title: Statistical tests of selective neutrality in the age of genomics.
abstract: Examining genomic data for traces of selection provides a powerful tool for identifying genomic regions of functional importance. Many methods for identifying such regions have focused on conserved sites. However, positive selection may also be an indication of functional importance. This article provides a brief review of some of the statistical methods used to detect selection using DNA sequence data or other molecular data. Statistical tests based on allelic distributions or levels of variability often depend on strong assumptions regarding population demographics. In contrast, tests based on comparisons of the level of variability in nonsynonymous and synonymous sites can be constructed without demographic assumptions. Such tests appear to be useful for identifying specific regions or specific sites targeted by selection.
authors.affiliation: Department of Biometrics, Cornell University, 439 Warren Hall, Ithaca, NY 14853-7801, USA. rn28@cornell.edu
authors.forename: R
authors.initials: R
authors.lastname: Nielsen
mesh_terms: D000483:Alleles; D000818:Animals; D017124:Conserved Sequence; D019143:Evolution, Molecular; D005787:Gene Frequency / Q000235:genetics; D014644:Genetic Variation / Q000235:genetics; D016678:Genome; D023281:Genomics / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D012641:Selection, Genetic; D013223:Statistics as Topic