Eleftheria Zeggini

Prof. Dr.

Eleftheria Zeggini

Associated Investigator

Prof. Dr.

Eleftheria Zeggini

Professor of Translational Genomics, Institute of Translational Genomics, Computational Health Center

Helmholtz Zentrum München

Research-Website itg.office@helmholtz-munich.de

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Research Background

The research of Eleftheria Zeggini focuses on translational genomics to elucidate the genetic basis of medically important human traits. Her work develops and applies statistical and computational methods to analyze large-scale genomic and molecular datasets, integrating genetic variation with functional genomics and epidemiological information. By characterizing the genetic architecture of common complex diseases and linking DNA variants to downstream biological mechanisms, she aims to bridge the gap between genetic discovery and clinical application. Through robust data integration strategies, this research advances precision medicine approaches for disease prognosis, management, precision prevention and therapeutic development.

Research fields

C1 Computational Biology
C3 Therapeutics

My research aims to translate insights from genomics into mechanisms of disease development and progression, shortening the path to translation and empowering precision medicine and prevention.

Prof. Dr. Eleftheria Zeggini

Publications

Translational genomics of osteoarthritis in 1,962,069 individuals.

Hatzikotoulas, K.; Southam, L.; Stefansdottir, L.; Boer, CG.; McDonald, ML.; Pett, JP.; Park, YC.; Tuerlings, M.; Mulders, R.; Barysenka, A.; Arruda, AL.; Tragante, V.; Rocco, A.; Bittner, N.; Chen, S.; Horn, S.; Srinivasasainagendra, V.; To, K.; Katsoula, G.; Kreitmaier, P.; Tenghe, AMM.; Gilly, A.; Arbeeva, L.; Chen, LG.; de Pins, AM.; Dochtermann, D.; Henkel, C.; Höijer, J.; Ito, S.; Lind, PA.; Lukusa-Sawalena, B.; Minn, AKK.; Mola-Caminal, M.; Narita, A.; Nguyen, C.; Reimann, E.; Silberstein, MD.; Skogholt, AH.; Tiwari, HK.; Yau, MS.; Yue, M.; Zhao, W.; Zhou, JJ.; Alexiadis, G.; Banasik, K.; Brunak, S.; Campbell, A.; Cheung, JTS.; Dowsett, J.; Faquih, T.; Faul, JD.; Fei, L.; Fenstad, AM.; Funayama, T.; Gabrielsen, ME.; Gocho, C.; Gromov, K.; Hansen, T.; Hudjashov, G.; Ingvarsson, T.; Johnson, JS.; Jonsson, H.; Kakehi, S.; Karjalainen, J.; Kasbohm, E.; Lemmelä, S.; Lin, K.; Liu, X.; Loef, M.; Mangino, M.; McCartney, D.; Millwood, IY.; Richman, J.; Roberts, MB.; Ryan, KA.; Samartzis, D.; Shivakumar, M.; Skou, ST.; Sugimoto, S.; Suzuki, K.; Takuwa, H.; Teder-Laving, M.; Thomas, L.; Tomizuka, K.; Turman, C.; Weiss, S.; Wu, TT.; Zengini, E.; Zhang, Y.; , .; , .; , .; , .; , .; , .; , .; , .; , .; Ferreira, MAR.; Babis, G.; Baras, A.; Barker, T.; Carey, DJ.; Cheah, KSE.; Chen, Z.; Cheung, JP.; Daly, M.; de Mutsert, R.; Eaton, CB.; Erikstrup, C.; Furnes, ON.; Golightly, YM.; Gudbjartsson, DF.; Hailer, NP.; Hayward, C.; Hochberg, MC.; Homuth, G.; Huckins, LM.; Hveem, K.; Ikegawa, S.; Ishijima, M.; Isomura, M.; Jones, M.; Kang, JH.; Kardia, SLR.; Kloppenburg, M.; Kraft, P.; Kumahashi, N.; Kuwata, S.; Lee, MTM.; Lee, PH.; Lerner, R.; Li, L.; Lietman, SA.; Lotta, L.; Lupton, MK.; Mägi, R.; Martin, NG.; McAlindon, TE.; Medland, SE.; Michaëlsson, K.; Mitchell, BD.; Mook-Kanamori, DO.; Morris, AP.; Nabika, T.; Nagami, F.; Nelson, AE.; Ostrowski, SR.; Palotie, A.; Pedersen, OB.; Rosendaal, FR.; Sakurai-Yageta, M.; Schmidt, CO.; Sham, PC.; Singh, JA.; Smelser, DT.; Smith, JA.; Song, YQ.; Sørensen, E.; Tamiya, G.; Tamura, Y.; Terao, C.; Thorleifsson, G.; Troelsen, A.; Tsezou, A.; Uchio, Y.; Uitterlinden, AG.; Ullum, H.; Valdes, AM.; van Heel, DA.; Walters, RG.; Weir, DR.; Wilkinson, JM.; Winsvold, BS.; Yamamoto, M.; Zwart, JA.; Stefansson, K.; Meulenbelt, I.; Teichmann, SA.; van Meurs, JBJ.; Styrkarsdottir, U.; Zeggini, E.

