The main research interest of the Marigorta lab revolves around the genetic basis of disease in humans. We identify emerging questions about complex disease etiology, and tackle them using an integrative approach at the interface of statistical, quantitative and evolutionary genomics. Through collaboration with doctors working in the clinic, we analyze data from –omics profiling of patient cohorts to illuminate our understanding of disease pathogenesis and, eventually, gear this knowledge towards translational advances. The long-term goal of this research lies in achieving precision medicine: being able to use individual profiles to personalize health treatment, ensuring that the right medications are used for the right patients, and tailoring medical care to patient needs.
At present, our work focuses on combining statistical genomics and medical transcriptomics (gene expression) to improve our ability to estimate risk of disease and predict phenotypes. These are the three main lines of research in the lab:
1. Statistical genetics for understanding susceptibility to disease: Whereas the last years in human genetics have been mostly about discovering loci associated with disease, the field is now shifting towards interrogation of cells and tissues from patients to characterize the molecular underpinnings of disease. As a biomarker that integrates genetic and environmental risk, we analyze transcriptomic data to uncover genetic regulatory programs that modulate risk of disease. We pay special attention to discovering genetic variants that are activated to engage in protective responses that decrease risk, as they can be the key to understand why most individuals do not develop disease. Although our work is currently focused on autoimmune disease, especially inflammatory bowel disease, we are happy to collaborate with researchers working on other disease classes.
2. Individual prediction of disease risk: A central goal of personalized medicine is to deliver numerical scores that can assess risk of disease and predict response to drug therapy for each individual. In the last few years a flurry of papers has demonstrated that polygenic risk scores can be used for stratification, pinpointing at subgroups of individuals with significantly enhanced risk of disease. Yet, estimating disease risk is only the first step in the quest for incorporating genomic information into standard clinical practice. We are currently addressing two of the main incoming challenges in this area, namely devising new methods for adapting genetic risk estimates according to information about environmental and lifestyle factors, and transforming them into tools that can be routinely used as biomarkers by doctors. In particular, a central focus is adapting the transcriptional risk score methodology to disease classes other than autoimmune disease and to tissues with easier access such as peripheral blood.
3. Heterogeneity and interactions in disease: We are interested in understanding the role that our current western lifestyle, and particularly its imbalance with human evolutionary history, plays in the heritability and genetic susceptibility of modern diseases such as type 2 diabetes and other anthropometric traits such as body mass index. Taking profit of the availability of large datasets such as the UK Biobank, we are currently working on new statistical methods to detect non-additive effects, such as epistatic and gene-by-environment interactions, that shape genetic susceptibility to disease.
In summary, our research strengthens the genomics and bioinformatics research portfolio at the CIC bioGUNE and adds an integrative –omics perspective aimed at helping towards successful implementation of precision medicine based solutions in the Basque Country. If you are interested in our work, or are considering joining the lab (at any level), please contact us at email@example.com
Life & Medical Sciences
- Biosciences & Health
How to arrive
- Statistical genetics for understanding susceptibility to disease
- Individual prediction of disease risk
- Heterogeneity and interactions in disease