Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease. It has been observed that the genetic architecture of SLE often overlaps with other diseases, a phenomenon known as pleiotropy. This project aims to delve deeper into this aspect of SLE and ascertain how these shared genetic components influence the onset, progression, and treatment outcomes of SLE and its comorbidities.
Our primary objective is to analyze large Genome-Wide Association Studies (GWAS) data sets of SLE to identify shared genetic variants among multiple diseases. The data will help us better understand the underlying genetic basis of SLE and its association with other health conditions.
Throughout the course of the PhD project, the student will be trained in various bioinformatics and statistical techniques to analyze and interpret GWAS data. The skills acquired would include genetic data analysis, advanced statistical modeling, data visualization, and interpretation of complex biological data.
Year 1: The focus will be on acquiring essential skills and gaining an in-depth understanding of SLE genetics, pleiotropy, and GWAS analysis.
Year 2: The student will commence with a detailed analysis of the GWAS data, identifying overlapping genetic variants between SLE and other diseases.
Year 3: The findings from the analysis will be consolidated, followed by interpretation and synthesis of results to delineate the role of shared genetic components in disease manifestation and progression.
Year 4: The final year will involve drafting the PhD thesis, potential publication of results, and exploring translational aspects of our findings to determine how these insights could potentially shape future clinical approaches to SLE and associated conditions.