Nature. · 2025

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Osteoarthritis is the third most rapidly growing health condition associated with disability, after dementia and diabetes. By 2050, the total number of patients with osteoarthritis is estimated to reach 1 billion worldwide. As no disease-modifying treatments exist for osteoarthritis, a better understanding of disease aetiopathology is urgently needed. Here we perform a genome-wide association study meta-analyses across up to 489,975 cases and 1,472,094 controls, establishing 962 independent associations, 513 of which have not been previously reported. Using single-cell multiomics data, we identify signal enrichment in embryonic skeletal development pathways. We integrate orthogonal lines of evidence, including transcriptome, proteome and epigenome profiles of primary joint tissues, and implicate 700 effector genes. Within these, we find rare coding-variant burden associations with effect sizes that are consistently higher than common frequency variant associations. We highlight eight biological processes in which we find convergent involvement of multiple effector genes, including the circadian clock, glial-cell-related processes and pathways with an established role in osteoarthritis (TGFβ, FGF, WNT, BMP and retinoic acid signalling, and extracellular matrix organization). We find that 10% of the effector genes express a protein that is the target of approved drugs, offering repurposing opportunities, which can accelerate translation.

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Suzuki, K.; Hatzikotoulas, K.; Southam, L.; Taylor, HJ.; Yin, X.; Lorenz, KM.; Mandla, R.; Huerta-Chagoya, A.; Melloni, GEM.; Kanoni, S.; Rayner, NW.; Bocher, O.; Arruda, AL.; Sonehara, K.; Namba, S.; Lee, SSK.; Preuss, MH.; Petty, LE.; Schroeder, P.; Vanderwerff, B.; Kals, M.; Bragg, F.; Lin, K.; Guo, X.; Zhang, W.; Yao, J.; Kim, YJ.; Graff, M.; Takeuchi, F.; Nano, J.; Lamri, A.; Nakatochi, M.; Moon, S.; Scott, RA.; Cook, JP.; Lee, JJ.; Pan, I.; Taliun, D.; Parra, EJ.; Chai, JF.; Bielak, LF.; Tabara, Y.; Hai, Y.; Thorleifsson, G.; Grarup, N.; Sofer, T.; Wuttke, M.; Sarnowski, C.; Gieger, C.; Nousome, D.; Trompet, S.; Kwak, SH.; Long, J.; Sun, M.; Tong, L.; Chen, WM.; Nongmaithem, SS.; Noordam, R.; Lim, VJY.; Tam, CHT.; Joo, YY.; Chen, CH.; Raffield, LM.; Prins, BP.; Nicolas, A.; Yanek, LR.; Chen, G.; Brody, JA.; Kabagambe, E.; An, P.; Xiang, AH.; Choi, HS.; Cade, BE.; Tan, J.; Broadaway, KA.; Williamson, A.; Kamali, Z.; Cui, J.; Thangam, M.; Adair, LS.; Adeyemo, A.; Aguilar-Salinas, CA.; Ahluwalia, TS.; Anand, SS.; Bertoni, A.; Bork-Jensen, J.; Brandslund, I.; Buchanan, TA.; Burant, CF.; Butterworth, AS.; Canouil, M.; Chan, JCN.; Chang, LC.; Chee, ML.; Chen, J.; Chen, SH.; Chen, YT.; Chen, Z.; Chuang, LM.; Cushman, M.; Danesh, J.; Das, SK.; de