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Interpretations of Practical Population Genetics Analyses of Genome-Wide SNP Data on Human Demography

  • Ryosuke KimuraEmail author
Chapter
Part of the Replacement of Neanderthals by Modern Humans Series book series (RNMH)

Abstract

Recent advances in DNA technologies enable researchers to investigate the genetic diversity across the whole genome within a species and to gain insight into the genetic structures of populations at an unprecedented resolution. Moreover, several recently developed statistical techniques are used to infer the demographic history of populations. However, reconstruction of a complicated demographic history of a large number of interrelated populations remains difficult. In this study, I focused on (1) phylogenetic analysis, (2) principal component analysis, and (3) model-based clustering analysis with the aim of learning by computer simulations how to interpret of a large-scale genomic data set using these methods. Such empirical understandings of these practical analyses must facilitate to set an appropriate model to be tested in the estimation of demographic parameters.

Keywords

Clustering analysis Demography Genome-wide SNP data Phylogenetic analysis Principal component analysis 

Notes

Acknowledgement

This study was supported by a Grant-in-Aid for Scientific Research on Innovative Areas from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

References

  1. Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CrossRefGoogle Scholar
  2. Beaumont MA, Rannala B (2004) The Bayesian revolution in genetics. Nat Rev Genet 5:251–261CrossRefGoogle Scholar
  3. Bryant D, Moulton V (2004) Neighbor-net: an agglomerative method for the construction of phylogenetic networks. Mol Biol Evol 21:255–265CrossRefGoogle Scholar
  4. Ewing G, Hermisson J (2010) MSMS: a coalescent simulation program including recombination, demographic structure and selection at a single locus. Bioinformatics 26:2064–2065CrossRefGoogle Scholar
  5. Francois O, Currat M, Ray N, Han E, Excoffier L, Novembre J (2010) Principal component analysis under population genetic models of range expansion and admixture. Mol Biol Evol 27:1257–1268CrossRefGoogle Scholar
  6. Huson DH, Bryant D (2006) Application of phylogenetic networks in evolutionary studies. Mol Biol Evol 23:254–267CrossRefGoogle Scholar
  7. Latter BD (1972) Selection in finite populations with multiple alleles. 3. Genetic divergence with centripetal selection and mutation. Genetics 70:475–490Google Scholar
  8. McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5:e1000686CrossRefGoogle Scholar
  9. Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New YorkGoogle Scholar
  10. Novembre J, Stephens M (2008) Interpreting principal component analyses of spatial population genetic variation. Nat Genet 40:646–649CrossRefGoogle Scholar
  11. Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2:e190CrossRefGoogle Scholar
  12. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CrossRefGoogle Scholar
  13. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959Google Scholar
  14. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CrossRefGoogle Scholar
  15. Reich D, Price AL, Patterson N (2008) Principal component analysis of genetic data. Nat Genet 40:491–492CrossRefGoogle Scholar
  16. Ruiz-Linares A (1994) Analysis of classical and DNA markers for reconstructing human population history. In: Brenner S, Hanihara K (eds) The origin and past of Modern Humans as viewed from DNA. World Scientific, Singapore, pp 123–148Google Scholar
  17. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425Google Scholar
  18. Tang H, Peng J, Wang P, Risch NJ (2005) Estimation of individual admixture: analytical and study design considerations. Genet Epidemiol 28:289–301CrossRefGoogle Scholar
  19. The HUGO Pan-Asian SNP Consortium (2009) Mapping human genetic diversity in Asia. Science 326:1541–1545CrossRefGoogle Scholar

Copyright information

© Springer Japan 2014

Authors and Affiliations

  1. 1.Graduate School of MedicineUniversity of the RyukyusOkinawaJapan

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