ, Sorrells ME et al (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7:e32253

    CAS  PubMed Central  PubMed  Google Scholar 

  • Poland JA, Rife TW (2012) Genotyping-by-Sequencing for plant breeding and genetics. Plant Genome (in press)

    Google Scholar 

  • Price AL, Patterson NJ, Plenge RM et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909

    CAS  PubMed  Google Scholar 

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

    CAS  Google Scholar 

  • Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000b) association mapping in structured populations. Am J Hum Genet 67:170–181

    CAS  Google Scholar 

  • Quaas RL, Pollak EJ (1981) Modified equations for sire models with groups. J Dairy Sci 64:1868–1872

    Google Scholar 

  • Ramsay L, Comadran J, Druka A et al (2011) INTERMEDIUM-C, a modifier of lateral spikelet fertility in barley, is an ortholog of the maize domestication gene TEOSINTE BRANCHED 1. Nat Genet 43:169–172

    CAS  PubMed  Google Scholar 

  • Remington DL, Thornsberry JM, Matsuoka Y et al (2001) Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc Natl Acad Sci USA 98:11479–11484

    CAS  PubMed Central  PubMed  Google Scholar 

  • Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273:1516–1517

    CAS  PubMed  Google Scholar 

  • Rogers SO, Bendich AJ (1987) Ribosomal RNA genes in plants: variability in copy number and in the intergenic spacer. Plant Mol Biol 9:509–520

    CAS  PubMed  Google Scholar 

  • Salvi S, Sponza G, Morgante M et al (2007) Conserved non-coding genomic sequences controlling flowering time differences in maize. Proc Natl Acad Sci USA 104:11376–11381

    CAS  PubMed Central  PubMed  Google Scholar 

  • Schmutz J, Cannon SB, Schlueter J et al (2010) Genome sequence of the palaeopolyploid soybean. Nature 463:178–183

    CAS  PubMed  Google Scholar 

  • Schnable PS, Ware D, Fulton RS et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115

    CAS  PubMed  Google Scholar 

  • Schneeberger K, Ossowski S, Lanz C et al (2009) SHOREmap: simultaneous mapping and mutation identification by deep sequencing. Nat Meth 6:550–551

    CAS  Google Scholar 

  • Schulte D, Close TJ, Graner A et al (2009) The international barley sequencing consortium-at the threshold of efficient access to the barley genome. Plant Physiol 149:142–147

    CAS  PubMed Central  PubMed  Google Scholar 

  • Segura V, Vilhjálmsson BJ, Platt A et al (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44:825–830

    CAS  PubMed Central  PubMed  Google Scholar 

  • Sequencing Project International Rice G (2005) The map-based sequence of the rice genome. Nature 436:793–800

    Google Scholar 

  • Shomura A, Izawa T, Ebana K et al (2008) Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet 40:1023–1028

    CAS  PubMed  Google Scholar 

  • Sorrells ME, Yu J (2009) Linkage disequilibrium and association mapping in the Triticeae. In: Feuillet C JMG (eds) Genetics and genomics of the triticeae, plant Genetics/Genomics. Springer Verlag, pp 655–684

    Google Scholar 

  • Springer NM, Ying K, Fu Y et al (2009) Maize inbreds exhibit high levels of copy number variation (CNV) and Presence/Absence Variation (PAV) in Genome Content. PLoS Genet 5:e1000734

    PubMed Central  PubMed  Google Scholar 

  • Sukumaran S, Xiang W, Bean SR et al (2012) Association mapping for grain quality in a diverse sorghum collection. Plant Genome 5:126–135

    CAS  Google Scholar 

  • Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327:818–822

    CAS  PubMed  Google Scholar 

  • Thornsberry JM, Goodman MM, Doebley J et al (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet 28:286–289

    CAS  PubMed  Google Scholar 

  • Tian F, Bradbury PJ, Brown PJ et al (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162

    CAS  PubMed  Google Scholar 

  • Trebbi D, Maccaferri M, de Heer P et al (2011) High-throughput SNP discovery and genotyping in durum wheat (Triticum durum Desf.). Theor Appl Genet 123:555–569

    PubMed  Google Scholar 

  • van Poecke RMP, Maccaferri M, Tang J et al (2013). Sequence-based SNP genotyping in durum wheat (submitted).

