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Dissection of genetically complex traits with extremely large pools of yeast segregants

Abstract

Most heritable traits, including many human diseases1, are caused by multiple loci. Studies in both humans and model organisms, such as yeast, have failed to detect a large fraction of the loci that underlie such complex traits2,3. A lack of statistical power to identify multiple loci with small effects is undoubtedly one of the primary reasons for this problem. We have developed a method in yeast that allows the use of much larger sample sizes than previously possible and hence permits the detection of multiple loci with small effects. The method involves generating very large numbers of progeny from a cross between two Saccharomyces cerevisiae strains and then phenotyping and genotyping pools of these offspring. We applied the method to 17 chemical resistance traits and mitochondrial function, and identified loci for each of these phenotypes. We show that the level of genetic complexity underlying these quantitative traits is highly variable, with some traits influenced by one major locus and others by at least 20 loci. Our results provide an empirical demonstration of the genetic complexity of a number of traits and show that it is possible to identify many of the underlying factors using straightforward techniques. Our method should have broad applications in yeast and can be extended to other organisms.

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Figure 1: X-QTL design and quantitative allele frequency measurement in DNA pools.
Figure 2: X-QTL detection of loci for 4-NQO resistance.
Figure 3: Genetic architecture of chemical resistance traits.
Figure 4: X-QTL mapping of mitochondrial activity by cell sorting.

References

  1. Plomin, R., Haworth, C. M. A. & Davis, O. S. P. Common disorders are quantitative traits. Nature Rev. Genet. 10, 872–878 (2009)

    Article  CAS  Google Scholar 

  2. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009)

    Article  ADS  CAS  Google Scholar 

  3. Brem, R. B. & Kruglyak, L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA 102, 1572–1577 (2005)

    Article  ADS  CAS  Google Scholar 

  4. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009)

    Article  ADS  CAS  Google Scholar 

  5. Visscher, P. M. Sizing up human height variation. Nature Genet. 40, 489–490 (2008)

    Article  CAS  Google Scholar 

  6. Michelmore, R. W., Paran, I. & Kesseli, R. V. Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc. Natl Acad. Sci. USA 88, 9828–9832 (1991)

    Article  ADS  CAS  Google Scholar 

  7. Wolyn, D. J. et al. Light-response quantitative trait loci identified with composite interval and eXtreme array mapping in Arabidopsis thaliana . Genetics 167, 907–917 (2004)

    Article  CAS  Google Scholar 

  8. Brauer, M. J., Christianson, C. M., Pai, D. A. & Dunham, M. J. Mapping novel traits by array-assisted bulk segregant analysis in Saccharomyces cerevisiae . Genetics 173, 1813–1816 (2006)

    Article  CAS  Google Scholar 

  9. Segrè, A. V., Murray, A. W. & Leu, J. Y. High-resolution mutation mapping reveals parallel experimental evolution in yeast. PLoS Biol. 4, e256 (2006)

    Article  Google Scholar 

  10. Lai, C. Q. et al. Speed-mapping quantitative trait loci using microarrays. Nature Methods 4, 839–841 (2007)

    Article  CAS  Google Scholar 

  11. Schneeberger, K. et al. SHOREmap: simultaneous mapping and mutation identification by deep sequencing. Nature Methods 6, 550–551 (2009)

    Article  CAS  Google Scholar 

  12. Tong, A. H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001)

    Article  ADS  CAS  Google Scholar 

  13. Tong, A. H. & Boone, C. High-throughput strain construction and systematic synthetic lethal screening in Saccharomyces cerevisiae . In Yeast Gene Analysis 2nd edn Methods in Microbiology Vol. 36, 369–707 (Elsevier, 2007)

    Google Scholar 

  14. Gresham, D. et al. Optimized detection of sequence variation in heterozygous genomes using DNA microarrays with isothermal-melting probes. Proc. Natl Acad. Sci. USA 107, 1482–1487 (2010)

    Article  ADS  CAS  Google Scholar 

  15. Demogines, A., Smith, E., Kruglyak, L. & Alani, E. Identification and dissection of a complex DNA repair sensitivity phenotype in Baker’s yeast. PLoS Genet. 4, e1000123 (2008)

