Theoretical and Applied Genetics

, Volume 126, Issue 6, pp 1419–1430 | Cite as

Family-based association mapping in crop species

Review

Abstract

Identification of allelic variants associated with complex traits provides molecular genetic information associated with variability upon which both artificial and natural selections are based. Family-based association mapping (FBAM) takes advantage of linkage disequilibrium among segregating progeny within crosses and among parents to provide greater power than association mapping and greater resolution than linkage mapping. Herein, we discuss the potential adaption of human family-based association tests and quantitative transmission disequilibrium tests for use in crop species. The rapid technological advancement of next generation sequencing will enable sequencing of all parents in a planned crossing design, with subsequent imputation of genotypes for all segregating progeny. These technical advancements are easily adapted to mating designs routinely used by plant breeders. Thus, FBAM has the potential to be widely adopted for discovering alleles, common and rare, underlying complex traits in crop species.

References

  1. Abecasis GR, Cardon LR, Cookson WOC (2000) A general test of association for quantitative traits in nuclear families. Am J Hum Genet 66:279–292PubMedCrossRefGoogle Scholar
  2. Allison DB, Heo M, Kaplan N, Martin ER (1999) Sibling based tests of linkage and association for quantitative traits. Am J Hum Genet 64:1754–1764PubMedCrossRefGoogle Scholar
  3. Anderson L, Georges M (2004) Domestic-animal genomics: deciphering the genetics of complex traits. Nat Rev Genetics 5:202–212CrossRefGoogle Scholar
  4. Bernardo R (2009) Should maize doubled haploids be induced among F1 or F2 plants. Theor Appl Genet 119:255–262PubMedCrossRefGoogle Scholar
  5. Bernardo R (2010) Breeding for quantitative traits in plants. Stemma Press, MinnesotaGoogle Scholar
  6. 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 113:206–224PubMedCrossRefGoogle Scholar
  7. Bordes J, Dumas de Vaul R, Lapierre A, Pollacsek M (1997) Haplodiploidization of maize (Zea mays L.) through induced gynogenesis assisted by glossy markers and its use in breeding. Agronomie 17:291–297CrossRefGoogle Scholar
  8. Buckler E, Gore M (2007) An Arabidopsis haplotype map takes root. Nat Genet 39:1056–1057PubMedCrossRefGoogle Scholar
  9. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C et al (2009) The genetic architecture of maize flowering time. Science 325:714–718PubMedCrossRefGoogle Scholar
  10. Burdick JT, Chen W, Abecasis GR, Cheung VG (2006) In silico method for inferring genotypes in pedigrees. Nat Genet 38:1002–1004PubMedCrossRefGoogle Scholar
  11. Burgueno J, Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype x environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719CrossRefGoogle Scholar
  12. Chase SS (1951) Production of homozygous diploids of maize from monoploids. Agron J 44:263–267CrossRefGoogle Scholar
  13. Churchill GA et al (2004) The collaborative cross: a community resource for the genetic analysis of complex traits. Nat Genet 36d:1133–1137CrossRefGoogle Scholar
  14. Cirulli ET, Goldstein DB (2010) Uncovering the roles of rare variants in common disease through whole genome sequencing. Nat Rev Genet 11:415–425PubMedCrossRefGoogle Scholar
  15. Darvasi A, Weinreb A, Minke V, Weller JI, Soller M (1993) Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134:943–951PubMedGoogle Scholar
  16. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB (2010) Rare variants create synthetic genome wide associations. PLoS Biol 6:e1000294CrossRefGoogle Scholar
