Modelling Large-scale Medical Record Data
Common complex diseases such as type-2 diabetes, obesity, stroke, and cardiovascular disease are among the leading causes of mortality worldwide. Our limited understanding of how genetic variation and the environment affect health and disease makes it impossible to respond optimally, treat and ultimately prevent symptoms.
The Robinson Group develops statistical models and the computational tools required to implement these models for very large-scale human medical record data. The overall goal is to improve our understanding of how genetics and our lifestyles shape our risk of disease.
We still have very little understanding of why people develop first symptoms at different age, or why the severity of symptoms varies. The Robinson Group works to better characterize the underlying pathways and relationships among diseases. The hope is to improve our ability to predict not only an individual’s overall risk of disease, but also when people are likely to become sick and how they might respond to different treatments.
Answers to long-standing questions at the heart of understanding the changes that occur at important stages of our lives are also investigated: How does the maternal and child genome interact to shape pregnancy and early life? What constitutes a healthy pregnancy? How does our genome shape our growth? How do genetics influence our ability to lead long and healthy lives?
+43 2243 9000 2173
Jessica de Antoni
Assistant to Professors
+43 2243 9000 1725
On this site:
Statistical models for the genetic basis of common complex disease | The genetic basis of age of onset | The genetics of ageing | Maternal health | Genomic prediction for personalized health
Bernabeu E, Mccartney DL, Gadd DA, Hillary RF, Lu AT, Murphy L, Wrobel N, Campbell A, Harris SE, Liewald D, Hayward C, Sudlow C, Cox SR, Evans KL, Horvath S, Mcintosh AM, Robinson MR, Vallejos CA, Marioni RE. 2023. Refining epigenetic prediction of chronological and biological age. Genome Medicine. 15, 12. View
Ojavee SE, Kutalik Z, Robinson MR. 2022. Liability-scale heritability estimation for biobank studies of low-prevalence disease. The American Journal of Human Genetics. 109(11), 2009–2017. View
Patxot M, Stojanov M, Ojavee SE, Gobert RP, Kutalik Z, Gavillet M, Baud D, Robinson MR. 2022. Haematological changes from conception to childbirth: An indicator of major pregnancy complications. European Journal of Haematology. 109(5), 566–575. View
Orliac E, Trejo Banos D, Ojavee S, Läll K, Mägi R, Visscher P, Robinson MR. 2022. Improving genome-wide association discovery and genomic prediction accuracy in biobank data, Dryad, 10.5061/DRYAD.GTHT76HMZ. View
Orliac EJ, Trejo Banos D, Ojavee SE, Läll K, Mägi R, Visscher PM, Robinson MR. 2022. Improving GWAS discovery and genomic prediction accuracy in biobank data. Proceedings of the National Academy of Sciences of the United States of America. 119(31), e2121279119. View
ReX-Link: Matthew Robinson
since 2020 Assistant Professor, Institute of Science and Technology Austria (ISTA)
2017 – 2020 Assistant Professor, University of Lausanne, Switzerland
2013 – 2017 Postdoc, University of Queensland, Australia
2009 – 2013 NERC Junior Research Fellow, University of Sheffield, UK
2008 PhD, University of Edinburgh, UK
2019 SNSF Eccellenza Grant awardee
2010 NERC Research Fellowship