Jennifer Asimit

Summary

I have over 15 years of research experience in statistical genetics, focused on developing methods to advance understanding of the genetics of cardiometabolic diseases and their co-morbidities. I completed a PhD in Statistics before moving into this field, and maintain active interdisciplinary collaborations across the UK, USA and Africa in cardiometabolic genetics and statistical methodology.

I was funded by an MRC Career Development Award (grant title "Methods to improve genetic understanding of cardiometabolic traits through multiple traits and diverse population studies"), which helped me establish my research group. Previously, I held an MRC Methodology Research fellowship (now known as MRC Skills Development. Fellowship) in the Metabolic Disease group at the Wellcome Sanger institute.

Research Interests

Current methodological focuses in my research include approaches to jointly analyse multiple diseases/traits and methods for diverse genetic ancestries. An important component for my team is to promote open science and improve accessibility of our methods to maximise impact on human health in a wider spectrum of diseases. Research output includes user-friendly software, that takes the additional step of interactive data visualisation tools to help in interpretation of the results, without requiring programming experience.

GitHub: https://github.com/jennasimit

ORCID: 0000-0002-4857-2249 

X:  https://twitter.com/JennAsimit

Selected Papers

• J Soenksen, J Chen, A Varshney, S Martin, SCJ Parker, AP Morris, JL Asimit, I Barroso. (2025). Combining functional annotation and multi-trait fine-mapping methods improves fine-mapping resolution at glycaemic trait loci. Human Molecular Genetics. DOI: 10.1093/hmg/ddaf164
• WJ Astle, AS Butterworth, JL Asimit. (2025). Protocol for genetic discovery and fine-mapping of multivariate latent factors from high-dimensional traits. STAR Protocols. DOI: 10.1016/j.xpro.2025.104198
• F Zhou, WJ Astle, AS Butterworth, JL Asimit. (2025). Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits. Cell Genomics. DOI: 10.1016/j.xgen.2025.100847
• S Wang, OO Ojewunmi, A Kamiza, M Ramsay, AP Morris, T Chikowore, S Fatumo, JL Asimit. (2024). Accounting for heterogeneity due to environmental sources in meta-analysis of genome-wide association studies. Communications Biology volume 7, Article number: 1512
• F Zhou, O Soremekun, T Chikowore, S Fatumo, I Barroso, AP Morris, JL Asimit. (2023). Leveraging information between multiple population groups and traits improves fine-mapping resolution. Nature Communications 14, 7279
• AB Kamiza, SM Touré, F Zhou, O Soremekun, C Cissé, M Wélé, AM Touré, O Nashiru, M Corpas, M Nyirenda, A Crampin, J Shaffer, S Doumbia, E Zeggini, AP Morris, JL Asimit, T Chikowore, S Fatumo. (2023). Multi-trait discovery and fine-mapping of lipid loci in 125,000 individuals of African ancestry. Nature Communications 14, 5403
• F Zhou, AS Butterworth, JL Asimit. (2022). flashfm-ivis: interactive visualisation for fine-mapping of multiple quantitative traits. Bioinformatics. btac453.
• N Hernandez, J Soenksen, P Newcombe, M Sandhu, I Barroso, C Wallace, JL Asimit. (2021). The flashfm approach for fine-mapping multiple quantitative traits. Nature Communications 12, 6147.
• A Hutchinson, J Asimit, C Wallace. (2020). Fine-mapping genetic associations. Human Molecular Genetics 29(R1), R81–R88.
• JL Asimit, DB Rainbow, MD Fortune, NF Grinberg, LS Wicker, C Wallace. (2019). Stochastic search and joint fine-mapping increases accuracy and identifies previously unreported associations in immune-mediated diseases. Nature Communications 10 (1), 3216.
• Y Xue, M Mezzavilla, M Haber, S McCarthy, Y Chen, VNarasimhan, A Gilly, Q Ayub, V Colonna, L Southam, C Finan, A Massala, H Chheda, P Palta, G Ritchie, J Asimit, G Dedoussis, P Gasparini, A Palotie, S Ripatti, N Soranzo, D Toniolo, FL Wilson, R Durbin, C Tyler-Smith, E Zeggini. (2017). Enrichment of low-frequency functional variants revealed by whole-genome sequencing of multiple isolated European populations. Nature Communications, 8, 15927.