    Article  Google Scholar 

  16. Perlstein, E. O., Ruderfer, D. M., Roberts, D. C., Schreiber, S. L. & Kruglyak, L. Genetic basis of individual differences in the response to small-molecule drugs in yeast. Nature Genet. 39, 496–502 (2007)

    Article  CAS  Google Scholar 

  17. Kim, H. S. & Fay, J. C. A combined cross analysis reveals genes with drug-specific and background-dependent effects on drug sensitivity in Saccharomyces cerevisiae . Genetics 183, 1141–1151 (2009)

    Article  CAS  Google Scholar 

  18. Steinmetz, L. M. et al. Dissecting the architecture of a quantitative trait locus in yeast. Nature 416, 326–330 (2002)

    Article  ADS  CAS  Google Scholar 

  19. Deutschbauer, A. M. & Davis, R. W. Quantitative trait loci mapped to single-nucleotide resolution in yeast. Nature Genet. 37, 1333–1340 (2005)

    Article  CAS  Google Scholar 

  20. Smith, E. N. & Kruglyak, L. Gene-environment interaction in yeast gene expression. PLoS Biol. 6, e83 (2008)

    Article  Google Scholar 

  21. Dimitrov, L. N., Brem, R. B., Kruglyak, L. & Gottschling, D. E. Polymorphisms in multiple genes contribute to the spontaneous mitochondrial genome instability of Saccharomyces cerevisiae S288C strains. Genetics 183, 365–383 (2009)

    Article  CAS  Google Scholar 

  22. Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002)

    Article  ADS  CAS  Google Scholar 

  23. Yvert, G. et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nature Genet. 35, 57–64 (2003)

    Article  CAS  Google Scholar 

  24. Gaisne, M., Becam, A. M., Verdiere, J. & Herbert, C. J. A ‘natural’ mutation in Saccharomyces cerevisiae strains derived from S288c affects the complex regulatory gene HAP1 (CYP1). Curr. Genet. 36, 195–200 (1999)

    Article  CAS  Google Scholar 

  25. Foss, E. J. et al. Genetic basis of proteome variation in yeast. Nature Genet. 39, 1369–1375 (2007)

    Article  CAS  Google Scholar 

  26. Mackay, T. F. & Lyman, R. F. Drosophila bristles and the nature of quantitative genetic variation. Phil. Trans. R. Soc. Lond. B 360, 1513–1527 (2005)

    Article  CAS  Google Scholar 

  27. Buckler, E. S. et al. The genetic architecture of maize flowering time. Science 325, 714–718 (2009)

    Article  ADS  CAS  Google Scholar 

  28. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  29. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to members of the Kruglyak laboratory for comments on this manuscript; D. Botstein, J. Gerke, G. Lang and E. Perlstein for input regarding experiments; J. Bloom, L. Parsons, D. Sangurdekar and J. Storey for advice regarding analyses; and E. Alani for sharing the EAY1467 strain. This work was supported by NIH grant R37 MH59520, a James S. McDonnell Centennial Fellowship, and the Howard Hughes Medical Institute (L.K.), a NIH postdoctoral fellowship F32 HG51762 (I.M.E.), and NIH grant P50 GM071508 to the Center for Quantitative Biology at the Lewis-Sigler Institute of Princeton University.

Author Contributions Experiments were designed by I.M.E., A.A.C. and L.K. Strains were constructed by I.M.E., Y.J., S.M. and A.A.C. Microarrays were designed by I.M.E. and D.G. Experiments were performed by I.M.E., N.T., Y.J., J.K., S.M. and A.A.C. Simulation scripts were written by I.M.E. and J.A.S. Analyses were conducted by I.M.E. The manuscript was written by I.M.E. and L.K., and incorporates comments by all other authors.

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Correspondence to Leonid Kruglyak.

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This file contains Supplementary Tables S1 - S2 and Supplementary Figures S1A-S1D, S2A-S2D, S3, S4, S5A-S5Q and S6 with legends. (PDF 5002 kb)

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Ehrenreich, I., Torabi, N., Jia, Y. et al. Dissection of genetically complex traits with extremely large pools of yeast segregants. Nature 464, 1039–1042 (2010). https://doi.org/10.1038/nature08923

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