title: Population genetic inference from genomic sequence variation.
abstract: Population genetics has evolved from a theory-driven field with little empirical data into a data-driven discipline in which genome-scale data sets test the limits of available models and computational analysis methods. In humans and a few model organisms, analyses of whole-genome sequence polymorphism data are currently under way. And in light of the falling costs of next-generation sequencing technologies, such studies will soon become common in many other organisms as well. Here, we assess the challenges to analyzing whole-genome sequence polymorphism data, and we discuss the potential of these data to yield new insights concerning population history and the genomic prevalence of natural selection.
authors.affiliation: Department of Integrative Biology, University of California, Berkeley, Berkeley, California 94720, USA.
authors.forename: John E
authors.initials: JE
authors.lastname: Pool
authors.forename: Ines
authors.initials: I
authors.lastname: Hellmann
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Jensen
authors.forename: Rasmus
authors.initials: R
authors.lastname: Nielsen
mesh_terms: D001483:Base Sequence; D002874:Chromosome Mapping; D005796:Genes; D014644:Genetic Variation; D005828:Genetics, Population; D016678:Genome / Q000235:genetics*; D006801:Humans; D011110:Polymorphism, Genetic; D044382:Population Groups; D012641:Selection, Genetic