Silva, HJ.; Dedoussis, G.; Dimitrov, L.; Doumatey, AP.; Du, S.; Duan, Q.; Eckardt, KU.; Emery, LS.; Evans, DS.; Evans, MK.; Fischer, K.; Floyd, JS.; Ford, I.; Franco, OH.; Frayling, TM.; Freedman, BI.; Genter, P.; Gerstein, HC.; Giedraitis, V.; González-Villalpando, C.; González-Villalpando, ME.; Gordon-Larsen, P.; Gross, M.; Guare, LA.; Hackinger, S.; Hakaste, L.; Han, S.; Hattersley, AT.; Herder, C.; Horikoshi, M.; Howard, AG.; Hsueh, W.; Huang, M.; Huang, W.; Hung, YJ.; Hwang, MY.; Hwu, CM.; Ichihara, S.; Ikram, MA.; Ingelsson, M.; Islam, MT.; Isono, M.; Jang, HM.; Jasmine, F.; Jiang, G.; Jonas, JB.; Jørgensen, T.; Kamanu, FK.; Kandeel, FR.; Kasturiratne, A.; Katsuya, T.; Kaur, V.; Kawaguchi, T.; Keaton, JM.; Kho, AN.; Khor, CC.; Kibriya, MG.; Kim, DH.; Kronenberg, F.; Kuusisto, J.; Läll, K.; Lange, LA.; Lee, KM.; Lee, MS.; Lee, NR.; Leong, A.; Li, L.; Li, Y.; Li-Gao, R.; Ligthart, S.; Lindgren, CM.; Linneberg, A.; Liu, CT.; Liu, J.; Locke, AE.; Louie, T.; Luan, J.; Luk, AO.; Luo, X.; Lv, J.; Lynch, JA.; Lyssenko, V.; Maeda, S.; Mamakou, V.; Mansuri, SR.; Matsuda, K.; Meitinger, T.; Melander, O.; Metspalu, A.; Mo, H.; Morris, AD.; Moura, FA.; Nadler, JL.; Nalls, MA.; Nayak, U.; Ntalla, I.; Okada, Y.; Orozco, L.; Patel, SR.; Patil, S.; Pei, P.; Pereira, MA.; Peters, A.; Pirie, FJ.; Polikowsky, HG.; Porneala, B.; Prasad, G.; Rasmussen-Torvik, LJ.; Reiner, AP.; Roden, M.; Rohde, R.; Roll, K.; Sabanayagam, C.; Sandow, K.; Sankareswaran, A.; Sattar, N.; Schönherr, S.; Shahriar, M.; Shen, B.; Shi, J.; Shin, DM.; Shojima, N.; Smith, JA.; So, WY.; Stančáková, A.; Steinthorsdottir, V.; Stilp, AM.; Strauch, K.; Taylor, KD.; Thorand, B.; Thorsteinsdottir, U.; Tomlinson, B.; Tran, TC.; Tsai, FJ.; Tuomilehto, J.; Tusie-Luna, T.; Udler, MS.; Valladares-Salgado, A.; van Dam, RM.; van Klinken, JB.; Varma, R.; Wacher-Rodarte, N.; Wheeler, E.; Wickremasinghe, AR.; van Dijk, KW.; Witte, DR.; Yajnik, CS.; Yamamoto, K.; Yamamoto, K.; Yoon, K.; Yu, C.; Yuan, JM.; Yusuf, S.; Zawistowski, M.; Zhang, L.; Zheng, W.; , .; Raffel, LJ.; Igase, M.; Ipp, E.; Redline, S.; Cho, YS.; Lind, L.; Province, MA.; Fornage, M.; Hanis, CL.; Ingelsson, E.; Zonderman, AB.; Psaty, BM.; Wang, YX.; Rotimi, CN.; Becker, DM.; Matsuda, F.; Liu, Y.; Yokota, M.; Kardia, SLR.; Peyser, PA.; Pankow, JS.; Engert, JC.; Bonnefond, A.; Froguel, P.; Wilson, JG.; Sheu, WHH.; Wu, JY.; Hayes, MG.; Ma, RCW.; Wong, TY.; Mook-Kanamori, DO.; Tuomi, T.; Chandak, GR.; Collins, FS.; Bharadwaj, D.; Paré, G.; Sale, MM.; Ahsan, H.; Motala, AA.; Shu, XO.; Park, KS.; Jukema, JW.; Cruz, M.; Chen, YI.; Rich, SS.; McKean-Cowdin, R.; Grallert, H.; Cheng, CY.; Ghanbari, M.; Tai, ES.; Dupuis, J.; Kato, N.; Laakso, M.; Köttgen, A.; Koh, WP.; Bowden, DW.; Palmer, CNA.; Kooner, JS.; Kooperberg, C.; Liu, S.; North, KE.; Saleheen, D.; Hansen, T.; Pedersen, O.; Wareham, NJ.; Lee, J.; Kim, BJ.; Millwood, IY.; Walters, RG.; Stefansson, K.; Ahlqvist, E.; Goodarzi, MO.; Mohlke, KL.; Langenberg, C.; Haiman, CA.; Loos, RJF.; Florez, JC.; Rader, DJ.; Ritchie, MD.; Zöllner, S.; Mägi, R.; Marston, NA.; Ruff, CT.; van Heel, DA.; Finer, S.; Denny, JC.; Yamauchi, T.; Kadowaki, T.; Chambers, JC.; Ng, MCY.; Sim, X.; Below, JE.; Tsao, PS.; Chang, KM.; McCarthy, MI.; Meigs, JB.; Mahajan, A.; Spracklen, CN.; Mercader, JM.; Boehnke, M.; Rotter, JI.; Vujkovic, M.; Voight, BF.; Morris, AP.; Zeggini, E.

