Etiological research aims to uncover causal effects, whilst prediction research aims to forecast an outcome with the best accuracy. Causal and prediction research usually require different methods,... Show moreEtiological research aims to uncover causal effects, whilst prediction research aims to forecast an outcome with the best accuracy. Causal and prediction research usually require different methods, and yet their findings may get conflated when reported and interpreted. The aim of the current study is to quantify the frequency of conflation between etiological and prediction research, to discuss common underlying mistakes and provide recommendations on how to avoid these. Observational cohort studies published in January 2018 in the top-ranked journals of six distinct medical fields (Cardiology, Clinical Epidemiology, Clinical Neurology, General and Internal Medicine, Nephrology and Surgery) were included for the current scoping review. Data on conflation was extracted through signaling questions. In total, 180 studies were included. Overall, 26% (n = 46) contained conflation between etiology and prediction. The frequency of conflation varied across medical field and journal impact factor. From the causal studies 22% was conflated, mainly due to the selection of covariates based on their ability to predict without taking the causal structure into account. Within prediction studies 38% was conflated, the most frequent reason was a causal interpretation of covariates included in a prediction model. Conflation of etiology and prediction is a common methodological error in observational medical research and more frequent in prediction studies. As this may lead to biased estimations and erroneous conclusions, researchers must be careful when designing, interpreting and disseminating their research to ensure this conflation is avoided. Show less
In this thesis, we aimed to better understand how genetic variation affect the processes underlying health and disease, as trait-associated genetic variants are often located in non-coding... Show moreIn this thesis, we aimed to better understand how genetic variation affect the processes underlying health and disease, as trait-associated genetic variants are often located in non-coding regions. This hampers their interpretability, and has prompted the exploration of their effects on transcriptional regulation, a process that is crucial in the development of common and complex diseases. To do this, we have used a variety of omics data in a large collection of individuals from the general population. Using these data, we have investigated the local and distal effects of genetic variants on other molecular phenotypes, such as gene expression levels and DNA methylation levels of CpG sites, and the underlying mechanisms. This has resulted in a framework enabling the exploration of causal hypotheses about transcriptional regulation using genetics as a causal anchor. The approaches used in this thesis have yielded insight into transcriptional (dys)regulation and several underlying mechanisms. This will be helpful in better understanding how transcriptional regulation contributes to complex phenotypes related to health and disease, such as common diseases. Show less
