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Bayesian inference of population structure and history from genotype data

Reference	BBS/E/C/00004940
Principal Investigator / Supervisor	Professor Christopher Rawlings
Co-Investigators / Co-Supervisors	Dr Kevin Dawson
Institution	Rothamsted Research
Department	Rothamsted Research Department
Funding type	Research
Value (£)	212,114
Status	Completed
Type	Institute Project
Start date	01/04/2008
End date	31/03/2010
Duration	24 months

Abstract

The aim of this project is to develop new state-of-the-art statistical methods for making inferences about population structure from genotype data. Population structure includes features such as barriers to gene flow, and corridors of elevated gene flow. In species where reproduction can occur by self-fertilisation, apomixes or vegetative propagation, as well as by outcrossing, the age and abundance of individual selfing lines, or clonal lineages, is an important aspect of population structure. One of the most important and challenging aspects of population structure is the pattern of adaptive genetic variation, across the genome (which loci contrite to local adaptation), and across habitats, geographical regions or populations (which alleles, contributing to phenotypic differences, are present in which populations). Examples of adaptive divergence include host plant specificity in phytophagous insects (such as aphids, phylloxera, whitefly and scale insects), host specificity of parasitoid wasps to their insect hosts, and resistance and tolerance to pathogens in plant populations. The discovery of evolutionary units (such as subspecies or host races) within species is important for population management, whether the goal is conservation or containment. Information about adaptive genetic divergence between populations can help us to ensure that appropriate source populations are used for reintroduction programmes. Information about population structure and adaptive divergence can help us to match populations of candidate biocontrol agents in a more targeted way to specific pest populations (or populations of other invasive organisms). Inferences about population structure can provide information which is valuable for planning germplasm collections, selective breeding programs and gene mapping projects.Bayesian methods enable us to quantify our uncertainty about the parameters of population genetic models, where the number of parameters may be very large.

Summary

unavailable

Committee	Closed Committee - Genes & Developmental Biology (GDB)
Research Topics	Crop Science, Plant Science, Technology and Methods Development
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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