Jennifer L. Asimit

Multi-ancestry fine-mapping accounting for ancestral and environmental heterogeneity improves resolution and interpretation

The paper introduces two fine-mapping methods to account for ancestral and environmental heterogeneity in GWAS, improving resolution of causal variants across diverse populations.

Combining functional annotation and multi-trait fine-mapping methods improves fine-mapping resolution at glycaemic trait loci

This paper combines multi-trait fine-mapping and functional annotation to improve the identification of causal variants at glycaemic trait loci, reducing the number of likely causal variants significantly.

Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits

The paper introduces a method to improve genetic discovery and fine-mapping by analyzing latent factors from high-dimensional traits, showing enhanced power compared to traditional approaches.

Accounting for heterogeneity due to environmental sources in meta-analysis of genome-wide association studies

This paper introduces env-MR-MEGA, a meta-regression model that adjusts for environmental and ancestral heterogeneity in GWAS meta-analysis, improving association power.

Leveraging information between multiple population groups and traits improves fine-mapping resolution

This paper introduces MGflashfm and MGfm, methods that improve fine-mapping resolution by integrating multiple traits and population groups using summary statistics.

flashfmZoom: a tool for joint fine-mapping and exploration of GWAS results in the UK Biobank

flashfmZoom is an R Shiny app that enables joint fine-mapping and exploration of GWAS results from the UK Biobank to identify shared or distinct causal variants across traits.

flashfm-ivis: interactive visualisation for fine-mapping of multiple quantitative traits

flashfm-ivis provides interactive visualizations for exploring shared and distinct causal variants across multiple quantitative traits using fine-mapping results.

Flashfm: A Flexible and Shared Information Fine-mapping Approach for Multiple Quantitative Traits

Flashfm improves fine-mapping of multiple quantitative traits by sharing information between traits, reducing potential causal variants by 20% in a Ugandan cohort.

Sharing information between related diseases using Bayesian joint fine mapping increases accuracy and identifies novel associations in six immune mediated diseases

This paper introduces a Bayesian joint fine-mapping method that improves accuracy in identifying causal variants across related immune-mediated diseases by leveraging shared genetic information.