Association mapping (AM) or linkage disequilibrium (LD) mapping uses natural genome-wide distribution of various genes together with other detectable loci (markers) in predicting the marker-trait associations. Because of their physical or functional conservation and co-inheritance, loci that are in nonrandom association on the genome are said to be in linkage disequilibrium. Haplotype blocks, the physical chromosomal segments that are in LD, are the best conserved genetic regions on the plant genome, and they have evolutionary significance. Since AM is applicable to natural as well as synthetic populations, it considers several similar haplotypes in the population to derive the most robust marker-trait associations. Therefore, precision of the associations are greater in AM than that of the conventional linkage mapping. While linkage mapping is feasible only in crops that are amenable to relatively simple pedigree breeding, AM can be deployed in crops that are perennial and have complex breeding systems. AM uses several statistical methods and procedures ranging from fixed-model to mixed-model analyses and considers population characteristics such as population structure, kinship, and adaptive parameters for determining reliable marker-trait associations. There are several advantages of AM over linkage mapping, but it suffers from some limitations as well. This chapter discusses in detail the various principles, methods, prospects, and problems of AM in the different types of breeding systems and crop plants.


Linkage Disequilibrium Gene Conversion Association Mapping Linkage Disequilibrium Block Transmission Disequilibrium Test 
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  1. Abdurakhmonov IY, Abdukarimov A (2008) Application of association mapping to understanding the genetic diversity of plant germplasm resources. Int J Plant Genomics vol 2008: 1–18Google Scholar
  2. Akey JM, Zhang K, Xiong M (2003) The effect of single nucleotide polymorphism identification strategies on estimates of linkage disequilibrium. Mol Biol Evol 20:232–242PubMedCrossRefGoogle Scholar
  3. Ardlie KG, Kruglyak L, Seielstad M (2002) Patterns of linkage disequilibrium in the human genome. Genetics 3:299–309PubMedGoogle Scholar
  4. Ball RD (2007) Statistical analysis and experimental design. In: Oraguzie NC, Rikkerink EHA, Gardiner SE et al (eds) Association mapping in plants. Springer Science + Business Media, LLC, NY, pp 133–196CrossRefGoogle Scholar
  5. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B 57:289–300Google Scholar
  6. Bernardo R (2013) Genome-wide markers for controlling background variation in association mapping. Plant Genome 6:1–9Google Scholar
  7. Breseghello F, Sorrells ME (2006) Association analysis as a strategy for improvement of quantitative traits in plants. Crop Sci 46:1323–1330CrossRefGoogle Scholar
  8. Devlin B, Risch N (1995) A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 29:311–322PubMedCrossRefGoogle Scholar
  9. Flint-Garcia SA (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–74PubMedCrossRefGoogle Scholar
  10. Gaut BS, Long AD (2003) The lowdown on linkage disequilibrium. Plant Cell 15:1502–1506PubMedCentralPubMedCrossRefGoogle Scholar
  11. Geiringer H (1944) On the probability theory of linkage in Mendelian heredity. Ann Math Stat 15:25–57CrossRefGoogle Scholar
  12. Gorelick R, Laubichler MD (2004) Decomposing multilocus linkage disequilibrium. Genetics 166:1581–1583PubMedCentralPubMedCrossRefGoogle Scholar
  13. Gupta PK, Rustgi S, Kulwal PL (2005) Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol 57:461–485PubMedCrossRefGoogle Scholar
  14. Gupta PK, Kulwal PL, Jaiswal V (2014) Association mapping in crop plants: opportunities and challenges. In: Friedmann T, Dunlap J, Goodwin S (eds) Advances in genetics.. Academic, Elsevier, Vol 85, pp 109–148Google Scholar
  15. Hedrick PW (1987) Gametic disequilibrium measures: proceed with caution. Genetics 117:331–341PubMedCentralPubMedGoogle Scholar
  16. Ingvarsson PK, Street NR (2011) Association genetics of complex traits in plants. New Phytol 189:909–922PubMedCrossRefGoogle Scholar
  17. Jackson SA, Hass-Jacobus B, Pagel J (2004) The gene space of the soybean genome. In: Wilson RF, Stalker HT, Brummer EC (eds) Legume crop genomics. AOCS, Champaign, pp 187–193Google Scholar
  18. Kang HM, Zaitlen NA, Wade CM et al (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723PubMedCentralPubMedCrossRefGoogle Scholar
  19. Korte A, Vilhjalmsson BJ, Segura V et al (2012) A mixed model approach for genome-wide association studies of correlated traits in structured populations. Nature Genet 44:1066–1071PubMedCentralPubMedCrossRefGoogle Scholar
  20. Kumar M, Sharma CM, Rajwar GS (2004) A study on the community structure and diversity of a sub-tropical forest of Garhwal Himalayas. Indian Forester 130:207–214Google Scholar
