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Title: A Bayesian chromosome painting approach to detect signals of incomplete positive selection in sequence data : applications to 1000 genomes
Author: Gamble, Christopher Thomas
ISNI:       0000 0004 5354 2612
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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Methods to detect patterns of variation associated with ongoing positive selection often focus on identifying regions of the genome with extended haplotype homozygosity - indicative of recently shared ancestry. Whilst these have been shown to be powerful they have two major challenges. First, these methods are constructed to detect variation associated with a classical selective sweep; a single haplotype background gets swept up to a higher than expected frequency given its age. Recently studies have shown that other forms of positive selection, e.g. selection on standing variation, may be more prevalent than previous thought. Under such evolution, a mutation that is already segregating in the population becomes beneficial, possibly as a result of an environmental change. The second challenge with these methods is that they base their inference on non-parametric tests of significance which can result in uncontrolled false positive rates. We tackle these problems using two approaches. First, by exploiting a widely used model in population genomics we construct a new approach to detect regions where a subset of the chromosomes are much more related than expected genome-wide. Using this metric we show that it is sensitive to both classical selective sweeps, and to soft selective sweeps, e.g. selection on standing variation. Second, building on existing methods, we construct a Bayesian test which bi-partitions chromosomes at every position based on their allelic type and tests for association between chromosomes carrying one allele and significantly reduced time to common ancestor. Using simulated data we show that this approach results in a powerful, fast, and robust approach to detect signals of positive selection in sequence data. Moreover by comparing our model to existing techniques we show that we have similar power to detect recent classical selective sweeps, and considerably greater power to detect soft selective sweeps. We apply our method, ABACUS, to three human populations using data from the 1000 Genome Project. Using existing and novel candidates of positive selection, we show that the results between ABACUS and existing methods are comparable in regions of classical selection, and are arguably superior in regions that show evidence for recent selection on standing variation.
Supervisor: Holmes, Christopher; Spencer, Christopher Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Natural Selection ; Hidden Markov Models ; Bayesian ; Sequence Analysis