University of Bristol at ASHG 2026
Oct 20–24, 2026 Montréal, Canada ~7.5k attendees Website
University of Bristol brings 9 presentations from 6 research groups to ASHG 2026 in Montréal. Its program centres on population genetics, computational analysis and therapeutic research, with reproductive and prenatal genetics also represented. MRC Integrative Epidemiology Unit examines gut microbiome causality and pregnancy safety of antibody treatments, Diverse Epigenetic Epidemiology Partnership (DEEP) compares methylation across ancestries, and DMER Group studies gene and protein regulation.
| Organization | ASHG 2026 Attendance |
|---|---|
University of Bristol Bristol, UK | 4 PhD Students · 2 PIs · 1 Postdoc · 1 Faculty |
MRC Integrative Epidemiology Unitbristol.ac.uk/integrative-epidemiology Analyzes GWAS, DNA methylation, metabolomic, proteomic and electronic health-record data with Mendelian randomization and machine learning. Studies causal drivers of chronic disease and pregnancy complications.
| Poster Wed Oct 21 2:30 pm Assessing the causal role of the gut microbiome in human health and disease: a systematic review and application of Mendelian randomization in cancer aetiology Mendelian randomizationMicrobiomeCancerGenetic epidemiology Poster Wed Oct 21 2:30 pm ★ Reviewers’ Choice Evaluating the consequences of childhood adiposity on the human plasma proteome at four timepoints across the lifecourseGenetic epidemiologyMendelian randomizationObesityProteomics Poster Fri Oct 23 2:30 pm Investigating the safety profile of monoclonal antibody therapeutics during pregnancy: a drug target Mendelian randomization study dissecting maternal and fetal genetic effects Mendelian randomizationTeratogensPharmacologic therapyAutoimmune disorder |
Diverse Epigenetic Epidemiology Partnership (DEEP)deep-epigenetics.org Generates and analyzes Illumina methylation and genetic array data. Studies disease mechanisms and risk prediction across ancestries, focusing on child and cardiovascular health.
| Poster Fri Oct 23 2:30 pm Understanding disease discordance across ancestries through federated analysis of DNA methylation variation in the Diverse Epigenetic Epidemiology Partnership (DEEP) consortium EpigeneticsGenetic epidemiologyEpigenome-wide association studiesMethylation |
DMER Groupbiocompute.org.uk Integrates biomedical and epidemiological data with EpiGraphDB, OpenGWAS and BERT. Uses Mendelian randomization, knowledge graphs and EHR data for disease mechanisms and drug-target prioritization.
| Poster Fri Oct 23 2:30 pm Evaluating discordance between expression quantitative trait loci and protein quantitative trait loci using enrichment analysis Expression quantitative trait lociQuantitative traitEpidemiologyGenetic epidemiology |
Immunopsychiatry: Inflammation and Psychiatry Research Groupimmunopsychiatry.com/team Studies inflammation in psychiatric disorders using population cohorts, genetic analyses, proteomics and early-phase clinical trials. Targets causal mechanisms and treatments for depression and schizophrenia.
| Poster Thu Oct 22 4:15 pm Relationship between IL6 activity, inflammatory markers and psychiatric conditions: findings from UK Biobank cohort and genetic analyses DepressionGenome-wide association studyImmune systemProteomics |
Lifecourse GWAS Consortiumlifecourse-gwas.github.io Analyzes genome-wide association results from 50 cohort studies and biobanks, stratified by age, sex and ancestry. Builds polygenic risk scores and Mendelian-randomisation instruments across age.
| Poster Fri Oct 23 2:30 pm Genetic effects on complex traits exhibit distinct patterning across the lifecourse Complex traitsGenetic epidemiologyGenome-wide association studyMethodology |
Skin Genetics Consortiumskingeneticsconsortium.org Analyzes population-scale genomic data from biobanks and electronic health records to study skin diseases and traits. Shares results through the Skin Knowledge Portal. | Poster Wed Oct 21 2:30 pm ★ Reviewers’ Choice Utilizing the Genotype-Phenotype Map (GPMap) to uncover shared skin biology and prioritize drug targetsComplex diseasesComputational toolsGenetic epidemiologyGenome-wide association study |
| 1 more presenter — research group not yet identified | |
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