Medical Genomics

Medical GenomicsMedical Genomics

Medical Genomics

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Coordinator: Jorge Ferrer

Understanding how our genome functions offers unrivalled opportunities to transform medicine. By providing insights into the genetic mechanisms of disease, it enables the development of more efficient preventive or curative therapies.  

The Medical Genomics Programme acts as an innovation hub for CRG´s cross-cutting efforts to understand human disease. It fosters partnerships and creates collaborative forums within CRG and across partner institutions, including research centres, clinical scientists, and industry.                      

The programme is built around two major challenges. One is focused on dissecting human genome variation and its role in human disease. Genome sequencing is emerging as a widespread clinical instrument, and has the capacity to inform on the ~5 million DNA variants that each of us carries in our genome. For the vast majority of these variants, however, we are still unable to interpret their clinical or functional significance. The programme brings together multidisciplinary expertise to define the function and disease impact of human variants. It seeks to build risk models and discover causal variants for rare and common diseases, and thereby uncover markers and therapeutic targets for precision medicine.

Another challenge is focused on applying genomics and disease models to define causal mechanisms and new therapies. New technologies that modify the function of the genome have the potential to solve major outstanding questions in human disease. The programme leverages the power of large-scale genomic tools to uncover disease mechanisms or markers, and to create therapeutic avenues. It brings together expertise in genetic screens, single cell genomics, network biology, and deep learning, applying these to disease models and primary biosamples.

Both areas are highly overlapping, and promise to deliver breakthroughs for cancer, rare Mendelian disorders, or chronic diseases such as diabetes, Alzheimer´s or lung fibrosis.   


Currently, clinical genomics largely focuses on protein coding genes. Our group aims to understand the function of the noncoding genome in pancreatic beta cells. This knowledge is leveraged to define new genetic mechanisms for rare and common forms of diabetes. This in turn is used to define molecular subtypes of diabetes, and to develop therapies that correct disease mechanisms. 

Our group is developing statistical methods to identify genetic variants, and transcriptional changes associated to complex phenotypes (i.e. histopathological images, shift work, compound toxicity, etc.). We are also co-leading the NIH-funded ENTEx project in which multiple omics assays have been performed into multiple tissues from a few post-mortem donors. The ENTEx resource will provide a framework for personalized functional genomics.

The group manages the European Genome-phenome Archive and studies the relationship between genomes and phenomes at several levels. These include using comparative genomics to understand the genetic architecture of complex traits and diseases, and exploring the consequences of pleiotropy in human senescence patterns.

The Weghorn group is focused on the analysis of cancer in the framework of population genetics. This enables the identification of genomic regions that are under selection and, hence, important for tumorigenesis.

The group of Lars Velten uses single cell transcriptomics and single cell genetic screens to identify disease mechanisms in haematological disease. A focus of the group is on the role of leukemic stem cells in AML, a rare population of cells that fuels disease progression and relapse. The group is committed to the development of new genomic and bioinformatic technologies.

In my group, we aim to better understand the early epigenetic events that contribute to tumour formation. For this purpose, we use Non-Hodgkin lymphomas (NHLs, tumours that arise from immune cells) as a model. More specifically, we aim to generate NHL models by genome editing and, in these models as well as in healthy and pre-malignant cells obtained from human individuals we aim to characterise the epigenetic landscape using single-cell technologies.

Our group studies mechanisms of alternative pre-mRNA splicing and their alterations in disease, with a focus on cancer. We analyse changes in splicing that control cell proliferation, apoptosis or drug resistance of cancer cells. Based upon this knowledge, we design mechanism-based approaches to revert these changes and thus explore possible RNA-based therapies.

Fátima Gebauer is interested in post-transcriptional mechanisms of gene expression and their roles in cancer progression, with a current focus on melanoma. She performs large scale analysis of RNA binding proteins (RIC, iCLIP) combined with transcriptomics, translatomics and proteomics to understand translational reprogramming of cancer cells.

Our lab is exploring the use of single molecule nanopore sequencing technologies coupled with deep learning algorithms to provide novel, rapid and cost-effective methods for cancer diagnosis, prognosis and sample stratification.

Our lab is developing and using massively parallel mutagenesis and machine learning to quantify, understand and predict the effects of mutations and why their effects vary across individuals.   The aim is both to understand the fundamental sequence-to-activity relationships that underlie biology and to provide data and predictive models to guide clinical genetics and drug development.  Three areas of interest at the moment are proteins that aggregate in neurodegenerative diseases including Alzheimer’s, Parkinson’s and ALS, mutations that interact to cause cancer, and drug resistance mutations.

My group is developing a non-pathogenic bacterial chassis capable of producing and secreting engineered biomolecules (nanobodies, cytokines and others) to treat lung diseases related to inflammatory processes (i.e. cancer, fibrosis, infections etc..).  We combined synthetic biology of our bacteria (M. pneumoniae) with protein design to make more efficient, stable, and functional biomolecules.