Data Science for Population Health

This Grand Challenge Programme is led by Professor Jaakko Kaprio.

This Grand Challenge Programme studies genetic and environmental determinants of common, complex diseases and their behavioural risk factors with modern omics approaches. The main focus is on traits with a high relevance for public health, such as addictions and substance use, obesity, mental health, physical activity, sleep, and cognition.

The main aim of the Grand Challenge is to resolve causality for observed association between exposures, including genetic variation and to assess and quantify the impact of genetic variation in the Finnish population.

Primary datasets used are Finnish population-based cohorts, preferably those with follow-up of outcomes (cohorts) and repeated measures (longitudinal studies). These are richly phenotyped with detailed exposure and risk information. Examples include GeneRisk, Finnrisk, Health2000/2011 and Finnish Twin Cohorts. Secondary data sets for replication and meta-analyses are appropriate foreign studies such as HUNT, ALSPAC and many others. Whole population analyses, without genetic information, linking register-based exposures and disease outcomes provide context and generate hypotheses.

Methods used: Standard epidemiological analyses, Mendelian randomization and quasi-experimental designs (within-family analyses). Gene-environment and mediation/moderation analyses 

Data Science for Population Health research groups and projects