Biology subjects

Jacobs, B. M.

Publications and source records attributed to Jacobs, B. M..

3 recordsLinked to original sources

bioRxiv2020

Gene-environment interactions in Multiple Sclerosis: a UK Biobank study

ImportanceMultiple Sclerosis (MS) is a neuro-inflammatory disorder caused by a combination of environmental exposures and genetic risk factors. We sought to determine whether genetic risk modifies the effect of environmental MS risk factors. MethodsPeople with MS were identified within UK Biobank using ICD10-coded MS or self-report. Associations between environmental risk factors and MS risk were quantified with a case-control design using multivariable logistic regression. Polygenic risk scores (PRS) were derived using the clumping-and-thresholding approach with external weights from the largest genome-wide association study of MS. Separate scores were created including (PRSMHC) and excluding (PRSNon-MHC) the MHC locus. The best performing PRS were identified in 30% of the cohort and validated in the remaining 70%. Interaction between environmental and genetic risk factors was quantified using the Attributable Proportion due to interaction (AP) and multiplicative interaction. ResultsData were available for 2250 people with MS and 486,000 controls. Childhood obesity, earlier age at menarche, and smoking were associated with MS. The optimal PRS were strongly associated with MS in the validation cohort (PRSMHC: Nagelkerkes Pseudo-R2 0.033, p=3.92x10-111; PRSNon-MHC: Nagelkerkes Pseudo-R2 0.013, p=3.73x10-43). There was strong evidence of interaction between polygenic risk for MS and childhood obesity (PRSMHC: AP=0.17, 95% CI 0.06 - 0.25, p=0.004; PRSNon-MHC: AP=0.17, 95% CI 0.06 - 0.27, p=0.006). Conclusions and RelevanceThis study provides novel evidence for an interaction between childhood obesity and a high burden of autosomal genetic risk. These findings may have significant implications for our understanding of MS biology and inform targeted prevention strategies.

genetics↗

bioRxiv2020

Parkinsons disease determinants, prediction and gene-environment interactions in the UK Biobank

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinsons disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate existing risk prediction algorithms and the impact of including addition genetic risk on the performance of prediction. MethodsWe identified individuals with incident PD diagnoses (n=1276) and unmatched controls (n=500,406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. A polygenic risk score (PRS) was constructed and used to examine gene-environment interactions. The PRS was also incorporated into a previously-developed prediction algorithm for finding incident cases. ResultsStrong evidence of association (Pcorr<0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, and daytime somnolence, and novel associations with epilepsy and earlier menarche. Individuals with the highest 10% of PRS scores had increased risk of PD (OR=3.30, 95% CI 2.57-4.24) compared to the lowest risk decile. Higher PRS scores were associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm improved model performance (Nagelkerke pseudo-R2 0.0053, p=6.87x10-14). We found evidence of interaction between the PRS and diabetes. InterpretationHere we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity and predictive power of a polygenic risk score, and to demonstrate a novel gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on prior genetic risk for PD.

genetics↗

bioRxiv2020

Summary-data-based mendelian randomisation reveals druggable targets for multiple sclerosis

BackgroundMultiple Sclerosis (MS) is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study (GWAS) findings in MS into therapeutics and effective preventive strategies has been limited to date. MethodsWe used Summary Data-Based Mendelian Randomisation (SMR) to synthesise findings from public expression quantitative trait locus (eQTL; eQTLgen and CAGE), methylation quantitative trait locus (mQTL; Lothian Birth Cohort and Brisbane Systems Genetics Study), and MS GWAS datasets (International Multiple Sclerosis Genetics Consortium). By correlating the effects of methylation on MS (M-2-MS), methylation on expression (M-2-E), and expression on MS susceptibility (E-2-MS), we prioritise genetic loci with strong evidence of causally influencing MS susceptibility. We overlay these findings onto a list of druggable genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and STRING to identify protein-protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr Virus-infected B cells, Lymphoblastoid Cell Lines (LCLs). We conducted a systematic review of prioritised genes using the Open Targets platform to identify completed and planned trials targeted prioritised genes in MS and related disease areas. ResultsExpression of 45 genes in peripheral was strongly associated with MS susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leukocyte signalling. We refined this prioritised gene list by restricting to loci where CpG site methylation was associated with MS susceptibility (M-2-MS), with gene expression (M-2-E), and where expression was associated with MS susceptibility (E-2-MS). This approach yielded a list of 15 prioritised druggable target genes for which there was evidence of a causal pathway linking methylation, expression, and MS. Five of these 15 genes are targeted by existing drugs (CD40, ERBB2, VEGFB, MERTK, and PARP1), and three were replicated in a smaller eQTL dataset (CD40, MERTK, and PARP1). In LCLs, SMR prioritised 7 druggable gene targets, of which only one was priortised by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritised genes in disorders related to MS. ConclusionsWe use public datasets and SMR to identify a list of prioritised druggable genetic targets in Multiple Sclerosis. We hope our findings could be translated into effective repurposing of existing drugs to provide novel therapies for MS and, potentially, provide a platform for developing preventive therapies.

genetics↗