THESIS
2020
xvi, 205 pages : illustrations ; 30 cm
Abstract
One of the fundamental problems in population genetics and molecular evolution is
to understand the drivers of genetic change in a population: which mutations affect
the ability of an organism to survive, reproduce, and pass its genes to the next generation,
while which mutations are mere "passengers" that do not affect this ability? This
question is of great practical importance, for example, it can aid in the identification of
mutations in viral genomes that enable viruses to evade immune response, in bacterial
genomes that enable bacteria to develop drug resistance, and in mammalian cells that
lead to cancer. In principle, the evolutionary history of a population contains information
of the effects (deleterious, beneficial or neutral) of mutations occurring in the
population...[
Read more ]
One of the fundamental problems in population genetics and molecular evolution is
to understand the drivers of genetic change in a population: which mutations affect
the ability of an organism to survive, reproduce, and pass its genes to the next generation,
while which mutations are mere "passengers" that do not affect this ability? This
question is of great practical importance, for example, it can aid in the identification of
mutations in viral genomes that enable viruses to evade immune response, in bacterial
genomes that enable bacteria to develop drug resistance, and in mammalian cells that
lead to cancer. In principle, the evolutionary history of a population contains information
of the effects (deleterious, beneficial or neutral) of mutations occurring in the
population. However, extracting this information is challenging because the evolutionary
dynamics depend not only on the cumulative effects of many mutations, but also
on the competition between many individuals in a diverse population. The inference
problem is further complicated by the confounding in
uence of drift, recombination,
and epistasis. Despite intense interest, until now it has been unclear how to systematically
disentangle the effects of individual mutations from such complex evolutionary
dynamics.
This work developed the marginal path likelihood (MPL) framework, a path integral
approach, to estimate the effects of mutations from genetic time-series data which
takes into account the complex evolutionary dynamics, thereby solving a longstanding
problem in population genetics inference. A key feature of this framework is that it
allows to derive an analytical expression for the estimate of effects of mutations. The resulting solution is computationally efficient and accurate. The work also included
a detailed performance comparison of the MPL estimate with seven state-of-the-art
methods on synthetic data using extensive simulations. The MPL estimate was then
used to analyze a half-genome length data of HIV evolution. The last part of the work
extended the MPL framework to also account for epistasis between pairs of mutations.
The performance of the resulting estimate was tested on synthetic data as well as on
HIV within-host evolution data.
Post a Comment