Nature. · 2024

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Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.

Boer, CG.; Hatzikotoulas, K.; Southam, L.; Stefánsdóttir, L.; Zhang, Y.; Coutinho de Almeida, R.; Wu, TT.; Zheng, J.; Hartley, A.; Teder-Laving, M.; Skogholt, AH.; Terao, C.; Zengini, E.; Alexiadis, G.; Barysenka, A.; Bjornsdottir, G.; Gabrielsen, ME.; Gilly, A.; Ingvarsson, T.; Johnsen, MB.; Jonsson, H.; Kloppenburg, M.; Luetge, A.; Lund, SH.; Mägi, R.; Mangino, M.; Nelissen, RRGHH.; Shivakumar, M.; Steinberg, J.; Takuwa, H.; Thomas, LF.; Tuerlings, M.; , .; , .; , .; , .; Babis, GC.; Cheung, JPY.; Kang, JH.; Kraft, P.; Lietman, SA.; Samartzis, D.; Slagboom, PE.; Stefansson, K.; Thorsteinsdottir, U.; Tobias, JH.; Uitterlinden, AG.; Winsvold, B.; Zwart, JA.; Davey Smith, G.; Sham, PC.; Thorleifsson, G.; Gaunt, TR.; Morris, AP.; Valdes, AM.; Tsezou, A.; Cheah, KSE.; Ikegawa, S.; Hveem, K.; Esko, T.; Wilkinson, JM.; Meulenbelt, I.; Lee, MTM.; van Meurs, JBJ.; Styrkársdóttir, U.; Zeggini, E.

Cell. · 2021

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Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

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Translational genomics and precision medicine: Moving from the lab to the clinic.

Zeggini, E.; Gloyn, AL.; Barton, AC.; Wain, LV.

Science. · 2019

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Translational genomics aims to improve human health by building on discoveries made through genetics research and applying them in the clinical setting. This progress has been made possible by technological advances in genomics and analytics and by the digital revolution. Such advances should enable the development of prognostic markers, tailored interventions, and the design of prophylactic preventive approaches. We are at the cusp of predicting disease risk for some disorders by means of polygenic risk scores integrated with classical epidemiological risk factors. This should lead to better risk stratification and clinical decision-making. A deeper understanding of the link between genome-wide sequence and association with well-characterized phenotypes will empower the development of biomarkers to aid diagnosis, inform disease progression trajectories, and allow better targeting of treatments to those patients most likely to respond.

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Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.

Zeggini, E.; Weedon, MN.; Lindgren, CM.; Frayling, TM.; Elliott, KS.; Lango, H.; Timpson, NJ.; Perry, JR.; Rayner, NW.; Freathy, RM.; Barrett, JC.; Shields, B.; Morris, AP.; Ellard, S.; Groves, CJ.; Harries, LW.; Marchini, JL.; Owen, KR.; Knight, B.; Cardon, LR.; Walker, M.; Hitman, GA.; Morris, AD.; Doney, AS.; , .; McCarthy, MI.; Hattersley, AT.

Science. · 2007

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The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.

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