• JL Asimit, F Payne, AP Morris, HJ Cordell, I Barroso (2017). A two-stage inter-rater approach for enrichment testing of variants associated with multiple traits, European Journal of Human Genetics,25, 341–349.
• M Horikoshi, L Pasquali, S Wiltshire, JR Huyghe, A Mahajan, JL Asimit, T Ferreira, AE Locke, NR Robertson, X Wang, X Sim, H Fujita, K Hara, R Young, W Zhang, S Choi, H Chen, I Kaur, F Takeuchi, P Fontanillas, D Thuillier, L Yengo, JE Below, CHT Tam, Y Wu, T2D-GENES Consortium, Goncalo Abecasis, D Altshuler, GI Bell, J Blangero, NP Burtt, R Duggirala, JC Florez, CL Hanis, M Seielstad, G Atzmon, JCN Chan, RCW Ma, P Froguel, JG Wilson, D Bharadwaj, J Dupuis, JB Meigs, YS Cho, T Park, JS Kooner, JC Chambers, D Saleheen, T Kadowaki, ES Tai, KL Mohlke, N J Cox, J Ferrer, E Zeggini, N Kato, YY Teo, M Boehnke, MI McCarthy, and AP Morris (2016). Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms. Human Molecular Genetics.
• JL Asimit, K Hatzikotoulas, M McCarthy, AP Morris, E Zeggini (2016). Trans-ethnic study design approaches for fine-mapping, European Journal of Human Genetics. 24, 1330–1336.
• JL Asimit, K Panoutsopoulou, E Wheeler, SI Berndt, the GIANT consortium, the arcOGEN consortium, HJ Cordell, AP Morris, E Zeggini, I Barroso (2015). A Bayesian approach to the overlap analysis of epidemiologically linked traits, Genetic Epidemiology, 39, 624-634.
• D Gurdasani, T Carstensen, F Tekola-Ayele, L Pagani, I Tachmazidou, K Hatzikotoulas, S Karthikeyan, L Iles, A Choudhury, GRS Ritchie, Y Xue, JL Asimit, RN Nsubuga, EH Young, C Pomilla, K Kivinen, K Rockett, A Kamali, AP Doumatey, G Asiki, J Seeley, F Sisay-Joof, M Jallow, S Tollman, E Mekonnen, R Ekong, T Oljira, N Bradman, K Bojang, M Ramsay, A Adeyemo, E Bekele, A Motala, S Norri, F Pirie, P Kaleebu, D Kwiatkowski, C Tyler-Smith, C Rotimi, MS Sandhu, E Zeggini (2015). The African Genome Variation Project shapes medical genetics in Africa, Nature, 517, 327—332.
• R Magi, JL Asimit, AG Day-Williams, E Zeggini and AP Morris. (2012). Genome-wide association analysis of imputed rare variants: application to seven common complex diseases, Genetic Epidemiology, 36, 785—796.
• JL Asimit, AG Day-Williams, AP Morris and E Zeggini (2012). ARIEL and AMELIA: Testing for an accumulation of rare variants using next-generation sequencing data, Human Heredity, 73, 84—94.
• JL Asimit, AG Day-Williams, L Zgaga, I Rudan, V Boraska and E Zeggini (2012). An evaluation of different meta-analysis approaches in the presence of allelic heterogeneity, European Journal of Human Genetics, 20, 709—712.
• MC Lopes, C Joyce, GRS Ritchie, SL John, F Cunningham, JL Asimit, and E Zeggini (2012). A combined functional annotation score for non-synonymous variants, Human Heredity, 73, 47—51.
• JL Asimit, IL. Andrulis, and SB Bull (2011). Regression models, scan statistics and reappearance probabilities to detect regions of association between gene expression and copy number, Statistics in Medicine, 30, 1157—1178.

Refereed Book Chapter

• JL Asimit and AP Morris (2015). Collapsing approaches for the association analysis of rare variants, In Assessing rare variation in complex traits: Design and analysis of genetic studies, Springer-Verlag, New York, 135-148.

Software Development

1. flashfmZero (latent factor GWAS and FLexible And SHared information Fine-Mapping for latent factors)
2. env-MR-MEGA (environment-adjusted Meta-Regression of Multi-Ethnic Genetic Association)
3. MGflashfm (Multi-Group FLexible And SHared information Fine-Mapping)
4. flashfmZoom (a tool for joint fine-mapping and exploration of GWAS results in the UK Biobank)
5. flashfm-ivis (FLexible And SHared information Fine-Mapping - Interactive VISualisation)
6. flashfm (FLexible And SHared information Fine-Mapping)
7. MFM (Multinomial Fine-mapping)
8. COMET (Corrected Overlap and Marginal Enrichment Test)
9. BOAT (Bayesian Overlap Analysis Tool)
10. ARIEL (Accumulation of Rare variants Integrated and Extended Locus-specific test)
11. AMELIA (Allele Matching Empirical Locus-specific Integrated Association test)