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SUMMARY:Recent Approaches for Detecting Local Selection
DTSTART:20140402T150000
DTSTAMP:20260503T011052Z
UID:0441d76e9a02c9f07bb927ae965822331306e04b925cf52bb4e232e9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Mark Beaumont\, University of Bristol (UK)\nBIOENGINEERI
 NG SEMINARAbstract:\nIn the past 5 years there has been an increased use o
 f methods for detecting the putative effects of natural selection that lea
 d to adaptive differences between populations. At the same time there has 
 been a greatly increased appreciation of the difficulties inherent in such
  methods\, particularly those that lead to false positives. This talk will
  give a brief review of the area\, starting with Lewontin and Krakauer's o
 riginal proposals. Most methods are based on detecting outliers under a ne
 utral model of differentiation. I will describe a recent approach (Vitalis
 \, Gautier\, Dawson & Beaumont\, Genetics\, 2014) in which the parameteris
 ation is in terms of Wright's stationary distribution for alleles under se
 lection in an infinite island model. The method appears to have some advan
 tages in terms of ROC characteristics\, and reduced sensitivity to the eff
 ects of population covariance in allele frequency.Bio:\nEducation:\n- BSc 
 Zoology (Manchester)\n- PhD Genetics (Nottingham)\nHonours:\n- Wellcome Ma
 thematical Biology Fellowship (1992-1995)\n- NERC Advanced Fellowship (200
 3-2008)\nResearch Interests:\nI am a biologist by background\, and I am in
 terested in general problems of statistical inference in population geneti
 cs\, evolutionary biology\, and conservation genetics. Most of my work has
  involved Monte Carlo statistical methods. Particular areas of application
  that interest me include: detecting evidence of selection in the genome\;
  modelling demographic history of populations\; inference in structured po
 pulations\; modelling temporally sampled genetic data\; inference in agent
 -based models.
LOCATION:SV1717A http://map.epfl.ch/?room=sv1717a
STATUS:CONFIRMED
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