PhD defense: Line Skotte

Statistical approaches accommodating uncertainty in modern genomic data

Supervisor
Anders Albrechtsen, Associate Professor, Bioinformatics Center, Department of Biology

Co-supervisor
Rasmus Nielsen, Professor, Center for Theoretical Evolutionary Genomics, Integrative Biology, University of California, Berkeley

Exam Committee
Eleftheria Zeggini, Professor, Department of Human Genetics, Wellcome Trust Sanger Institute
Mikkel H Schierup, Professor, Bioinformatics Research Center, Aarhus University
Hans Siegismund, Associate Professor, Bioinformatics Center, Department of Biology, University of Copenhagen

Abstract
This thesis presents new statistical methods for inference in genetic epidemiology and population genetics. Genomic technologies evolves rapidly and their area of application have broadened in the recent years, however uncertainties pervade every level of modern genome-wide data. The aim of this thesis is to provide statistical methods that appropriately address the resulting challenges and offer solutions to current problems in genetics.The first two papers in the thesis describe a new method for association mapping and a new method for estimating individual admixture proportions, both accommodate the uncertainty of genotypes from next-generation sequencing data, the third paper describes a new method for association mapping that accommodate the unknown ancestry of chromosomal segments in admixed individuals and finally the fourth paper contributes a new method for inferring imbalanced allelic transcription from RNA sequencing experiments, that takes into account the inherent over-dispersion.