Eleftheria Zeggini - Selected Publications#

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1. Suzuki K*, Hatzikotoulas K*, Southam L*, …, Voight BF*, Morris AP*, Zeggini E*. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications. Nature (2024), doi: 2023.03.31.23287839. (IF 2022: 64.8) This publication presents the largest genome-wide association study for type 2 diabetes to date, allowing us to identify novel genetic risk loci for the disease. We have constructed genetic risk scores that are associated with harmful complications using cutting-edge computational approaches to integrate data across multiple -omics modalities.

2. Bocher O, Willer CJ, Zeggini E. Unravelling the genetic architecture of human complex traits through whole genome sequencing. Nat Commun (2023), 14:3520. PMID: 37316478. (IF 2022: 16.6) Here, we highlight the importance of whole genome sequencing (WGS) for large-scale genetics studies. We argue for enhanced inclusion of diverse populations, the development of novel statistical models, and the integration of functional information at multiple levels to ensure robust and equitable WGS-informed clinical decisions and interventions in the future.

3. Arruda AL, Hartley A, Katsoula G, Smith GD, Morris AP, Zeggini E. Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. Am J Hum Genet (2023), 110:1304-1318. PMID: 37433298. (IF 2022: 9.8) In this paper, we have investigated the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis by integrating genotype data, omics and functional information from disease-relevant tissues. This novel approach is applicable to any combination of comorbid diseases and can help improve our understanding of the co-occurrence of chronic conditions, especially in light of the upward trajectory of the world population's life expectancy.

4. Kreitmaier P, …, Zeggini E. An epigenome-wide view of osteoarthritis in primary tissues. Am J Hum Genet (2022), 109: 1255-1271. PMID: 35679866. (IF 2022: 9.8) Here, we present an epigenome-wide association study of knee cartilage degeneration and derive methylation models of cartilage degeneration using machine learning approaches, for the first time. We report disease-grade-specific genome-wide methylation quantitative trait loci in osteoarthritis cartilage, thereby providing insights into epigenetic mechanisms underlying osteoarthritis in primary tissues.

5. Png G, …, Zeggini E. Mapping the serum proteome to neurological diseases using whole genome sequencing. Nat Commun (2021), 12:7042. PMID: 34857772. (IF 2021: 17.7) In this paper, we have utilised whole genome sequencing data to perform a protein quantitative trait locus analysis of neurologically relevant proteins. We have detected several independently-associated variants – many of which were not previously described – thereby highlighting the importance of utilising serum protein levels as intermediate traits for biomarker discovery.

6. Boer CG*, Hatzikotoulas K*, Southam L*, …, Zeggini E. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell (2021), 184:4784-4818.e17. PMID: 34450027. (IF 2021: 66.9) This publication presents the largest study of osteoarthritis genetics to date across individuals from 9 populations and identifies new genetic risk factors for the disease and high-value drug targets. We have been able to provide novel drug repositioning opportunities and describe the genetic links between osteoarthritis and its main symptom – pain – for the first time.

7. Zeggini E, Baumann M, Götz M, Herzig S, Hrabe de Angelis M, Tschöp MH. Biomedical Research Goes Viral: Dangers and Opportunities. Cell (2020), 181: 1189–1193. PMID: 32442404. (IF 2020: 41.6) Here, we argue that stakeholders in science and policy making should use the lessons learned from the SARS-CoV-2 outbreak and apply it to and not lose focus on other major diseases, such as cancer and cardiometabolic diseases.

8. Zeggini E, Gloyn AL, Barton AC, Wain LV. Translational Genomics and Precision Medicine: moving from the lab to clinic. Science (2019), 365: 1409-1413. PMID: 31604268. (IF 2019: 41.8) In this work, we provide an overview of cutting-edge approaches to translate genomics results from the lab into clinical use and highlight avenues to accelerate this pipeline.

9. Tachmazidou I*, Hatzikotoulas K*, Southam L*, …, Zeggini E. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank. Nat Genet (2019), 51: 230-236. PMID: 30664745. (IF 2019: 27.6) In this study, we conducted the largest genome-wide association study of osteoarthritis to date and were able to more than double the number of known loci influencing osteoarthritis risk. In combination with multi-omics data, we identified new targets of already approved drugs with the potential to treat osteoarthritis.

10. Zengini E, …, Zeggini E. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Genet (2018), 50: 549-558. PMID: 29559693. (IF 2018: 25.5) In this paper, we conducted the largest genome-wide association study of osteoarthritis to date using the UK Biobank resource, providing a much-needed boost in the number of robustly-replicating disease risk loci.

*contributed equally to this work