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Theoretical and Applied Genetics

, Volume 121, Issue 6, pp 1071–1082 | Cite as

Effects of missing marker and segregation distortion on QTL mapping in F2 populations

  • Luyan Zhang
  • Shiquan Wang
  • Huihui Li
  • Qiming Deng
  • Aiping Zheng
  • Shuangcheng Li
  • Ping Li
  • Zhonglai Li
  • Jiankang Wang
Original Paper

Abstract

Missing marker and segregation distortion are commonly encountered in actual quantitative trait locus (QTL) mapping populations. Our objective in this study was to investigate the impact of the two factors on QTL mapping through computer simulations. Results indicate that detection power decreases with increasing levels of missing markers, and the false discovery rate increases. Missing markers have greater effects on smaller effect QTL and smaller size populations. The effect of missing markers can be quantified by a population with a reduced size similar to the marker missing rate. As for segregation distortion, if the distorted marker is not closely linked with any QTL, it will not have significant impact on QTL mapping; otherwise, the impact of the distortion will depend on the degree of dominance of QTL, frequencies of the three marker types, the linkage distance between the distorted marker and QTL, and the mapping population size. Sometimes, the distortion can result in a higher genetic variance than that of non-distortion, and therefore benefits the detection of linked QTL. A formula of the ratio of genetic variance explained by QTL under distortion and non-distortion was given in this study, so as to easily determine whether the segregation distortion marker (SDM) increases or decreases the QTL detection power. The effect of SDM decreases rapidly as its linkage relationship with QTL becomes looser. In general, distorted markers will not have a great effect on the position and effect estimations of QTL, and their effects can be ignored in large-size mapping populations.

Keywords

Quantitative Trait Locus Quantitative Trait Locus Mapping Segregation Distortion Dominance Effect Detection Power 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the National 973 Projects of China (no. 2006CB101700) and Natural Science Foundation of China (no. 30771351).

Supplementary material

122_2010_1372_MOESM1_ESM.doc (378 kb)
Supplementary material (DOC 377 kb)

