Matthias Steinrücken
—
abgelegt unter:
FDM-Seminar
Inferring Demographic Histories using Coalescent Hidden Markov Models
| Was |
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|---|---|
| Wann |
03.05.2019 von 12:00 bis 14:00 |
| Wo | Ernst-Zermelo-Straße 1, Raum 232, 2. OG |
| Termin übernehmen |
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Inference of historical demographic events from contemporary
genomic sequence data has received a lot of attention in recent years. A
particular focus has been on the recent exponential growth of
population size in humans. This recent growth strongly impacts the
distribution of rare alleles, which are of importance when studying
disease related genetic variation. The popular method PSMC (Li and
Durbin, 2011) is used to infer population sizes from a sample of two
chromosomes. However, the small sample size severely limits the power of
this method in the recent past.
To improve
inference in the recent past, we extend the Coalescent Hidden Markov
model approach of PSMC to larger sample sizes. Since using the full
genealogical trees relating the sample at each locus is computationally
prohibitive, we introduce a flexible mathematical framework to employ
different representations of these local trees. In partciular, we
present the implementation of this framework using the height of the
local trees (TMRCA), corresponding to PSMC for sample size 2, and using
the total branch length of the local trees.
We
evaluate the different representations in simulation studies and
applications to genomic variation data from diverse human populations.
We discuss potential extension of the framework to infer divergence
times and migration rates in structured populations, and employing the
posterior distribution of the local trees to detect regions under
selection.