    Google Scholar 

  • Wang M, Zhu C, Barkley N et al (2009a) Genetic diversity and population structure analysis of accessions in the US historic sweet sorghum collection. Theor Appl Genet 120:13–23

    Google Scholar 

  • Wang Z, Gerstein M, Snyder M (2009b) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63

    CAS  Google Scholar 

  • Weber A, Clark RM, Vaughn L et al (2007) Major regulatory genes in maize contribute to standing variation in Teosinte (Zea mays ssp. parviglumis). Genetics 177:2349–2359

    CAS  PubMed Central  PubMed  Google Scholar 

  • Weigel D, Mott R (2009) The 1001 Genomes project for Arabidopsis thaliana. Genome Biol 10:107

    PubMed Central  PubMed  Google Scholar 

  • Wisser RJ, Kolkman JM, Patzoldt ME et al (2011) Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene. Proc Natl Acad Sci USA 108:7339–7344

    CAS  PubMed Central  PubMed  Google Scholar 

  • Yan J, Kandianis CB, Harjes CE et al (2010) Rare genetic variation at Zea mays crtRB1 increases β-carotene in maize grain. Nat Genet 42:322–327

    CAS  PubMed  Google Scholar 

  • Yu J, Arbelbide M, Bernardo R (2005) Power of in silico QTL mapping from phenotypic, pedigree, and marker data in a hybrid breeding program. Theor Appl Genet 110:1061–1067

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  • Yu J, Hamblin MT, Tuinstra MR (2013) Association Genetics Strategies and Resources. In: Paterson A (ed) genetics and genomics of the Saccharinae. Plant Genet Genom: crops and models 11:187–203

    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

    PubMed Central  PubMed  Google Scholar 

  • Yu J, Pressoir G, Briggs WH et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208

    CAS  PubMed  Google Scholar 

  • Zhang D, Bai G, Zhu C et al (2010a) Genetic diversity, population structure, and linkage disequilibrium in U.S. elite winter wheat. Plant Genome 3:117–127

    CAS  Google Scholar 

  • Zhang Z, Ersoz E, Lai C-Q et al (2010b) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360

    CAS  Google Scholar 

  • Zhao K, Aranzana MJ, Kim S et al (2007) An Arabidopsis example of association mapping in structured samples. PLoS Genet 3:e4

    PubMed Central  PubMed  Google Scholar 

  • Zhao K, Tung C-W, Eizenga GC et al (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467

    PubMed Central  PubMed  Google Scholar 

  • Zhao K, Wright M, Kimball J et al (2010) Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome. PLoS ONE 5:e10780

    PubMed Central  PubMed  Google Scholar 

  • Zhou X, Stephens M (2012) Genome-wide efficient mixed model analysis for association studies. Nat Genet 44:821–824

    CAS  PubMed Central  PubMed  Google Scholar 

  • Zhu C, Gore M, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20

    CAS  Google Scholar 

  • Zhu C, Li X, Yu J (2011) Integrating rare-variant testing, function prediction, and gene network in composite resequencing-based genome-wide association studies (CR-GWAS). G3: Genes, Genomes. Genetics 1:233–243

    CAS  Google Scholar 

  • Zhu C, Yu J (2009) Nonmetric multidimensional scaling corrects for population structure in association mapping with different sample types. Genetics 182:875–888

    PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Agriculture and Food Research Initiative Competitive Grant (2011-03587) from the USDA National Institute of Food and Agriculture, the Plant Feedstock Genomics Program (DE-SC0002259) of the U.S. Department of Energy, the Plant Genome Program (DBI-0820610) of the National Science Foundation, the Targeted Excellence Program of Kansas State University, and the Kansas State University Center for Sorghum Improvement.

Author information

Authors and Affiliations

  1. International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico

    Sivakumar Sukumaran

  2. Department of Agronomy, Iowa State University, Ames, Iowa, USA

    Jianming Yu

Authors
  1. Sivakumar Sukumaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianming Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Agricultural Sciences, University of Bologna, Bologna, Italy

    Roberto Tuberosa

  2. Genebank, IPK, Gatersleben, Sachsen-Anhalt, Germany

    Andreas Graner

  3. Bioversity International, Rome, Italy

    Emile Frison

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Sukumaran, S., Yu, J. (2014). Association Mapping of Genetic Resources: Achievements and Future Perspectives. In: Tuberosa, R., Graner, A., Frison, E. (eds) Genomics of Plant Genetic Resources. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7572-5_9

Download citation

Publish with us

Policies and ethics

Search

Navigation