PMC:3480682 / 23136-25118
Annnotations
{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/3480682","sourcedb":"PMC","sourceid":"3480682","source_url":"https://www.ncbi.nlm.nih.gov/pmc/3480682","text":"Conclusion\nIn general, the observed population structure observed by hierarchical clustering, multidimensional scaling (MDS) plots, and tree plots can be largely explained by random drift at neutral CNVs. However, as individuals from different populations often vary genetically at a few key sites in their genome, only the outlier method makes it possible to detect accelerated divergence in some CNV regions of the genome due to local selection. This local selection is detected relative to the majority of CNVs, in which among-population differences are purely a result of genetic drift.\nSuccessful outlier detection depends on reliable and obtainable estimates of FST and also on sampling variances of FST. Very large variances that are associated with single locus moment estimates of FST preclude the use of these estimates to detect selection in spite of the fact that sampling variances will decrease with the number of alleles at a locus and with the numbers of populations sampled. In this respect, the availability of locus- and population-specific Bayesian estimates of FST provides a set of tools for identifying genomic regions or populations with unusual evolutionary histories. The most important benefits of Bayesian estimates and its selection method are that the Bayesian methods allow probability statements to be made about FST and can be extended to explore the relationship with demographic or environmental covariates in the model [26]. Furthermore, likelihood-based Bayesian methods have the flexibility to accommodate missing data. However, implementations of Bayesian methods may be computationally demanding.\nSelection provides information about the adaptation to a wide range of habitats and climates [30], and thus, interpreting the story of human adaptation is an interesting research area for the studies of evolution and disease processes in the future. Thus, more studies of outlier detection need to be replicated with large population-based data.","divisions":[{"label":"Title","span":{"begin":0,"end":10}}],"tracks":[{"project":"2_test","denotations":[{"id":"23105934-16951078-44845834","span":{"begin":1456,"end":1458},"obj":"16951078"},{"id":"23105934-16494531-44845835","span":{"begin":1731,"end":1733},"obj":"16494531"}],"attributes":[{"subj":"23105934-16951078-44845834","pred":"source","obj":"2_test"},{"subj":"23105934-16494531-44845835","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#ec9993","default":true}]}]}}