Theoretical and Applied Genetics

, Volume 111, Issue 1, pp 100–109 | Cite as

Estimation of gametic frequencies from F2 populations using the EM algorithm and its application in the analysis of crossover interference in rice

Original Paper

Abstract

The gametes produced in meiosis provide information on the frequency of recombination and also on the interdependence of recombination events, i.e. interference. Using F2 individuals, it is not possible in all cases to derive the gametes, which have fused, and which provide the information about interference unequivocally when three or more segregating markers are considered simultaneously. Therefore, a method was developed to estimate the gametic frequencies using a maximum likelihood approach together with the expectation maximisation algorithm. This estimation procedure was applied to F2 mapping data from rice (Oryza sativa L.) to carry out a genome-wide analysis of crossover interference. The distribution of the coefficient of coincidence in dependence on the recombination fraction revealed for all chromosomes increasing positive interference with decreasing interval size. For some chromosomes this mutual inhibition of recombination was not so strong in small intervals. The centromere had a significant effect on interference. The positive interference found in the chromosome arms were reduced significantly when the intervals considered spanned the centromere. Two chromosomes even demonstrated independent recombination and slightly negative interference for small intervals including the centromere. Different marker densities had no effect on the results. In general, interference depended on the frequency of recombination events in relation to the physical length. The strength of the centromere effect on interference seemed to depend on the strength of recombination suppression around the centromere.

Keywords

Crossover interference Oryza sativa Gametic frequencies Coefficient of coincidence Centromere 

Notes

Acknowledgements

The author thanks W.E. Weber (University of Halle, Germany) for stimulating and fruitful discussions about interference and for helpful comments on the manuscript.

Supplementary material

Table S1 Genotypes in the F2 population

122_2005_1998_ESM_supp.pdf (73 kb)
(PDF 73 KB)

