
Chaz Hyseni
I am an evolutionary biologist and population geneticist with extensive experience in molecular techniques and statistical analyses, including next-generation sequencing and bioinformatics. I use a multi-disciplinary approach to address questions in ecology and evolution, by adapting methods from spatial and multivariate statistics, as well as machine learning. I use both empirical data and spatial eco-evolutionary modeling to understand the distribution of genetic variation in natural environments. The modeling component involves new ways to assess the role spatial heterogeneity of environments plays in generating genetic variation and biological diversity at multiple levels of organization, from alleles to communities. Timescales of evolution is a unifying theme of my research. At the historical timescale, I have examined geographic and environmental influences on changes in DNA sequence. At the contemporary timescale, I have investigated how epigenetic mechanisms–specifically, DNA methylation–facilitate rapid responses to human-mediated disturbance, both in forest and urban environments.
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Papers by Chaz Hyseni
Location: Global.
Taxa: Diploid out-crossing species.
Methods: We considered a range of scenarios that could reveal the impacts of different combinations of read length versus number of loci (arrangement of DNA sequence data), and whether or not pre-fusion populations experienced bottlenecks coinciding with their divergence (historical context of fusion). Post-fusion gene pools were sampled along 10 successive time points representing increasing lag times following merging of sister populations, and summary statistic values were recalculated at each. Results: Many summary statistics were able to detect signatures of complete merging of populations after a sampling lag time of 1.5 N e generations, but the most informative ones included two neutrality tests and four diversity metrics, with Z nS (a linkage disequilibrium-based neutrality test) being particularly powerful. Correlation was relatively low among the two neutrality tests and two of the diversity metrics. There were clear benefits of many short (200-bp × 200) loci over a handful of long (4-kb × 10) loci. Also, only the latter genetic dataset type showed impacts of bottlenecks during divergence upon the number of informative summary statistics.
Main conclusions: This work contributes to identifying cases of lineage fusion, and advances phylogeography by enabling more nuanced reconstructions of how individual species, or multiple members of an ecological community, responded to past environmental change.
livestock in Uganda. The human disease (sleeping sickness) manifests itself in two forms: acute and chronic. The
Lake Victoria basin in Uganda has the acute form and a history of tsetse re-emergence despite concerted efforts to
control tsetse. The government of Uganda has targeted the basin for tsetse eradication. To provide empirical data
for this initiative, we screened tsetse flies from the basin for genetic variation at the mitochondrial DNA cytochrome
oxidase II (mtDNA COII) gene with the goal of investigating genetic diversity and gene flow among tsetse, tsetse
demographic history; and compare these results with results from a previous study based on microsatellite loci data
in the same area.
Methods: We collected 429 Gff tsetse fly samples from 14 localities in the entire Ugandan portion of the Lake
Victoria coast, covering 40,000 km2. We performed genetic analyses on them and added data collected for 56 Gff
individuals from 4 additional sampling sites in the basin. The 529pb partial mitochondrial DNA cytochrome oxidase
II (mtDNA COII) sequences totaling 485 were analysed for genetic differentiation, structuring and demographic
history. The results were compared with findings from a previous study based on microsatellite loci data from the
basin.
Results: The differences within sampling sites explained a significant proportion of the genetic variation. We found
three very closely related mtDNA population clusters, which co-occurred in multiple sites. Although ΦST (0 – 0.592;
P < 0.05) and Bayesian analyses suggest some level of weak genetic differentiation, there is no correlation between
genetic divergence and geographic distance (r = 0.109, P = 0.185), and demographic tests provide evidence of
locality-based demographic history.
Conclusion: The mtDNA data analysed here complement inferences made in a previous study based on
microsatellite data. Given the differences in mutation rates, mtDNA afforded a look further back in time than
microsatellites and revealed that Gff populations were more connected in the past. Microsatellite data revealed
more genetic structuring than mtDNA. The differences in connectedness and structuring over time could be related
to vector control efforts. Tsetse re-emergence after control interventions may be due to re-invasions from outside
the treated areas, which emphasizes the need for an integrated area-wide tsetse eradication strategy for sustainable
removal of the tsetse and trypanosomiasis problem from this area.