  21. 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 43:163–168PubMedCrossRefGoogle Scholar
  22. Labate JA, Lamkey KR, Lee M et al (2000) Hardy-Weinberg and linkage equilibrium estimates in the BSSS and BSCB1 random mated populations. Maydica 45:243–255Google Scholar
  23. Lewontin RC (1964) The interaction of selection and linkage. I. General considerations; heterotic models. Genetics 49:49–67PubMedCentralPubMedGoogle Scholar
  24. Lewontin RC (1974) The genetic basis of evolutionary change. Columbia University, NYGoogle Scholar
  25. Li Y, Li Y, Wu S et al (2007b) Estimation of multilocus linkage disequilibria in diploid populations with dominant markers. Genetics 176:1811–1821PubMedCentralPubMedCrossRefGoogle Scholar
  26. Lippert C, Listgarten J, Liu Y et al (2011) FaST linear mixed models for genome-wide association studies. Nature Methods 8:833–835PubMedCrossRefGoogle Scholar
  27. Mather KA, Caicedo AL, Polato NR et al (2007) The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177:2223–2232PubMedCentralPubMedCrossRefGoogle Scholar
  28. Myles S, Peiffer J, Brown PJ et al (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202PubMedCentralPubMedCrossRefGoogle Scholar
  29. Oraguzie NC, Rikkerink EHA, Gardiner SE et al (2007) Association mapping in plants. Springer Science+Business Media, LLC, NYCrossRefGoogle Scholar
  30. Palaisa KA, Morgante M, Williams M et al (2003) Contrasting effects of selection on sequence diversity and linkage disequilibrium at two phytoene synthase loci. Plant Cell 15:1795–1806PubMedCentralPubMedCrossRefGoogle Scholar
  31. Price AL, Patterson NJ, Plenge RM et al (2006) Principal component analysis corrects for stratification in genome-wide association studies. Nature Genet 38:904–909PubMedCrossRefGoogle Scholar
  32. Pritchard JK, Stephens M, Rosenberg NA et al (2000a) Association mapping in structured populations. Am J Hum Genet 67:170–181PubMedCentralPubMedCrossRefGoogle Scholar
  33. Pritchard JK, Stephens M, Donnelly P (2000b) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedCentralPubMedGoogle Scholar
  34. Rakitsch B, Lippert C, Stegle O et al (2013) A lasso multi-marker mixed model for association mapping with population structure correction. Bioinformatics 29:206–214PubMedCrossRefGoogle Scholar
  35. 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 98:11479–11484PubMedCentralPubMedCrossRefGoogle Scholar
  36. Segura V, Vilhjalmsson BJ, Platt A et al (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nature Genet 44:825–830PubMedCentralPubMedCrossRefGoogle Scholar
  37. Spielman RS, McGinnis RE, Ewens WJ (1993) Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 52:506–516PubMedCentralPubMedGoogle Scholar
  38. Stich B, Melchinger AE, Frisch M et al (2005) Linkage disequilibrium in European elite maize germplasm investigated with SSRs. Theor Appl Genet 111:723–730PubMedCrossRefGoogle Scholar
  39. Stich B, Mohring J, Piepho H et al (2008) Comparison of mixed-model approaches for association mapping. Genetics 178:1745–1754PubMedCentralPubMedCrossRefGoogle Scholar
  40. Storey JD (2002) A direct approach to false discovery rates. J Royal Stat Soc Sr B 64:479–498CrossRefGoogle Scholar
  41. Van Inghelandt D, Reif JC, Dhillon BS et al (2011) Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm. Theor Appl Genet 123:11–20PubMedCrossRefGoogle Scholar
  42. Wu R, Zeng ZB (2001) Joint linkage and linkage disequilibrium mapping in natural populations. Genetics 157:899–909PubMedCentralPubMedGoogle Scholar
  43. Wurschum T (2012) Mapping QTL for agronomic traits in breeding populations. Theor Appl Genet 125:201–210PubMedCrossRefGoogle Scholar
  44. Yu J, Pressoir G, Briggs WH et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genet 38:203–208PubMedCrossRefGoogle Scholar
  45. Yu J, Holland JB, McMullen MD et al (2008) Genetic design and statistical power of nested association. Genetics 178:539–551PubMedCentralPubMedCrossRefGoogle Scholar
  46. Zhao K, Aranzana MJ, Kim S et al (2007) An Arabidopsis example of association mapping in structured samples. PLoS Genet 3:71–82CrossRefGoogle Scholar
  47. Zhou X, Stephens M (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11:407–409. doi: 10.1038/nmeth.2848 PubMedCentralPubMedCrossRefGoogle Scholar
  48. Zhu C, Gore M, Buckler ES et al (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20CrossRefGoogle Scholar

Copyright information

© Author(s) 2015

Authors and Affiliations

  • B. D. Singh
    • 1
  • A. K. Singh
    • 2
  1. 1.School of BiotechnologyBanaras Hindu UniversityVaranasiIndia
  2. 2.Division of GeneticsIndian Agricultural Research InstituteNew DelhiIndia

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