Despite advances in sequencing technology and computational biology which led to identifying
underlying causes for complex traits, utilization of X chromosome data lags behind the autosomes.
This can be attributed to the inherent complexities of analyzing X chromosome data
and extra data processing steps needed before the analysis. The aim of this thesis was to develop
a computational workflow for the inclusion of X chromosome analysis and improve the
shortcomings in order to supplement the existing eQTL analysis methods. We demonstrated
that after adjustment of X chromosome dosage differences between females and males, existing
workflows can be used to uncover potential causal variants for complex traits and diseases. Using
RNA-seq data from human lymphoblastoma cell lines obtained from GEUVADIS project
we performed statistical fine mapping and colocalization analysis with external databases. Results
show significant associations of PLP2 gene with respiratory and cardiovascular functions.