References

  1. Barton NH, Keightley PD (2002) Understanding quantitative genetic variation. Nat Rev Genet 3:11–21CrossRefPubMedGoogle Scholar
  2. Browning SR (2008) Missing data imputation and haplotype phase inference for genome-wide association studies. Hum Genet 124:439–450CrossRefPubMedGoogle Scholar
  3. Butruille DV, Guries RP, Osborn TC (1999) Linkage analysis of molecular markers and quantitative trait loci in populations of inbred backcross lines of Brassica napus L. Genetics 153:949–964PubMedGoogle Scholar
  4. Doerge RW (2002) Mapping and analysis of quantitative trait loci in experimental populations. Nat Rev Genet 3:43–52CrossRefPubMedGoogle Scholar
  5. Garcia-Dorado A, Gallego A (1992) On the use of the classical tests for detecting linkage. Heredity 83(2):143–146Google Scholar
  6. Hedrick PW, Muona O (1990) Linkage of viability genes to marker loci in selfing organisms. Heredity 64:67–72CrossRefGoogle Scholar
  7. Jiang C, Zeng Z (1997) Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101:47–58CrossRefPubMedGoogle Scholar
  8. Li H, Ye G, Wang J (2007) A modified algorithm for the improvement of composite interval mapping. Genetics 175:361–374CrossRefPubMedGoogle Scholar
  9. Little RJA (1992) Regression with missing X’s: a review. J Am Stat Assoc 87:1227–1237CrossRefGoogle Scholar
  10. Lorieux M, Goffinet B, Perrier X, González de León D, Lanaud C (1995a) Maximum likelihood models for mapping genetic markers showing segregation distortion. 1. Backcross population. Theor Appl Genet 90:73–80Google Scholar
  11. Lorieux M, Perrier X, Goffinet B, Lanaud C, González de León D (1995b) Maximum likelihood models for mapping genetic markers showing segregation distortion. 2. F2 population. Theor Appl Genet 90:81–89Google Scholar
  12. Luo L, Xu S (2003) Mapping viability loci using molecular markers. Heredity 90:459–467CrossRefPubMedGoogle Scholar
  13. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, Inc, Sunderland, MAGoogle Scholar
  14. Mackay TFC (2001) Quantitative trait loci in Drosophila. Nat Rev Genet 2:11–20CrossRefPubMedGoogle Scholar
  15. Martínez O, Curnow RN (1994) Missing markers when estimating quantitative trait loci using regression mapping. Heredity 73:198–206CrossRefGoogle Scholar
  16. Paterson AH, Damon S, Hewitt JD, Zamir D, Rabinowitch HD, Lincoln SE, Lander ES, Tanksley SD (1991) Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments. Genetics 127:181–197PubMedGoogle Scholar
  17. Tai GCC, Seabrook JEA, Aziz AN (2000) Linkage analysis of anther-derived monoploids showing distorted segregation of molecular markers. Theor Appl Genet 101:126–130CrossRefGoogle Scholar
  18. Wang J (2009) Inclusive composite interval mapping of quantitative trait genes. Acta Agronom Sinica 35(2):239–245CrossRefGoogle Scholar
  19. Wang J, van Ginkel M, Podlich D, Ye G, Trethowan R, Pfeiffer W, DeLacy IH, Cooper M, Rajaram S (2003) Comparison of two breeding strategies by computer simulation. Crop Sci 43:1764–1773CrossRefGoogle Scholar
  20. Wang J, van Ginkel M, Trethowan R, Ye G, Delacy I, Podlich D, Cooper M (2004) Simulating the effects of dominance and epistasis on selection response in the CIMMYT Wheat Breeding Program using QuCim. Crop Sci 44:2006–2018CrossRefGoogle Scholar
  21. Xu S (2008) Quantitative trait locus mapping can benefit from segregation distortion. Genetics 180:2201–2208CrossRefPubMedGoogle Scholar
  22. Ye S, Zhang Q, Li J, Zhao B, Li P (2005) QTL mapping for yield component traits using (Pei’ai 64s/Nipponbare) F2 population. Acta Agronom Sinica 31:1620–1627 (in Chinese with English abstract)Google Scholar
  23. Ye S, Zhang Q, Li J, Zhao B, Yin D, Li P (2007) Mapping of quantitative trait loci for six agronomic traits of rice in Pei’ai 64s/Nipponbare F2 population. Chin J Rice Sci 21(1):39–43 (in Chinese with English abstract)Google Scholar
  24. Yu Z, Schaid DJ (2007) Methods to impute missing genotypes for population data. Hum Genet 122:495–504CrossRefPubMedGoogle Scholar
  25. Zhang L, Li H, Li Z, Wang J (2008) Interactions between markers can be caused by the dominance effect of quantitative trait loci. Genetics 180:1177–1190CrossRefPubMedGoogle Scholar
  26. Zhu C, Wang C, Zhang Y (2007) Modeling segregation distortion for viability selection. I. Reconstruction of linkage maps with distorted markers. Theor Appl Genet 114:295–305CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Luyan Zhang
    • 1
    • 2
  • Shiquan Wang
    • 3
  • Huihui Li
    • 2
  • Qiming Deng
    • 3
  • Aiping Zheng
    • 3
  • Shuangcheng Li
    • 3
  • Ping Li
    • 3
  • Zhonglai Li
    • 1
  • Jiankang Wang
    • 2
  1. 1.School of Mathematical SciencesBeijing Normal UniversityBeijingChina
  2. 2.Institute of Crop ScienceThe National Key Facility for Crop Gene Resources and Genetic Improvement, CIMMYT China, Chinese Academy of Agricultural SciencesBeijingChina
  3. 3.Rice Research InstituteSichuan Agricultural UniversityChengduChina

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