A5054050132- Bioinformatics and Genomic Networks
- Genetic Associations and Epidemiology
- Genetic Mapping and Diversity in Plants and Animals
- Race, Genetics, and Society
- Peroxisome Proliferator-Activated Receptors
- Biomedical Text Mining and Ontologies
- Genetic and phenotypic traits in livestock
- Microbial Metabolic Engineering and Bioproduction
- Advanced Causal Inference Techniques
- Genomics and Phylogenetic Studies
University of Cambridge
2024
British Heart Foundation
2024
McGill University
2019-2023
McGill University and Génome Québec Innovation Centre
2019
Human populations feature both discrete and continuous patterns of variation. Current analysis approaches struggle to jointly identify these because modelling assumptions, mathematical constraints, or numerical challenges. Here we apply uniform manifold approximation projection (UMAP), a non-linear dimension reduction tool, three well-studied genotype datasets discover overlooked subpopulations within the American Hispanic population, fine-scale relationships between geography, genotypes,...
Biobanks now contain genetic data from millions of individuals. Dimension-ality reduction, visualization and stratification are standard when exploring at these scales; while efficient tractable methods exist for the first two, remains challenging because uncertainty about sources population structure. In practice, is commonly performed by drawing shapes around dimensionally reduced or assuming populations have a “type” genome. We propose method stratifying with topo-logical analysis that...
Abstract Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood have been used understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. associated with variance, for 29 from the UK Biobank (N∼408,111). We discovered 176 independent vQTLs, of which 147 were not found additive...
Abstract Genome-wide association studies have identified thousands of variants associated with disease risk but the mechanism by which such contribute to remains largely unknown. Indeed, a major challenge is that do not act in isolation rather framework highly complex biological networks, as human metabolic network, can amplify or buffer effect specific alleles on susceptibility. In our previous work, we established models be leveraged simulate emerging effects genetically driven variation...