Modern Methods for past DNA: Unlocking the full potential for ancient genomes

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Thorfinn Sand Korneliussen


University of Copenhagen


DKK 4,110,911




Semper Ardens: Accelerate


The rapid advances in technology has revolutionized not just the field of natural sciences such as population genetics, bioinformatics, evolutionary genetics, conservation studies medical genetics etc, but has cascaded and found applications within humanities such as anthropology and archeology mainly through genetic studies based on genetic sequencing data. The goal of this project is the development of a statistical framework that encapsulates the different levels of uncertainty that are associated with ancient and low-coverage sequencing data.


The industry has facilitated large scale data generation to the degree that it is not the sample collection or data generation that is the bottleneck for DNA studies, but more the lack of appropriate methods that takes into account the problems that exists inherently in the sequencing platform itself. With this project we will allow researchers to perform analyses that would otherwise not be possible, not just within ancient DNA, but also low-pass sequencing data in general.


The objective of this project is firstly to improve the so called ‘genotype likelihoods’ which is used as the unit of information for sequencing studies. We will achieve this by using supplementary information of the biological material as covariates in the statistical modelling of the uncertainty. As baseline we will use classical statistical methods such as generalized linear models and will compare with modern approaches within machine learning. Secondly we will apply these improved genotype likelihoods to various new methods such as estimation of population size changes, methods for performing selection scans and local tracts of relatedness.

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