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Detecting selective sweeps using hidden markov models (HMMs)
Date
2011
Abstract
A selective sweep occurs when a positive mutation spreads through a population. It causes a reduction in variation among the nucleotides in the neighbourhood of the mutation. Whether or not a selective sweep has occurred can be investigated in various ways. To detect selective sweeps, we usually use the concept of the coalescent tree. It employs a sample of individuals from a population to trace all the alleles of a gene shared by all members of the pop- ulation to a single ancestral copy, known as the most recent common ancestor (MRCA). Recent advances in theory and technology have created a background for genome-wide surveys for selective sweep events. These advances include the development of new statistical tests tailored to detect incomplete, or partial, selective sweeps associated with weaker selection, and large-scale acquisition of Deoxyribonucleic acid (DNA) sequence data which provide ample source for the detection of polymorphism patterns. In this work, we aim to detect selective sweeps by using Hidden Markov Models (HMMs).
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Description
peer-reviewed
Publisher
Citation
26th International Workshop on Statistical Modelling;
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Funding Information
Science Foundation Ireland (SFI)