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Trait Mapping Approaches Through Association Analysis in Plants

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Plant Genetics and Molecular Biology

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 164))

Abstract

Previously, association mapping (AM) methodology was used to unravel genetic complications in animal science by measuring the complex traits for candidate and non-candidate genes. Nowadays, this statistical approach is widely used to clarify the complexity in plant breeding program-based genome-wide breeding strategies, marker development, and diversity analysis. This chapter is particularly focused on methodologies with limitations and provides an overview of AM models and software used up to now. Association or linkage disequilibrium mapping has become a very popular method for discovering candidate and non-candidate genes and confirmation of quantitative trait loci (QTL) on various parts of the genome and in marker-assisted selection for breeding. Previously, various QTL investigations were carried out for different plants exclusively by linkage mapping. To help to understand the basics of modern molecular genetic techniques, in this chapter we summarize previous studies done on different crops. AM offers high-resolution power when there is large genotypic diversity and low linkage disequilibrium (LD) for the germplasm being investigated. The benefits of AM, compared with traditional QTL mapping, include a relatively detailed mapping resolution and a far less time-consuming approach since no mapping populations need to be generated. The advancements in genotyping and computational techniques have encouraged the use of AM. AM provides a fascinating approach for genetic investigation of QTLs, due to its resolution and the possibility to study the various genomic areas at the same time without construction of mapping populations. In this chapter we also discuss the advantages and disadvantages of AM, especially in the dicotyledonous crops Fabaceae and Solanaceae, with various genome-size reproductive strategies (clonal vs. sexual), and statistical models. The main objective of this chapter is to highlight the uses of association genetics in major and minor crop species that have trouble being analyzed for dissection of complex traits by identification of the factor responsible for controlling the effect of trait.

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Abbreviations

AM:

Association mapping

CV:

Coefficient variance

EST:

Expressed sequence tags

FDR:

False discovery rate

FWER:

Family-wise error rate

GLM:

General linear model

GS:

Genomic selection

GWAS:

Genome-wide association study

LD:

Linkage disequilibrium

MAS:

Marker-assisted selection

MCA:

Multiple correspondence analysis

MCMC:

Markov chain Monte Carlo

MLM:

Mixed linear model

MLMM:

Multiple locus multiple marker

MTMM:

Multiple trait multiple marker

PCA:

Principal component analysis

QTL:

Quantitative trait locus

SA:

Structure analysis

SLST:

Single locus single trait

SNP:

Single nucleotide polymorphism

References

  1. Rodney M (2001) Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology. Nat Rev Genet 2(5):370

    Article  CAS  Google Scholar 

  2. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9(5):356

    Article  CAS  PubMed  Google Scholar 

  3. Álvarez MF, Mosquera T, Blair MW (2014) The use of association genetics approaches in plant breeding. Plant Breed Rev 38:17–68

    Google Scholar 

  4. Muluale T, Bekeko Z (2016) Advances in quantitative trait loci, mapping and importance of markers assisted selection in plant breeding research. Int J Plant Breed Genet 10:58–68

    Article  CAS  Google Scholar 

  5. Zondervan KT, Cardon LR (2004) The complex interplay among factors that influence allelic association. Nat Rev Genet 5(2):89

    Article  CAS  PubMed  Google Scholar 

  6. Roy JK, Smith KP, Muehlbauer GJ, Chao S, Close TJ, Steffenson BJ (2010) Association mapping of spot blotch resistance in wild barley. Mol Breed 26(2):243–256

    Article  PubMed  PubMed Central  Google Scholar 

  7. Breseghello F, Finney PL, Gaines C, Andrews L, Tanaka J, Penner G, Sorrells ME (2005) Genetic loci related to kernel quality differences between a soft and a hard wheat cultivar. Crop Sci 45(5):1685–1695

    Article  CAS  Google Scholar 

  8. Jannink JL, Bink MC, Jansen RC (2001) Using complex plant pedigrees to map valuable genes. Trends Plant Sci 6(8):337–342

    Article  CAS  PubMed  Google Scholar 

  9. Yu J, Buckler ES (2006) Genetic association mapping and genome organization of maize. Curr Opin Biotechnol 17(2):155–160

    Article  CAS  PubMed  Google Scholar 

  10. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38(2):203

    Article  CAS  PubMed  Google Scholar 

  11. Wen W, Mei H, Feng F, Yu S, Huang Z, Wu J, Chen L, Xu X, Luo L (2009) Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice (Oryza sativa L.) Theor Appl Genet 119(3):459–470

    Article  PubMed  Google Scholar 

  12. Lu Q, Zhang M, Niu X, Wang S, Xu Q, Feng Y, Wang C, Deng H, Yuan X, Yu H, Wang Y (2015) Genetic variation and association mapping for 12 agronomic traits in indica rice. BMC Genomics 16(1):1067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bresseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172(2):1165–1177

    Article  Google Scholar 

  14. Bellucci A, Torp AM, Bruun S, Magid J, Andersen SB, Rasmussen SK (2015) Association mapping in scandinavian winter wheat for yield, plant height, and traits important for second-generation bioethanol production. Front Plant Sci 6:1046

