Carina Farah Mugal

forskare vid Institutionen för ekologi och genetik, Evolutionsbiologi

018-471 6466
Evolutionsbiologiskt Centrum
Norbyvägen 18D
752 36 UPPSALA
Evolutionsbiologiskt centrum
Norbyvägen 18D
752 36 UPPSALA

Kort presentation

My main research interest lies in patterns of genome evolution, such as base composition and gene sequence evolution. In particular, I focus on the impact of meiotic recombination and natural selection on these patterns of evolution. Moreover, I investigate the applicability of phylogenetic approaches to closely related species. In order to explore these questions, I use comparative genomics, statistical data analysis and mathematical modeling, where I primarily use birds as a model organism.

Nyckelord: meiotic recombination comparative genomics theoretical population genetics stochastic modeling population genomics

Mina kurser


July 2017 – present
Research Associate
Department of Ecology and Genetics
Uppsala University, Sweden

September 2013 – June 2017
Postdoctoral Researcher
Department of Ecology and Genetics
Uppsala University, Sweden

November 2008 – June 2013
PhD studies in evolutionary biology
Department of Ecology and Genetics
Uppsala University, Sweden

July 2011 – February 2012
Research internship
Department of Computational Molecular Biology
Max Planck Institute, Berlin

March 2005 – April 2008
Undergraduate studies in theoretical chemistry
Department of Chemistry
University of Graz, Austria


Causes and consequences of variation in meiotic recombination rate across the avian genome.

In most sexually reproducing species, meiotic recombination is essential for accurate chromosome segregation during the first meiotic division. Mechanisms of meiotic recombination are therefore evolutionary conserved and the localization of recombination is tightly regulated. As a consequence of this tight regulation mechanism the frequency of recombination is highly heterogeneous across the genomes of many animals and plants and is predominantly concentrated in so-called recombination hotspots. Nevertheless, we still lack a clear picture of the underlying mechanisms for regulating the localization of recombination events in most taxa. In order to improve our understanding, we investigate the determinants of the recombination landscape across avian genomes, which show some interesting features that are in clear contrast to the much more well-studied primate genomes. Avian genomes show a large heterogeneity in chromosome size, an evolutionary stable karyotype, preferential localization of recombination hotspots close to gene promoters, together with an evolutionary stable recombination landscape. Besides the determinants of the recombination landscape, we further investigate the consequences of the avian-specific characteristics of the recombination landscape on genome evolution. Here, we particularly study the impact of GC-biased gene conversion and Hill-Robertson Interference on gene sequence evolution and inference of the strength of natural selection in birds. We primarily use comparative genomic approaches, but also imply mathematical modeling to gain some conceptual understanding.

Application of phylogenetic approaches to closely related species.

Estimates of molecular evolutionary rates, such as point mutation rate, are important measures for a wide range of evolutionary analyses. A common way to estimate such rates is based on phylogenetic approaches, which estimate sequence divergence between sequences of related species. These approaches rely on the indirect assumption that sequence divergence and species divergence are identical, an assumption, which is generally violated and reasonable only for distantly related species. The violation of this assumption leads to a time-dependence of rate estimates and biases rate estimates in particular for closely related species. Here, an analytical understanding of the time-dependence constitutes a prerequisite to avoid such biases. In order to fill this gap, we use Poisson random field models to derive analytical expressions of sequence divergence D as a function of time and sample size for a scenario that involves natural selection. This allows us not only to address the time-dependence of rate estimates, but also enables us to evaluate if the use of polymorphism data can improve the estimation.


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