  17. Farnir F, Grisart B, Coppieters W, Riquet J, Berzi P, Cambisano N, Karim L et al (2002) Simultaneous mining of linkage and linkage disequilibrium to fine map quantitative trait loci in outbred hal-sib pedigrees: revisiting the location of a quantitative trait locus with major effect on milk production on bovine chromosome 14. Genetics 161:275–287PubMedGoogle Scholar
  18. Fulker DW, Cherny SS, Sham PC, Hewitt JK (1999) Combined linkage and association sib-pair analysis for quantitative traits. Am J Hum Genet 64:259–267PubMedCrossRefGoogle Scholar
  19. Gale MD, Youssefian S (1985) Dwarfing genes in wheat. In: Russell GE (ed) Progress in plant breeding 1. Butterworths, London, pp 1–35Google Scholar
  20. George AW, Visscher PM, Haley CS (2000) Mapping quantitative trait loci in complex pedigree: a two- step variance component approach. Genetics 156:2081–2092PubMedGoogle Scholar
  21. Guo B, Beavis WD (2011) In silico genotyping of the maize nested association mapping population. Mol Breed 27:107–113PubMedCrossRefGoogle Scholar
  22. Guo B, Sleper DA, Sun J, Nguyen HT, Arelli PR, Shannon JG (2006) Pooled analysis of data from multiple quantitative trait locus mapping populations. Theor Appl Genet 113:39–48PubMedCrossRefGoogle Scholar
  23. Guo B, Sleper DA, Beavis WD (2010) Nested association mapping for identification of functional markers. Genetics 186:373–383PubMedCrossRefGoogle Scholar
  24. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 89:315–324CrossRefGoogle Scholar
  25. Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:95–108PubMedCrossRefGoogle Scholar
  26. Horvath S, Xu X, Lake SL, Silverman EK, Weiss ST, Laird NM (2004) Family based tests for associating haplotypes with general phenotype data: application to asthma genetics. Genet Epidemiol 26:61–69PubMedCrossRefGoogle Scholar
  27. Huang X, Wei X, Sang T, Zhao Q, Feng Q et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967PubMedCrossRefGoogle Scholar
  28. International Hapmap Consortium (2003) The international HapMap project. Nature 426:789–796CrossRefGoogle Scholar
  29. Jansen RC, Jannink JL, Beavis WD (2003) Mapping quantitative trait loci in plant breeding populations: use of parental haplotype sharing. Crop Sci 43:829–834CrossRefGoogle Scholar
  30. Kiezun A, Garimella K, Do R, Stitziel NO, Neale BM et al (2012) Exome sequencing and the genetic basis of complex traits. Nat Genet 44:623–630PubMedCrossRefGoogle Scholar
  31. Kingsmore SF, Lindquist IE, Mudge J, Gesler DD, Beavis WD (2008) Genome-wide association studies: progress and potential for drug discovery and development. Nat Rev Drug Discov 7:221–230PubMedCrossRefGoogle Scholar
  32. Kump KL, Bradbury PJ, Buckler ES, Belcher AR, Oropeza-Rosas M 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
  33. Laird NM, Lange C (2006) Family based designs in the age of large scale gene association studies. Nat Rev Genetics 7:385–394CrossRefGoogle Scholar
  34. Lam HM, Xu X, Liu X, Chen W, Yang G, Wong FL, Li MW, Qin N, Wang B, Li J, Jian M, Wang J, Shao G, Wang J, Sun SM, Zhang G (2010) Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet 42:1053–1059PubMedCrossRefGoogle Scholar
  35. Lander ES, Bostein (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199PubMedGoogle Scholar
  36. Lange C, DeMeo DL, Laird NM (2002) Power and design considerations for a general class of family based association tests: quantitative traits. Am J Hum Genet 71:1330–1341PubMedCrossRefGoogle Scholar
  37. Lee SH, Werf JHJ (2004) The efficiency of designs for fine mapping of quantitative trait loci using combined linkage disequilibrium and linkage. Genet Sel Evol 36:145–161PubMedCrossRefGoogle Scholar
