Format

Send to

Choose Destination
Genetics. 2013 Mar;193(3):973-84. doi: 10.1534/genetics.112.147611. Epub 2013 Jan 10.

Estimating selection coefficients in spatially structured populations from time series data of allele frequencies.

Author information

1
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom. iain.mathieson@well.ox.ac.uk

Abstract

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.

PMID:
23307902
PMCID:
PMC3584010
DOI:
10.1534/genetics.112.147611
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center