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Finding the sources of missing heritability in a yeast cross

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Abstract

For many traits, including susceptibility to common diseases in humans, causal loci uncovered by genetic-mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this ‘missing heritability’ have been proposed1. Here we use a large cross between two yeast strains to accurately estimate different sources of heritable variation for 46 quantitative traits, and to detect underlying loci with high statistical power. We find that the detected loci explain nearly the entire additive contribution to heritable variation for the traits studied. We also show that the contribution to heritability of gene–gene interactions varies among traits, from near zero to approximately 50 per cent. Detected two-locus interactions explain only a minority of this contribution. These results substantially advance our understanding of the missing heritability problem and have important implications for future studies of complex and quantitative traits.

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Figure 1: Heritability for 46 yeast traits.
Figure 2: Most additive heritability is explained by detected QTL.
Figure 3: QTL detection for a complex trait.
Figure 4: Prediction of segregant trait values from QTL phenotypes.
Figure 5: Non-additive genetic variance explained by QTL–QTL interactions.

Change history

  • 19 February 2013

    A minor change was made to the Online Methods section.

References

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Hill, W. G. Understanding and using quantitative genetic variation. Phil. Trans. R. Soc. Lond. B 365, 73–85 (2010)

    Article  Google Scholar 

  3. Mackay, T. F. C., Stone, E. A. & Ayroles, J. F. The genetics of quantitative traits: challenges and prospects. Nature Rev. Genet. 10, 565–577 (2009)

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Atwell, S. et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mackay, T. F. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Aylor, D. L. et al. Genetic analysis of complex traits in the emerging Collaborative Cross. Genome Res. 21, 1213–1222 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rockman, M. V. The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution 66, 1–17 (2012)

    Article  PubMed  Google Scholar 

  9. Goldstein, D. B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009)

    Article  CAS  PubMed  Google Scholar 

  10. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genet. 42, 565–569 (2010)

    Article  CAS  PubMed  Google Scholar 

  11. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pritchard, J. K. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69, 124–137 (2001)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics edn 4 (Longman, 1996)

    Google Scholar 

  15. Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nature Rev. Genet. 11, 446–450 (2010)

    Article  CAS  PubMed  Google Scholar 

  16. Ehrenreich, I. M., Gerke, J. P. & Kruglyak, L. Genetic dissection of complex traits in yeast: insights from studies of gene expression and other phenotypes in the BY×RM cross. Cold Spring Harb. Symp. Quant. Biol. 74, 145–153 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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  PubMed  PubMed Central  Google Scholar 

  18. Ehrenreich, I. M. et al. Dissection of genetically complex traits with extremely large pools of yeast segregants. Nature 464, 1039–1042 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ruderfer, D. M., Pratt, S. C., Seidel, H. S. & Kruglyak, L. Population genomic analysis of outcrossing and recombination in yeast. Nature Genet. 38, 1077–1081 (2006)

    Article  CAS  PubMed  Google Scholar 

  20. Visscher, P. M. et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2, e41 (2006)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Orr, H. A. Adaptation and the cost of complexity. Evolution 54, 13–20 (2000)

    Article  CAS  PubMed  Google Scholar 

  22. Storey, J. D., Akey, J. M. & Kruglyak, L. Multiple locus linkage analysis of genomewide expression in yeast. PLoS Biol. 3, e267 (2005)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Brem, R. B., Storey, J. D., Whittle, J. & Kruglyak, L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436, 701–703 (2005)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Dowell, R. D. et al. Genotype to phenotype: a complex problem. Science 328, 469 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hill, W. G., Goddard, M. E. & Visscher, P. M. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008 (2008)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Maller, J. et al. Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nature Genet. 38, 1055–1059 (2006)

    Article  CAS  PubMed  Google Scholar 

  27. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Tennessen, J. A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Keinan, A. & Clark, A. G. Recent explosive human population growth has resulted in an excess of rare genetic variants. Science 336, 740–743 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Nelson, M. R. et al. An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people. Science 337, 100–104 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Amberg, D. C., Burke, D. & Strathern, J. N. Methods in Yeast Genetics: a Cold Spring Harbor Laboratory Course Manual (Cold Spring Harbor Laboratory Press, 2005)

    Google Scholar 

  32. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2012)

  33. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Broman, K. W., Wu, H., Sen, S. & Churchill, G. A. R/qtl: QTL mapping in experimental crosses. Bioinformatics 19, 889–890 (2003)

    Article  CAS  PubMed  Google Scholar 

  36. Abramoff, M. D., Magalhaes, P. J. & Ram, S. J. Image Processing with ImageJ. Biophotonics International 11, 36–42 (2004)

    Google Scholar 

  37. Pau, G., Fuchs, F., Sklyar, O., Boutros, M. & Huber, W. EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26, 979–981 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Loader, C. locfit: Local Regression, Likelihood and Density Estimation http://CRAN.R-project.org/package=locfit (2012)

    Google Scholar 

  39. Bates, D., Maechler, M. & Bolker, B. lme4: Linear Mixed-Effects Models Using S4 Classes http://CRAN.R-project.org/package=lme4 (2011)

    Google Scholar 

  40. Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Endelman, J. B. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4, 250–255 (2011)

    Article  Google Scholar 

  42. Visscher, P. M. Variation of estimates of SNP and haplotype diversity and linkage disequilibrium in samples from the same population due to experimental and evolutionary sample size. Ann. Hum. Genet. 71, 119–126 (2007)

    Article  CAS  PubMed  Google Scholar 

  43. Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits edn 1 (Sinauer Associates, 1998)

    Google Scholar 

  44. Chen, L. & Storey, J. D. Relaxed significance criteria for linkage analysis. Genetics 173, 2371–2381 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Doerge, R. W. & Churchill, G. A. Permutation tests for multiple loci affecting a quantitative character. Genetics 142, 285–294 (1996)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kruglyak, L. & Nickerson, D. A. Variation is the spice of life. Nature Genet. 27, 234–236 (2001)

    Article  CAS  PubMed  Google Scholar 

  47. Birney, E. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007)

    Article  ADS  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank D. Botstein, M. McClean, E. Andersen, F. Albert, S. Treusch, R. Ghosh and X. Wang for comments on the manuscript, Y. Jia and S. Schrader for technical assistance and E. Lander for discussions. This work was supported by National Institutes of Health (NIH) grants R37 MH59520 and R01 GM102308, a James S. McDonnell Centennial Fellowship, and the Howard Hughes Medical Institute (L.K.), a National Science Foundation (NSF) fellowship (J.S.B.), 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 information

Authors and Affiliations

Authors

Contributions

Experiments were designed by J.S.B., I.M.E. and L.K. Experiments were performed by J.S.B., I.M.E., W.T.L. and T.-L.V.L. Analyses were conducted by J.S.B. The manuscript was written by J.S.B. and L.K. and incorporates comments by all other authors.

Corresponding author

Correspondence to Leonid Kruglyak.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-4 and a link to the additional Supplementary Data and Code. (PDF 536 kb)

Supplementary Table 1

This table contains drug doses, heritability statistics, and QTL summary statistics for traits investigated in this study. (XLS 36 kb)

Supplementary Table 2

This table shows the additive genetic variance, partitioned by chromosome, for each trait. (XLS 34 kb)

Supplementary Table 3

This is a table of detected QTL. Positions, effect sizes, confidence intervals and genes underneath detected QTL for each trait are listed. (XLS 522 kb)

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Bloom, J., Ehrenreich, I., Loo, W. et al. Finding the sources of missing heritability in a yeast cross. Nature 494, 234–237 (2013). https://doi.org/10.1038/nature11867

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