RT Journal Article
SR Electronic
T1 Estimating Selection Coefficients in Spatially Structured Populations from Time Series Data of Allele Frequencies
JF Genetics
JO Genetics
FD Genetics Society of America
SP 973
OP 984
DO 10.1534/genetics.112.147611
VO 193
IS 3
A1 Mathieson, Iain
A1 McVean, Gil
YR 2013
UL http://www.genetics.org/content/193/3/973.abstract
AB Inferring the nature and magnitude of selection is an important problem in many biological contexts. Typically when estimating a selection coefficient for an allele, it is assumed that samples are drawn from a panmictic population and that selection acts uniformly across the population. However, these assumptions are rarely satisfied. Natural populations are almost always structured, and selective pressures are likely to act differentially. Inference about selection ought therefore to take account of structure. We do this by considering evolution in a simple lattice model of spatial population structure. We develop a hidden Markov model based maximum-likelihood approach for estimating the selection coefficient in a single population from time series data of allele frequencies. We then develop an approximate extension of this to the structured case to provide a joint estimate of migration rate and spatially varying selection coefficients. We illustrate our method using classical data sets of moth pigmentation morph frequencies, but it has wide applications in settings ranging from ecology to human evolution.