    Article  PubMed  PubMed Central  Google Scholar 

  15. Jaiswal V, Mir RR, Mohan A, Balyan HS, Gupta PK (2012) Association mapping for pre-harvest sprouting tolerance in common wheat (Triticum aestivum L.) Euphytica 188(1):89–102

    Article  CAS  Google Scholar 

  16. Jaiswal V, Gahlaut V, Meher PK, Mir RR, Jaiswal JP, Rao AR, Balyan HS, Gupta PK (2016) Genome wide single locus single trait, multi-locus and multi-trait association mapping for some important agronomic traits in common wheat (T. aestivum L.) PLoS One 11(7):e0159343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Flint-Garcia SA, Thuillet AC, Yu J, Pressoir G, Romero SM, Mitchell SE, Doebley J, Kresovich S, Goodman MM, Buckler ES (2005) Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J 44(6):1054–1064

    Article  CAS  PubMed  Google Scholar 

  18. Portis E, Cericola F, Barchi L, Toppino L, Acciarri N, Pulcini L, Sala T, Lanteri S, Rotino GL (2015) Association mapping for fruit, plant and leaf morphology traits in eggplant. PLoS One 10(8):e0135200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Riedelsheimer C, Lisec J, Czedik-Eysenberg A, Sulpice R, Flis A, Grieder C, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012) Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc Natl Acad Sci 109(23):8872–8877

    Article  PubMed  PubMed Central  Google Scholar 

  20. Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler IV ES (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet 28(3):286

    Article  CAS  PubMed  Google Scholar 

  21. 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(8):904

    Article  CAS  PubMed  Google Scholar 

  22. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959. PMID: 10835412

    PubMed  PubMed Central  CAS  Google Scholar 

  23. Zhao K, Aranzana MJ, Kim S, Lister C, Shindo C, Tang C, Toomajian C, Zheng H, Dean C, Marjoram P, Nordborg M (2007) An Arabidopsis example of association mapping in structured samples. PLoS Genet 3(1):e4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Aranzana MJ, Kim S, Zhao K, Bakker E, Horton M, Jakob K, Lister C, Molitor J, Shindo C, Tang C, Toomajian C (2005) Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes. PLoS Genet 1(5):e60

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chhatre VE (2013) Population structure, association mapping of economic traits and landscape genomics of east Texas loblolly pine (Pinus taeda L.) Texas A&M University, College Station

    Google Scholar 

  26. Pritchard JK, Donnelly P (2001) Case–control studies of association in structured or admixed populations. Theor Popul Biol 60(3):227–237

    Article  CAS  PubMed  Google Scholar 

  27. Satten GA, Flanders WD, Yang Q (2001) Accounting for unmeasured population substructure in case-control studies of genetic association using a novel latent-class model. Am J Hum Genet 68(2):466–477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rosenberg NA, Burke T, Elo K, Feldman MW, Freidlin PJ, Groenen MA, Hillel J, Mäki-Tanila A, Tixier-Boichard M, Vignal A, Wimmers K (2001) Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds. Genetics 159(2):699–713

    PubMed  PubMed Central  CAS  Google Scholar 

  29. Beaumont M, Barratt EM, Gottelli D, Kitchener AC, Daniels MJ, Pritchard JK, Bruford MW (2001) Genetic diversity and introgression in the Scottish wildcat. Mol Ecol 10(2):319–336

    Article  CAS  PubMed  Google Scholar 

  30. Goossens B, Funk SM, Vidal C, Latour S, Jamart A, Ancrenaz M, Bruford MW (2002) Measuring genetic diversity in translocation programmes: principles and application to a chimpanzee release project. In: Animal conservation forum, vol 5(3). Cambridge University Press, Cambridge, pp. 225–236

    Google Scholar 

  31. Randi E, Lucchini V (2002) Detecting rare introgression of domestic dog genes into wild wolf (Canis lupus) populations by Bayesian admixture analyses of microsatellite variation. Conserv Genet 3(1):29–43

    Article  Google Scholar 

  32. Arnaud JF, Viard F, Delescluse M, Cuguen J (2003) Evidence for gene flow via seed dispersal from crop to wild relatives in Beta vulgaris (Chenopodiaceae): consequences for the release of genetically modified crop species with weedy lineages. Proc R Soc Lond B Biol Sci 270(1524):1565–1571

    Article  Google Scholar 

  33. Berry O, Tocher MD, Sarre SD (2004) Can assignment tests measure dispersal? Mol Ecol 13(3):551–561

    Article  PubMed  Google Scholar 

  34. Cegelski CC, Waits LP, Anderson NJ (2003) Assessing population structure and gene flow in Montana wolverines (Gulo Gulo) using assignment-based approaches. Mol Ecol 12(11):2907–2918

    Article  CAS  PubMed  Google Scholar 

  35. Caizergues A, Bernard-Laurent A, Brenot JF, Ellison L, Rasplus JY (2003) Population genetic structure of rock ptarmigan Lagopus Mutus in northern and Western Europe. Mol Ecol 12(8):2267–2274

