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Natural selection on protein-coding genes in the human genome

Nature volume 437, pages 1153–1157 (2005)Cite this article

Abstract

Comparisons of DNA polymorphism within species to divergence between species enables the discovery of molecular adaptation in evolutionarily constrained genes as well as the differentiation of weak from strong purifying selection1,2,3,4. The extent to which weak negative and positive darwinian selection have driven the molecular evolution of different species varies greatly5,6,7,8,9,10,11,12,13,14,15,16, with some species, such as Drosophila melanogaster, showing strong evidence of pervasive positive selection6,7,8,9, and others, such as the selfing weed Arabidopsis thaliana, showing an excess of deleterious variation within local populations9,10. Here we contrast patterns of coding sequence polymorphism identified by direct sequencing of 39 humans for over 11,000 genes to divergence between humans and chimpanzees, and find strong evidence that natural selection has shaped the recent molecular evolution of our species. Our analysis discovered 304 (9.0%) out of 3,377 potentially informative loci showing evidence of rapid amino acid evolution. Furthermore, 813 (13.5%) out of 6,033 potentially informative loci show a paucity of amino acid differences between humans and chimpanzees, indicating weak negative selection and/or balancing selection operating on mutations at these loci. We find that the distribution of negatively and positively selected genes varies greatly among biological processes and molecular functions, and that some classes, such as transcription factors, show an excess of rapidly evolving genes, whereas others, such as cytoskeletal proteins, show an excess of genes with extensive amino acid polymorphism within humans and yet little amino acid divergence between humans and chimpanzees.

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Figure 1: Summary distributions of McDonald–Kreitman cell entries and mkprf analyses.
Figure 2: A selection map of the human genome.
Figure 3: Association between negative selection and disease.

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Acknowledgements

We thank K. Thornton and B. Payseur for suggestions during the analysis. Some of the analysis was supported by NIH grants to C.D.B., R.N. and A.G.C. We also acknowledge the help of J. Pillardy and the Cornell University Theory Center Computational Biology Service Unit. Author Contributions S.G., D.M.T., D.C., T.J.W., J.J.S., M.D.A. and M.C. conceived, designed and performed the experiments. C.D.B., A.F.-A., A.G.C., S.W., R.N. and M.J.H. analysed the data.

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Author notes

  1. Michele Cargill and Andrew G. Clark: *These authors contributed equally to this work

Authors and Affiliations

  1. Department of Biological Statistics and Computational Biology, 101 Biotechnology Building, Cornell University, New York, 14853, Ithaca, USA

    Carlos D. Bustamante, Adi Fledel-Alon, Scott Williamson, Rasmus Nielsen, Melissa Todd Hubisz & Ryan D. Hernandez

  2. Center for Bioinformatics, University of Copenhagen, Universitetsparken 15, DK-2100, Copenhagen, Denmark

    Rasmus Nielsen

  3. Applied Biosystems, 45 West Gude Drive, Maryland, 20850, Rockville, USA

    Stephen Glanowski & David M. Tanenbaum

  4. Celera Diagnostics, 1401 Harbor Bay Parkway, California, 94502, Alameda, USA

    Thomas J. White, John J. Sninsky, Daniel Civello & Michele Cargill

  5. Department of Genetics, Case Western Reserve University, 10900 Euclid Avenue, Ohio, 44106, Cleveland, USA

    Mark D. Adams

  6. Department of Molecular Biology and Genetics, 227 Biotechnology Building, Cornell University, New York, 14853, Ithaca, USA

    Andrew G. Clark

Authors
  1. Carlos D. Bustamante
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Corresponding author

Correspondence to Carlos D. Bustamante.

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Competing interests

Accession numbers for the SNP markers analysed in this study are dbSNP numbers ss48401226–ss48429818 and ss48429821–ss48431291, submitted under the handle APPLERA_GI. Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

A spreadsheet file containing the Mann-Whitney and Z-test results for all Panther classification of molecular function and biological process. (XLS 139 kb)

Supplementary Data 2

An text file with one line per gene giving the cell entries in the McDonald-Kreitman tables, estimated selection intensities and confidence intervals, and well as posterior P-values. (TXT 1086 kb)

Supplementary Methods

A detailed description of how the Single Nucleotide Polymorphisms we analyze in this paper were discovered and validated. Also includes details on Bioinformatic controls and quality checks. (DOC 131 kb)

Supplementary Data 1

Provides a detailed mathematical description of the statistical method we employ in this paper as well as details of coalescent simulations used to gauge robustness to demographic misspecification. (PDF 798 kb)

Supplementary Figure 1

Relationship between scaled McDonald–Kreitman cell entries and posterior mean of the selection coefficient γ for all genes in the INS data set. (PDF 1614 kb)

Supplementary Figure 2

Scatterplot of log-odds posterior of negative selection . (PDF 45 kb)

Supplementary Figure Legends

Text to accompany the above Supplementary Figures. (DOC 40 kb)

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Bustamante, C., Fledel-Alon, A., Williamson, S. et al. Natural selection on protein-coding genes in the human genome. Nature 437, 1153–1157 (2005). https://doi.org/10.1038/nature04240

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