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. 2002 Jun;161(2):915–929. doi: 10.1093/genetics/161.2.915

Precision and high-resolution mapping of quantitative trait loci by use of recurrent selection, backcross or intercross schemes.

Z W Luo 1, Chung-I Wu 1, M J Kearsey 1
PMCID: PMC1462151  PMID: 12072485

Abstract

Dissecting quantitative genetic variation into genes at the molecular level has been recognized as the greatest challenge facing geneticists in the twenty-first century. Tremendous efforts in the last two decades were invested to map a wide spectrum of quantitative genetic variation in nearly all important organisms onto their genome regions that may contain genes underlying the variation, but the candidate regions predicted so far are too coarse for accurate gene targeting. In this article, the recurrent selection and backcross (RSB) schemes were investigated theoretically and by simulation for their potential in mapping quantitative trait loci (QTL). In the RSB schemes, selection plays the role of maintaining the recipient genome in the vicinity of the QTL, which, at the same time, are rapidly narrowed down over multiple generations of backcrossing. With a high-density linkage map of DNA polymorphisms, the RSB approach has the potential of dissecting the complex genetic architecture of quantitative traits and enabling the underlying QTL to be mapped with the precision and resolution needed for their map-based cloning to be attempted. The factors affecting efficiency of the mapping method were investigated, suggesting guidelines under which experimental designs of the RSB schemes can be optimized. Comparison was made between the RSB schemes and the two popular QTL mapping methods, interval mapping and composite interval mapping, and showed that the scenario of genomic distribution of QTL that was unlocked by the RSB-based mapping method is qualitatively distinguished from those unlocked by the interval mapping-based methods.

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Selected References

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  1. Alpert K. B., Tanksley S. D. High-resolution mapping and isolation of a yeast artificial chromosome contig containing fw2.2: a major fruit weight quantitative trait locus in tomato. Proc Natl Acad Sci U S A. 1996 Dec 24;93(26):15503–15507. doi: 10.1073/pnas.93.26.15503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Darvasi A. Interval-specific congenic strains (ISCS): an experimental design for mapping a QTL into a 1-centimorgan interval. Mamm Genome. 1997 Mar;8(3):163–167. doi: 10.1007/s003359900382. [DOI] [PubMed] [Google Scholar]
  3. Darvasi A., Soller M. Advanced intercross lines, an experimental population for fine genetic mapping. Genetics. 1995 Nov;141(3):1199–1207. doi: 10.1093/genetics/141.3.1199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Frary A., Nesbitt T. C., Grandillo S., Knaap E., Cong B., Liu J., Meller J., Elber R., Alpert K. B., Tanksley S. D. fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science. 2000 Jul 7;289(5476):85–88. doi: 10.1126/science.289.5476.85. [DOI] [PubMed] [Google Scholar]
  5. Guo S. W., Lange K. Genetic mapping of complex traits: promises, problems, and prospects. Theor Popul Biol. 2000 Feb;57(1):1–11. doi: 10.1006/tpbi.2000.1449. [DOI] [PubMed] [Google Scholar]
  6. Haley C. S., Knott S. A. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity (Edinb) 1992 Oct;69(4):315–324. doi: 10.1038/hdy.1992.131. [DOI] [PubMed] [Google Scholar]
  7. Hill W. G. On the theory of artificial selection in finite populations. Genet Res. 1969 Apr;13(2):143–163. doi: 10.1017/s0016672300002858. [DOI] [PubMed] [Google Scholar]
  8. Hill W. G. Selection with recurrent backcrossing to develop congenic lines for quantitative trait loci analysis. Genetics. 1998 Mar;148(3):1341–1352. doi: 10.1093/genetics/148.3.1341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Kearsey M. J., Farquhar A. G. QTL analysis in plants; where are we now? Heredity (Edinb) 1998 Feb;80(Pt 2):137–142. doi: 10.1046/j.1365-2540.1998.00500.x. [DOI] [PubMed] [Google Scholar]
  10. Lander E. S., Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 1989 Jan;121(1):185–199. doi: 10.1093/genetics/121.1.185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Lander E. S., Schork N. J. Genetic dissection of complex traits. Science. 1994 Sep 30;265(5181):2037–2048. doi: 10.1126/science.8091226. [DOI] [PubMed] [Google Scholar]
  12. Luo Z. W., Tao S. H., Zeng Z. B. Inferring linkage disequilibrium between a polymorphic marker locus and a trait locus in natural populations. Genetics. 2000 Sep;156(1):457–467. doi: 10.1093/genetics/156.1.457. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Luo Z. W., Thompson R., Woolliams J. A. A population genetics model of marker-assisted selection. Genetics. 1997 Jul;146(3):1173–1183. doi: 10.1093/genetics/146.3.1173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Luo Z. W., Wu C. I. Modeling linkage disequilibrium between a polymorphic marker locus and a locus affecting complex dichotomous traits in natural populations. Genetics. 2001 Aug;158(4):1785–1800. doi: 10.1093/genetics/158.4.1785. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Mackay T. F. The genetic architecture of quantitative traits. Annu Rev Genet. 2001;35:303–339. doi: 10.1146/annurev.genet.35.102401.090633. [DOI] [PubMed] [Google Scholar]
  16. Manly K. F., Olson J. M. Overview of QTL mapping software and introduction to map manager QT. Mamm Genome. 1999 Apr;10(4):327–334. doi: 10.1007/s003359900997. [DOI] [PubMed] [Google Scholar]
  17. Marth G., Yeh R., Minton M., Donaldson R., Li Q., Duan S., Davenport R., Miller R. D., Kwok P. Y. Single-nucleotide polymorphisms in the public domain: how useful are they? Nat Genet. 2001 Apr;27(4):371–372. doi: 10.1038/86864. [DOI] [PubMed] [Google Scholar]
  18. Mott R., Talbot C. J., Turri M. G., Collins A. C., Flint J. A method for fine mapping quantitative trait loci in outbred animal stocks. Proc Natl Acad Sci U S A. 2000 Nov 7;97(23):12649–12654. doi: 10.1073/pnas.230304397. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Rabinowitz D. A transmission disequilibrium test for quantitative trait loci. Hum Hered. 1997 Nov-Dec;47(6):342–350. doi: 10.1159/000154433. [DOI] [PubMed] [Google Scholar]
  20. Wang D. G., Fan J. B., Siao C. J., Berno A., Young P., Sapolsky R., Ghandour G., Perkins N., Winchester E., Spencer J. Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science. 1998 May 15;280(5366):1077–1082. doi: 10.1126/science.280.5366.1077. [DOI] [PubMed] [Google Scholar]
  21. Xiong M., Guo S. W. Fine-scale mapping of quantitative trait loci using historical recombinations. Genetics. 1997 Apr;145(4):1201–1218. doi: 10.1093/genetics/145.4.1201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Zeng Z. B. Precision mapping of quantitative trait loci. Genetics. 1994 Apr;136(4):1457–1468. doi: 10.1093/genetics/136.4.1457. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Zöllner S., von Haeseler A. A coalescent approach to study linkage disequilibrium between single-nucleotide polymorphisms. Am J Hum Genet. 2000 Feb;66(2):615–628. doi: 10.1086/302766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. de la Chapelle A., Wright F. A. Linkage disequilibrium mapping in isolated populations: the example of Finland revisited. Proc Natl Acad Sci U S A. 1998 Oct 13;95(21):12416–12423. doi: 10.1073/pnas.95.21.12416. [DOI] [PMC free article] [PubMed] [Google Scholar]

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