Topics of MSc and BSc theses
The evolutionary origin of disease-associated genetic variation
Level: BSc or MSc
Work group: Centre for Genomics, Evolution and Medicine
Supervisor: Michael Dannemann (michael.dannemann@ut.ee)
Language: English
Evolution of complex behavioural traits
Level: BSc or MSc
Work group: Centre for Genomics, Evolution and Medicine
Supervisor: Anders Eriksson (anders.eriksson@ut.ee)
Language: English
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Reconstructing ancestral migration paths
Level: BSc or MSc
Work group: Centre for Genomics, Evolution and Medicine
Supervisor: Anders Eriksson (anders.eriksson@ut.ee)
Language: English
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Dynamic of human population structure in East Europe during last two Millenia (wide)
Haplotype-based analyses of Medieval individuals/populations from Estonia (specific)
Level: BSc or MSc
Work group: Estonian Biocentre
Supervisor: Alena Kushniarevich (lkushniarevich@gmail.com)
Language: English
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Basic population genetic analyses of human ancient DNA samples with established pipelines.
Aim – to get familiar with Unix, command line, graphic visualization, and to get acquainted with some pop gen analyses used in aDNA studies. Project examples: - Kinship analysis of aDNA samples (detection of relatives with genetic methods); - Patterns of post-mortem damage in human and their detection; - mtDNA, Y chromosome determination in aDNA samples (Belarus/Estonia) and comparative analysis with other ancient and modern human samples; - EDA (exploratory data analyses) in aDNA population genetic studies (e.g. PCA, ADMIXTURE) and why they are needed.
Level: BSc
Work group: Estonian Biocentre
Supervisor: Alena Kushniarevich (lkushniarevich@gmail.com)
Language: English
Study of the genetic ancestry of ancient individuals from the Neolithic in Eurasia
The student would study the genetic ancestry of ancient individuals dated to the Neolithic time period in Eurasia and investigate possible reasons for the two clusters within the so-called European Neoltihic cluster. To investigate the differences, the student would have an insight into genome-wide analysis conducted in ancient DNA studies. The student would use cluster based (e.g. Principel component analysis) and statistical analysis (e.g. f3 and f4 statistics).
Level: BSc
Work group: Estonian Biocentre
Supervisor: Tina Saupe (main supervisor) (tina.saupe@ut.ee); Alena Kushniarevich
Language: English
Inferring and investigating regions with multiple Neandertal haplotypes
Using new WGS data from 1000 Genomes project and Neandertal genomes, find Neandertal SNPs that still persist in modern humans. Extract haplotypes based on R2, analyze and double-check results, extract regions that have multiple Neandertal haplotypes, annotate them, look into common; the reason to do it: such regions can share some features, give knowledge about special functions associated with Neandertal DNA, also there could be signs of balancing selection expected skills: Understand a bit shell terminal (on the first stages, manipulating with vcf-files in bcftools is expected), not hard (merging, subsetting, plotting) play around with tables of SNPs (R is preferable, python3 is OK). Even though all these skills (as well as the definitions of vcf-files, SNPs and haplotypes) can be learned on the fly, to be so, it would be nice to have some knowledge about programming logic (principles of syntax, variables, operations, logic operations, data types, input/output) and basics in genetics (nucleotides, gene, alleles).
Level: BSc or MSc
Work group: Centre for Genomics, Evolution and Medicine
Supervisor: Danat Yermakovich (main supervisor) (danatyermakovich@gmail.com); Michael Dannemann
Language: English
Certain features of Neandertal pleiotropy
- Investigate the pleiotropy table from Watanabe et al 2019 study. Extract Neandertal pleiotropy haplotypes, investigate them, compare them with the modern human context of pleiotropy, and annotate some of them.
- I have data of a big number of phenotypes pairs from UKBioBank, enriched for Neandertal SNPs. There are a lot of small things that can be done here, one is to re-classify traits with new categories, and try to extract haplotypes that bound many phenotypes from different categories in this dataset of only enriched pair and haplotypes.
The reason to do it: such haplotypes can play a big role in the interconnection between phenotypes, show an interesting mechanism of pleiotropy towards Neandertal DNA
Expected skills: Again, R is preferable, python3 is OK even though strange for such work. And it's good to have some experience: from the best of my knowledge, coding determines thinking, and if one struggles hardly on their level with a programming language, it's hard for them to come up with what they can do.
There should be fewer data processing work, but more table work. It looks like thinking (and implementing your ideas) about different angles of the same data.
Level: BSc
Work group: Centre for Genomics, Evolution and Medicine
Supervisor: Danat Yermakovich (main supervisor) (danatyermakovich@gmail.com); Michael Dannemann
Language: English
