Effect of Sample Size on Fine Mapping Expression Quantitative Trait Loci in Lymphoblastoid Cell Lines
Name
Anette Maria Kuklane
Abstract
In the human genome, coding regions called genes can be found. The sequence of genetic variants in the genes encode for proteins that are synthesised during gene expression. In turn, these proteins determine the phenotype of the organism. Genetic variants that affect gene expression are found in regions called expression quantitative trait loci and have a significant effect on the manifestation of traits, including diseases and disorders. Due to the correlation between the genetic variants associated with such diseases and disorders, identifying these variants and understanding the genetic mechanisms they affect remains a challenge. Using fine mapping, it is possible to systematically assess the probability of a genetic variant to be causal. This is done by finding sets of credible variants containing at least one causal variant according to the signal of the trait. Currently, however, the effect of sample size on the accuracy of fine mapping in finding causal variants has not been researched. The aim of this bachelor’s thesis was to investigate the extent to which the use of a larger sample size increases the statistical power to find causal variants in lymphoblastoid cell lines. It was determined that larger sample sizes increase the resolution of fine mapping as both the statistical significance of finding causal variants and the statistical power to identify the number of credible sets containing causal variant increase. Specifically, increasing sample size led to the detection of more fine mapped credible sets, and these credible sets often contained a smaller set of putatively causal genetic variants.
Graduation Thesis language
Estonian
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Kaur Alasoo
Defence year
2021