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Signals of natural selection in the HapMap project data The International HapMap Consortium

Signals of natural selection in the HapMap project data The International HapMap Consortium. Gil McVean Department of Statistics, Oxford University. The International HapMap Project. To facilitate the design and analysis of association studies

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Signals of natural selection in the HapMap project data The International HapMap Consortium

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  1. Signals of natural selection in the HapMap project dataThe International HapMap Consortium Gil McVean Department of Statistics, Oxford University

  2. The International HapMap Project • To facilitate the design and analysis of association studies • A genome-wide map of genetic variation across 270 individuals from four populations • CEPH families from Utah • Yoruba from Nigeria • Han Chinese from Beijing • Japanese from the Tokyo region • Phase I collected data on approximately 1.2 million SNPs • Phase II increases SNP density to more than one per kb • All data publicly available at www.hapmap.org

  3. Looking for selection • A genome-wide map of variation can also be used to hunt for regions of the genome where natural selection has acted • Selective sweeps • Balancing selection • Local adaptation • Why? • Interest • Functional polymorphism • The signal of selection we observe tells us about the genetic architecture of traits

  4. Methods for mapping selection • Model-based • Compare genetic variation to ‘neutral’ model • Purely empirical • Consider the ‘most extreme’ genomic regions • ‘Calibrated’ • Compare to examples of (very few) proven selective importance

  5. In what way are selected regions unusual? (in the HapMap data)

  6. HLA17q21 inversionLactaseDuffy

  7. HLA and resistance to infectious disease HLA The HLA region shows extremely high levels of polymorphism

  8. 17q21 inversion and reproductive success The inversion has multiple (66) SNPs in perfect association (r2 = 1)

  9. LCT and lactase persistence The LCT gene shows an extended haplotype structure in European populations

  10. The Duffy locus and resistance to Plasmodium vivax The FY gene shows extreme population differentiation

  11. Different selective histories leave different footprints in genetic variation

  12. How much of the genome looks as ‘unusual’ as these selected loci?

  13. Heterozygosity as extreme as HLA HLA

  14. Sets of perfect proxies as extreme as the 17q21 inversion Inversion

  15. EHH as extreme as LCT Lactase

  16. Differentiation as extreme as the Duffy locus (NB not FY*O) Duffy

  17. For ¾ cases, the selected locus is at the very extreme of the genome-wide distribution

  18. What can we learn from the unusual, but less extreme cases?

  19. Heterozygosity across the genome Top 1% Top 5% Top 10% Bottom 10% Bottom 5% Bottom 1%

  20. Elevated heterozygosity on 8p Chromosome 6 MHC Chromosome 8 8p23 inversion

  21. Distribution of long runs of perfect proxies ≥ 50 SNPs 20 – 50 SNPs 10-20 SNPs 17q21 Inversion

  22. An inversion on the X chromosome?

  23. Distribution of EHH Top 0.1% Top 1% Top 10%

  24. A selective sweep on chromosome 5?

  25. Distribution of differentiation Top 0.1% Top 1% Top 10%

  26. SLC24A5 Lamason et al (Science 2005)

  27. Unusual regions of the genome suggest interesting biology BUT The hypothesis of historical selection is fundamentally untestable

  28. What hypothesis can we test? Signals of selection should tend to occur near regions of known functional importance i.e. genes

  29. Are genes over-represented in regions of high heterozygosity?

  30. Are genes over-represented in regions of high proxy number?

  31. Are genes over-represented in regions of high EHH?

  32. Are genes over-represented in regions of high differentiation?

  33. Only differentiation shows a tendency for an increased density of ‘selection’ near genes

  34. The wild speculation

  35. Selection on standing variation • Why should we see an excess of one type of signal of adaptive evolution near genes, but not another? • Perhaps the signals are sensitive to assumptions about selection occurs? • EHH methods will be most powerful for identifying selection on a single, novel mutation • Differentiation will pick cases where an already polymorphic mutation, present on multiple haplotype backgrounds, becomes favoured in one geographic region • Perhaps most selection has been on standing variation?

  36. Acknowledgements • The International HapMap Consortium • Oxford Statistics • Peter Donnelly, Simon Myers, Chris Spencer, Raphaelle Chaix • Funding agencies • NIH, TSC, The Wellcome Trust, BBSRC, the Fyssen Foundation

  37. Distribution of Fay and Wu’s H statistic Bottom 0.1% Bottom 1% Bottom 10%

  38. Distribution of Tajima D statistic Top 1% Top 5% Top 10% Bottom 10% Bottom 5% Bottom 1%

  39. Fay and Wu H (negative) Tajima D (negative)

  40. Numbers of SNPs

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