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Genetics and Genomics of Human Population Structure

  • Sohini Ramachandran
  • Hua Tang
  • Ryan N. Gutenkunst
  • Carlos D. Bustamante
Chapter

Abstract

Recent developments in sequencing technology have created a flood of new data on human genetic variation, and this data has yielded new insights into human population structure. Here we review what both early and more recent studies have taught us about human population structure and history. Early studies showed that most human genetic variation occurs within populations rather than between them, and that genetically related populations often cluster geographically. Recent studies based on much larger data sets have recapitulated these observations, but have also demonstrated that high-density genotyping allows individuals to be reliably assigned to their population of origin. In fact, for admixed individuals, even the ancestry of particular genomic regions can often be reliably inferred. Recent studies have also offered detailed information about the composition of specific populations from around the world, revealing how history has shaped their genetic makeup. We also briefly review quantitative models of human genetic history, including the role natural selection has played in shaping human genetic variation.

Keywords

Demographic History Ancestral Population Admix Population Theor Popul Biol Allele Frequency Spectrum 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sohini Ramachandran
    • 1
  • Hua Tang
    • 2
  • Ryan N. Gutenkunst
    • 3
  • Carlos D. Bustamante
    • 4
  1. 1.Society of FellowsHarvard UniversityCambridgeUSA
  2. 2.Department of GeneticsStanford Medical SchoolStanfordUSA
  3. 3.Theoretical Biology and Biophysics, and Center for Nonlinear StudiesLos Alamos National LaboratoryLos AlamosUSA
  4. 4.Department of Biological Statistics and Computational BiologyCornell UniversityIthacaUSA

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