Abstracts (first author)


Equilibrium and non-equilibrium demographic history and the distribution of FST: deviations from the island model can strongly affect the conclusions of QST and FST outlier tests

Author(s): Whitlock M, Lotterhos K


Local adaptation predicts that selected alleles and traits will differ in frequencies among populations, as each population adapts to its own optimum. Recently, our field has made increasing use of several methods designed to look for loci or traits that have greater divergence among populations than expected by genetic drift alone. Statistical conclusions from QST approaches (in the case of traits) or FST genome scans (for selected loci) depend on the demographic properties assumed by their null models. Typically an island model or a Dirichlet distribution is assumed. We explored through simulation a number of demographic models that differ increasingly from the island model, including several realistic scenarios out of equilibrium. For both QST and FST, the distribution of differentiation measures sometimes is well described by the island model, but often the differences are profound. We show that the differences can cause a great excess of false positives in QST or FST outlier approaches, and we make several suggestions about how to identify and ameliorate these problems in real biological settings.

Abstracts (coauthor)


Next-generation technology has made it possible to obtain large amounts of genomic data. But how do we find locally-adapted genes from this mountain of data? One method is to look for outliers in the distribution of FST (FST outlier tests, FDIST (Beaumont and Nichols 1996) and BAYESCAN (Foll and Gaggiotti 2008)). Another method is to look at the correlation between allele frequencies and the putatively selective environment (environment-allele associations or EAAs, such as SAM (Joost et al. 2007) and BAYENV (Coop et al. 2010)). Using a large-scale landscape genetics simulator, we compared these two approaches for four common demographic histories with the same mean FST: island model, isolation-by-distance, expansion from one refuge, and expansion from two refugia with secondary contact. The latter two demographies were non-equilibrium scenarios. We found a widely used EAA called SAM doesn’t control for population structure and has an unacceptable rate of false positives. For the refugia scenarios, we found that FST outlier tests had more false positives, but BAYENV had few false positives. In contrast, for isolation-by-distance, we found that FST outlier tests resulted in few false positives, but BAYENV had many false positives. We explored the range of conditions over which FST Outlier Tests and EAAs could detect loci under selection, and we show that each method has different power depending on demographic history. We propose a decision process that can used to identify demographic histories that are likely to cause high error rates. We also suggest two approaches that can be used to more accurately account for demographic history in genome scans and EAAs.


Chairman: Octávio S. Paulo
Tel: 00 351 217500614 direct
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XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group