References

  1. Chen M, Presting G, Barbazuk WB, Goicoechea JL, Blackmon B, Fang G, Kim H, Frisch D, Yu Y, Sun S, Higingbottom S, Phimphilai J, Phimphilai D, Thurmond S, Gaudette B, Li P, Liu J, Hatfield J, Main D, Farrar K, Henderson C, Barnett L, Costa R, et al (2002) An integrated physical and genetic map of the rice genome. Plant Cell 14:537–545Google Scholar
  2. Cheng Z, Presting GG, Buell CR, Wing RA, Jiang J (2001) High-resolution pachytene chromosome mapping of bacterial artificial chromosomes anchored by genetic markers reveals the centromere location and the distribution of genetic recombination along chromosome 10 of rice. Genetics 157:1749–1757Google Scholar
  3. Choo KHA (1998) Why is the centromere so cold? Genome Res 8:81–82Google Scholar
  4. Chua PR, Roeder GS (1997) Tam1, a telomere-associated meiotic protein, functions in chromosome synapsis and crossover interference. Genes Dev 11:1786–1800Google Scholar
  5. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. JR Statist Soc 39B:1–38Google Scholar
  6. Egel R (1995) The synaptonemal complex and the distribution of meiotic recombination events. Trends Genet 11:206–208Google Scholar
  7. Egel-Mitani M, Olson LW, Egel R (1982) Meiosis in Aspergillus nidulans: another example for lacking synaptenomal complexes in the absence of crossover interference. Hereditas 97:179–187Google Scholar
  8. Esch E, Weber WE (2002) Investigation of crossover interference in barley (Hordeum vulgare L.) using the coefficient of coincidence. Theor Appl Genet 104:786–796Google Scholar
  9. Everitt BS (1987) Introduction to optimization methods and their application in statistics. Chapman & Hall, LondonGoogle Scholar
  10. Gorlov IP, Gorlova OY (2001) Cost–benefit analysis of recombination and its application for understanding of chiasma interference. J Theor Biol 213:1–8Google Scholar
  11. Harushima Y, Yano M, Shomura A, Sato M, Shimano T, Kuboki Y, Yamamoto T, Lin SY, Antonio BA, Parco A, Kajiya H, Huang N, Yamamoto K, Nagamura Y, Kurata N, Khush GS, Sasaki T (1998) A high-density rice genetic linkage map with 2,275 markers using a single F2 population. Genetics 148:479–494Google Scholar
  12. Hasenkampf CA (1996) The synaptonemal complex—the chaperone of crossing over. Chromosome Res 4:133–140Google Scholar
  13. Hastings PJ (1988) Conversion events in fugi. In: Kucherlapati R, Smith GR (eds) Genetic recombination. American Society for Microbiology, Washington, pp 397–428Google Scholar
  14. Kaback DB, Barber D, Mahon J, Lamb J, You J (1999) Chromosome size-dependent control of meiotic reciprocal recombination in Saccharomyces cerevisiae: the role of crossover interference. Genetics 152:1475–1486Google Scholar
  15. Kleckner N (1996) Meiosis: how could it work? Proc Natl Acad Sci USA 93:8167–8174Google Scholar
  16. Kosambi DD (1944) The estimation of map distances from recombination values. Ann Eugen 12:172–175Google Scholar
  17. Künzel G, Korzun L, Meister A (2000) Cytologically integrated physical restriction fragment length polymorphism maps for the barley genome based on translocation breakpoints. Genetics 154:397–412Google Scholar
  18. Liu BH (1998) Statistical genomics: linkage, mapping and QTL analysis. CRC, Boca RatonGoogle Scholar
  19. Muller HJ (1916) The mechanism of crossing over. Am Nat 50:193–221, 284–305, 350–366, 421–434Google Scholar
  20. Munz P (1994) An analysis of interference in the fission yeast Schizosaccharomyces pombe. Genetics 137:701–707Google Scholar
  21. Novak JE, Ross-Macdonald PB, Roeder GS (2001) The budding yeast Msh4 protein functions in chromosome synapsis and the regulation of crossover distribution. Genetics 158:1013–1025Google Scholar
  22. Olson LW, Edén U, Egel-Mitani M, Egel R (1978) Asynaptic meiosis in fission yeast? Hereditas 89:189–199Google Scholar
  23. Peng JH, Korol AB, Fahima T, Röder MS, Ronin YI, Li YC, Nevo E (2000) Molecular genetic maps in wild emmer wheat, Triticum dicoccoides: genome-wide coverage, massive negative interference, and putative quasi-linkage. Genome Res 10:1509–1531Google Scholar
  24. Roeder GS (1997) Meiotic chromosomes: it takes two to tango. Genes Dev 11:2600–2621Google Scholar
  25. Snow R (1979) Maximum likelihood estimation of linkage and interference from tetrad data. Genetics 92:291–245Google Scholar
  26. Storlazzi A, Xu L, Cao L, Kleckner N (1995) Crossover and noncrossover recombination during meiosis: timing and pathway relationships. Proc Natl Acad Sci USA 92:8512–8516Google Scholar
  27. Sym M, Roeder GS (1994) Crossover interference is abolished in the absence of a synaptonemal complex protein. Cell 79:283–292Google Scholar
  28. Wang S, Wang J, Jiang J, Zhang Q (2000) Mapping of centromeric regions on the molecular linkage map of rice (Oryza sativa L.) using centromere-associated sequences. Mol Gen Genet 263:165–172Google Scholar
  29. Weber WE, Wricke G (1994) Genetic markers in plant breeding. Parey Scientific, BerlinGoogle Scholar
  30. Wilcoxon F (1946) Individual comparison of grouped data by ranking methods. J Econ Entomol 39:269–270Google Scholar
  31. Wu J, Maehara T, Shimokawa T, Yamamoto S, Harada C, Takazaki Y, Ono N, Mukai Y, Koike K, Yazaki J, Fujii F, Shomura A, Ando T, Kono I, Waki K, Yamamoto K, Yano M, Matsumoto T, Sasaki T (2002) A comprehensive rice transcript map containing 6,591 expressed sequence tag sites. Plant Cell 14:525–535Google Scholar
  32. Wu JZ, Mizuno H, Hayashi-Tsugane M, Ito Y, Chiden Y, Fujisawa M, Katagiri S, Saji S, Yoshiki S, Karasawa W, Yoshihara R, Hayashi A, Kobayashi H, Ito K, Hamada M, Okamoto M, Ikeno M, Ichikawa Y, Katayose Y, Yano M, Matsumoto T, Sasaki T (2003) Physical maps and recombination frequency of six rice chromosomes. Plant J 36:720–730Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Abteilung Angewandte GenetikUniversität HannoverHannoverGermany

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