  38. Li R, Lyons MA, Wittenburg H, Paigen B, Churchill GA (2005) Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping. Genetics 169:1699–1709PubMedCrossRefGoogle Scholar
  39. Lund MS, Sorensen P, Guldbrandtsen B, Sorensen DA (2003) Multi fine mapping of quantitative trait loci using combined linkage disequilibria and linkage analysis. Genetics 163:405–410PubMedGoogle Scholar
  40. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, Inc, SunderlandGoogle Scholar
  41. Martin ER, Monks SA, Warren LL, Kaplan NL (2000) A test for linkage and association in general pedigrees: the pedigree disequilibrium test. Am J Hum Genet 67:146–154PubMedCrossRefGoogle Scholar
  42. Metzker M (2010) Sequencing technologies-the next generation. Nat Rev Genetics 11:31–46CrossRefGoogle Scholar
  43. Meuwissen THE, Karlsen A, Lien S, Olsaker I, Goddard ME (2002) Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping. Genetics 161:373–379PubMedGoogle Scholar
  44. Monks SA, Kaplan NL (2000) Removing the sampling restrictions from family based tests of association for a quantitative trait locus. Am J Hum Genet 66:576–592PubMedCrossRefGoogle Scholar
  45. Morrell PL, Buckler ES, Ross-Ibarra J (2012) Crop genomics: advances and applications. Nat Rev Genetics 13:85–96Google Scholar
  46. Rabinowitz D, Laird N (2000) A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum Hered 50:211–223PubMedCrossRefGoogle Scholar
  47. Rival MA, Beaudoin M, Gardet A, Stevens C, Sharma Y et al (2011) Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat Genet 43:1066–1073CrossRefGoogle Scholar
  48. SAS Institute (2004) SAS/STAT 9.1 user’s guide. SAS institute, Cary, NCGoogle Scholar
  49. Snape JW (1988) The detection and estimation of linkage using doubled haploid or single seed descent populations. Theor Appl Genet 76:125–128CrossRefGoogle Scholar
  50. So Y, Edwards J (2011) Predictive ability assessment of linear mixed models in multi-environment trials in corn. Crop Sci 51:542–552CrossRefGoogle Scholar
  51. Stanklewicz P, Lupski JR (2010) Structural variation in the human genome and its role in disease. Annu Rev Med 61:437–455CrossRefGoogle Scholar
  52. Steen KV, McQueen MB, Herbert A, Raby B, Lyon H et al (2005) Genomic screening and replication using the same data set in family based association testing. Nat Genet 37:683–691PubMedCrossRefGoogle Scholar
  53. Stich B, Melchinger AE, Piepho H, Heckenberger M, Manurer HP, Reif JC (2006) A new test for family based association mapping with inbred lines from plant breeding programs. Theor Appl Genet 113:1121–1130PubMedCrossRefGoogle Scholar
  54. Tian F, Bradbury PJ, Brown PJ, Sun Q, Flint-Garcia S et al (2011) Genome-wide association study of maize identifies genes affecting leaf architecture. Nat Genet 43:159–162PubMedCrossRefGoogle Scholar
  55. Valdar W, Holmes CC, Mott R, Flint J (2009) Mapping in structured populations by resample model averaging. Genetics 182:1263–1277PubMedCrossRefGoogle Scholar
  56. VSN international (2010) ASReml 3. VSSN international Ltd., Hemel HempsteadGoogle Scholar
  57. Xu S (1998) Mapping quantitative trait loci using multiple families of lines crosses. Genetics 148:517–524PubMedGoogle Scholar
  58. Yan J, Kandianis CB, Harjes CE, Bai L, Kim EH et al (2010) Rare genetic variation at Zea mays crtRB1 increases β-carotene in maize grain. Nat Genet 42:322–327PubMedCrossRefGoogle Scholar
  59. Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M et al (2006) A unified mixed model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208PubMedCrossRefGoogle Scholar
  60. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Syngenta Biotechnology, IncSlaterUSA
  2. 2.Syngenta Biotechnology, IncResearch Triangle ParkUSA
  3. 3.Iowa State UniversityAmesUSA

Personalised recommendations