    Article  CAS  PubMed  Google Scholar 

  36. Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, Feldman MW (2002) Genetic structure of human populations. Science 298(5602):2381–2385

    Article  CAS  PubMed  Google Scholar 

  37. Karp A (1997) Molecular tools in plant genetic resources conservation: a guide to the technologies (No. 2). Bioversity International, Rome

    Google Scholar 

  38. Jarne P, Lagoda PJ (1996) Microsatellites, from molecules to populations and back. Trends Ecol Evol 11(10):424–429

    Article  CAS  PubMed  Google Scholar 

  39. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164(4):1567–1587

    PubMed  PubMed Central  CAS  Google Scholar 

  40. Pritchard JK, Wen X, Falush D (2010) Documentation for STRUCTURE software, version 2.3. University of Chicago, Chicago

    Google Scholar 

  41. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14(8):2611–2620

    Article  CAS  PubMed  Google Scholar 

  42. Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2(12):e190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Pritchard JK, Wen W, Falush D (2003) Documentation for structure software: version 2

    Google Scholar 

  44. Han S, Guthridge JM, Harley IT, Sestak AL, Kim-Howard X, Kaufman KM, Gilkeson GS (2008) Osteopontin and systemic lupus erythematosus association: a probable gene-gender interaction. PLoS One 3(3):e0001757

    Article  CAS  PubMed  Google Scholar 

  45. Tian C, Gregersen PK, Seldin MF (2008) Accounting for ancestry: population substructure and genome-wide association studies. Hum Mol Genet 17(R2):R143–R150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Campbell CD, Ogburn EL, Lunetta KL, Lyon HN, Freedman ML, Groop LC, Hirschhorn JN (2005) Demonstrating stratification in a European American population. Nat Genet 37(8):868

    Article  CAS  PubMed  Google Scholar 

  47. Mir RR, Kumar N, Jaiswal V, Girdharwal N, Prasad M, Balyan HS, Gupta PK (2012) Genetic dissection of grain weight in bread wheat through quantitative trait locus interval and association mapping. Mol Breed 29(4):963–972

    Article  Google Scholar 

  48. Varshney RK, Graner A, Sorrells ME (2005) Genomics-assisted breeding for crop improvement. Trends Plant Sci 10(12):621–630

    Article  CAS  PubMed  Google Scholar 

  49. Gupta PK, Rustgi S, Kulwal PL (2005) Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol 57(4):461–485

    Article  CAS  PubMed  Google Scholar 

  50. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57(1):289–300

    Google Scholar 

  51. Bland JM, Altman DG (1995) Multiple significance tests: the Bonferroni method. BMJ 310(6973):170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Carlson CS, Eberle MA, Kruglyak L, Nickerson DA (2004) Mapping complex disease loci in whole-genome association studies. Nature 429:446–452

    Article  CAS  PubMed  Google Scholar 

  53. Garris AJ, McCouch SR, Kresovich S (2003) Population structure and its effect on haplotype diversity and linkage disequilibrium surrounding the xa5 locus of rice (Oryza sativa L.) Genetics 165(2):759–769

    PubMed  PubMed Central  Google Scholar 

  54. Simko I, Costanzo S, Haynes KG, Christ BJ, Jones RW (2004) Linkage disequilibrium mapping of a Verticillium dahliae resistance quantitative trait locus in tetraploid potato (Solanum tuberosum) through a candidate gene approach. Theor Appl Genet 108(2):217–224

    Article  CAS  PubMed  Google Scholar 

  55. Zhang J, Zhao J, Xu Y, Liang J, Chang P, Yan F, & Zou Z (2015) Genome-wide association mapping for tomato volatiles positively contributing to tomato flavor. Front Plant Sci, 6

    Google Scholar 

  56. Wei X, Jackson P A, McIntyre C L, Aitken K S, & Croft B (2006) Associations between DNA markers and resistance to diseases in sugarcane and effects of population substructure. Theor Appl Genet 114(1):155–164

    Google Scholar 

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Authors and Affiliations

  1. National Agri-Food Biotechnology Institute (NABI), Mohali, India

    M. Saba Rahim, Himanshu Sharma, Afsana Parveen & Joy K. Roy

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  1. M. Saba Rahim
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  2. Himanshu Sharma
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  3. Afsana Parveen
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  4. Joy K. Roy
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Correspondence to Joy K. Roy .

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Editors and Affiliations

  1. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Rajeev K. Varshney

  2. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Manish K. Pandey

  3. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Annapurna Chitikineni

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Saba Rahim, M., Sharma, H., Parveen, A., Roy, J.K. (2018). Trait Mapping Approaches Through Association Analysis in Plants. In: Varshney, R., Pandey, M., Chitikineni, A. (eds) Plant Genetics and Molecular Biology. Advances in Biochemical Engineering/Biotechnology, vol 164. Springer, Cham. https://doi.org/10.1007/10_2017_50

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