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Detalle de: Population genetic inference using a fixed number of segregating sites: a reassessment

Title Population genetic inference using a fixed number of segregating sites: a reassessment
Abstract

Summary Coalescent theory is commonly used to perform population genetic inference at the nucleotide level. Here, we examine the procedure that fixes the number of segregating sites (henceforth the FS procedure). In this approach a fixed number of segregating sites ( S ) are placed on a coalescent tree (independently of the total and internode lengths of the tree). Thus, although widely used, the FS procedure does not strictly follow the assumptions of coalescent theory and must be considered an approximation of (i) the standard procedure that uses a fixed population mutation parameter θ, and (ii) procedures that condition on the number of segregating sites. We study the differences in the false positive rate for nine statistics by comparing the FS procedure with the procedures (i) and (ii), using several evolutionary models with single-locus and multilocus data. Our results indicate that for single-locus data the FS procedure is accurate for the equilibrium neutral model, but problems arise under the alternative models studied; furthermore, for multilocus data, the FS procedure becomes inaccurate even for the standard neutral model. Therefore, we recommend a procedure that fixes the θ value (or alternatively, procedures that condition on S and take into account the uncertainty of θ) for analysing evolutionary models with multilocus data. With single-locus data, the FS procedure should not be employed for models other than the standard neutral model.

Apa Reference RAMOS-ONSINS, S. E., MOUSSET, S., MITCHELL-OLDS, T., & STEPHAN, W. (2007). Population genetic inference using a fixed number of segregating sites: a reassessment. Genetical Research, 89(4), 231–244. 10.1017/s0016672307008877
Doi 10.1017/S0016672307008877
Publication Date Aug 2007
Publication Type Article
Researcher Sebastian Ernesto Ramos-Onsins
Ontology Population genomics, Evolutionary modelling
Research Groups Statistical and population genomics
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