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?
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
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
McCartney DL, Hillary RF, Conole ELS, Banos DT, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Maniega SM, Valdés-Hernández MDC, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2022. Blood-based epigenome-wide analyses of cognitive abilities. Genome Biology. 23(1), 26. View
Patxot M, Trejo Banos D, Kousathanas A, Orliac EJ, Ojavee SE, Moser G, Sidorenko J, Kutalik Z, Magi R, Visscher PM, Ronnegard L, Robinson MR. 2021. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. Nature Communications. 12(1), 6972. View
Robinson MR, Patxot M, Stojanov M, Blum S, Baud D. 2021. Postpartum hemorrhage risk is driven by changes in blood composition through pregnancy. Scientific Reports. 11, 19238. View
Ojavee SE, Kousathanas A, Trejo Banos D, Orliac EJ, Patxot M, Lall K, Magi R, Fischer K, Kutalik Z, Robinson MR. 2021. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature Communications. 12(1), 2337. 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