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Simple Statistical Tools to Detect Signals of Recent Polygenic Selection

  • Received : 2014.02.07
  • Accepted : 2014.02.24
  • Published : 2014.03.31
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Abstract

A growing body of evidence shows that most psychological traits are polygenic, that is they involve the action of many genes with small effects. However, the study of selection has disproportionately been on one or a few genes and their associated sweep signals (rapid and large changes in frequency). If our goal is to study the evolution of psychological variables, such as intelligence, we need a model that explains the evolution of phenotypes governed by many common genetic variants. This study illustrates simple statistical tools to detect signals of recent polygenic selection: a) ANOVA can be used to reveal significant deviation from random distribution of allele frequencies across racial groups. b) Principal component analysis can be used as a tool for finding a factor that represents the strength of recent selection on a phenotype and the underlying genetic variation. c) Method of correlated vectors: the correlation between genetic frequencies and the average phenotypes of different populations is computed; then, the resulting correlation coefficients are correlated with the corresponding alleles' genome-wide significance. This provides a measure of how selection acted on genes with higher signal to noise ratio. Another related test is that alleles with large frequency differences between populations should have a higher genome-wide significance value than alleles with small frequency differences. This paper fruitfully employs these tools and shows that common genetic variants exhibit subtle frequency shifts and that these shifts predict phenotypic differences across populations.

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References

  1. Jonathan, K. P., and Anna Di, R. (2010). Adaptation - not by sweeps alone. Nature Reviews Genetics 11, 665-667.
  2. Pritchard, J. K., Pickrell, J. K., and Coop, G. (2010). The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation. Current biology : CB 20, R208-215. https://doi.org/10.1016/j.cub.2009.11.055
  3. Smith, J. M., and Haigh, J. (1974). The hitch-hiking effect of a favourable gene. Genetical research 23, 23-35. https://doi.org/10.1017/S0016672300014634
  4. Jensen, A. R., and Weng, L. J. (1994). What is a good g? Intelligence 18, 231-258. https://doi.org/10.1016/0160-2896(94)90029-9
  5. Turchin, M. C., Chiang, C. W., Palmer, C. D., Sankararaman, S., Reich, D., Genetic Investigation of, A. T. C., and Hirschhorn, J. N. (2012). Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Nat Genet 44, 1015-1019. https://doi.org/10.1038/ng.2368
  6. Piffer, D. (2013). Factor Analysis of Population Allele Frequencies as a Simple, Novel Method of Detecting Signals of Recent Polygenic Selection: The Example of Educational Attainment and IQ. Mankind Quarterly 54, 168-200.
  7. Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010). Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832-838. https://doi.org/10.1038/nature09410
  8. Lettre, G., Jackson, A. U., Gieger, C., Schumacher, F. R., Berndt, S. I., Sanna, S., Eyheramendy, S., Voight, B. F., Butler, J. L., Guiducci, C., et al. (2008). Identification of ten loci associated with height highlights new biological pathways in human growth. Nat Genet 40, 584-591. https://doi.org/10.1038/ng.125
  9. Migliano, A. B., Romero, I. G., Metspalu, M., Leavesley, M., Pagani, L., Antao, T., Huang, D. W., Sherman, B. T., Siddle, K., Scholes, C., et al. (2013). Evolution of the pygmy phenotype: evidence of positive selection fro genome-wide scans in African, Asian, and Melanesian pygmies. Human biology 85, 251-284. https://doi.org/10.13110/humanbiology.85.1-3.0251
  10. Bozzola, M., Travaglino, P., Marziliano, N., Meazza, C., Pagani, S., Grasso, M., Tauber, M., Diegoli, M., Pilotto, A., Disabella, E., et al. (2009). The shortness of Pygmies is associated with severe under-expression of the growth hormone receptor. Molecular Genetics and Metabolism 98, 310-313. https://doi.org/10.1016/j.ymgme.2009.05.009
  11. Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., Westra, H. J., Shakhbazov, K., Abdellaoui, A., Agrawal, A., et al. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467-1471. https://doi.org/10.1126/science.1235488
  12. Johnson, R., Nelson, G., Troyer, J., Lautenberger, J., Kessing, B., Winkler, C., and O'Brien, S. (2010). Accounting for multiple comparisons in a genome-wide association study (GWAS). BMC Genomics 11, 724. https://doi.org/10.1186/1471-2164-11-724

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