Implementation of novel high-dimensional multi-omic analytical approaches to dissect relationships between complex traits and diseases
Creators: name: Pupko, Igors
Publisher: name: University of Surrey
Year: 2024
Subjects: MPGWAS
GWAS
Omics Data
EWAS
Metabolomics
Cancer Genetics
Diabetes
Genomics
Machine Learning
Obesity

description: Obesity is common, complex and serious health problem worldwide. It relates to many pathologies and plays a crucial role in development of specific cancers: colorectal, prostate, postmenopausal breast and pancreatic. &lt;br&gt;Based on emerging evidence, I propose a link between obesity and cancer due to shared genetic factors. I conducted a detailed analysis of genetic influences on metabolic traits and common cancers using large-scale GWAS. Further relationship was examined between metabolic traits and two cancers within the EPIC cohort. Additionally, in the NFBC1966 cohort, I explored the association between epigenetic variability and metabolite levels.&lt;br&gt;This doctoral thesis consists of three studies, where each focussed on the approach described above.&lt;br&gt;Study 1 elucidated genetic risk loci between T2D, BMI and four cancers through multi-phenotype GWAS(MPGWAS). I analysed 5M SNPs from 36,173 individuals from EPIC cohort. I performed MPGWAS for 5 diseases(T2D-four cancers) as well as BMI and four cancers using SCOPA software. We compared the discovery power between MPGWAS and single-phenotype(SP) GWAS from the same dataset. MPGWAS identified 193 association signals for T2D-cancers or BMI-cancers models. Improved power of MPGWAS identified novel associations and dissected complex relationships between phenotypes at already established DNA loci.&lt;br&gt;Study 2 investigated colorectal(CrC) and breast cancer(BrC), that are two of the most common malignancies in the world. Since metabolite levels change in cancer, I performed MPGWAS by combining epidemiological, metabolomic and genetic data for 3,877 individuals from the EPIC. Using SCOPA multi-phenotype Model-1 that was tested consisted of six metabolic measures and CrC status. Model-2 consisted of 15 moderately correlated metabolic measures and BrC status. MPGWAS of Model-1 revealed two loci and Model-2 six loci, that have been described before for association with level of blood metabolites. &lt;br&gt;Study 3, Longitudinal EWAS, uncovered 43 epigenome-wide significant metabolite change – DNAm associations. The signals from the cross-sectional EWAS didn’t overlap with those from the longitudinal EWAS. We defined four separate DNAm loci clusters by their effects on metabolite level change. This epigenetic regulatory relationship could have implications for prevention and management of cardiometabolic conditions. Overall, study highlights the importance of longitudinal studies along with cross-sectional studies. &lt;br&gt;Methodologies used to produce these results in this thesis mark crucial step in understanding the intricate interplay among diseases. Functional experiments can enhance these findings, providing mechanistic understanding that could advance medicine and the novel therapeutic strategies. This research has capacity to substantially enhance public health outcomes and alleviate the burden of the studied diseases on a global scale.&lt;br&gt;