In the end of 2021, project Saving lives received two grants: 4.9 million kronor from Forte (Swedish Research Council for Health, Working Life and Welfare) and 2.4 million kronor from The Swedish Research Council. The research program involves several members from the research group, and the principal investigator of the project is Christian Rück.
Saving lives: Constructing a nationwide cohort with multimodal data to improve precision in prediction and prevention of suicide
Suicide is a major public health issue, causing severe impact on individuals and families, as well as relevant societal costs. In Sweden alone, ~70,000 years of potential life are lost each year due to suicide. Despite dedicated research efforts and prevention strategies, suicide-related outcomes are still difficult to predict and prevent. The goal of this research program is to utilize unique resources in Sweden to improve prediction of suicide by integrating environmental factors captured by national registers and genetic information using multi-modal modelling.
In aim 1, we will create the world’s largest suicide biobank by collecting neonatal blood spots stored at the Swedish PKU biobank from 4,000 individuals that died by suicide, i.e. total coverage of all suicides in Sweden from 1975 and onwards. DNA will be extracted from each of these blood spots. Data from the national registers will be added, covering major risk factors across the lifetime (socioeconomic, demographic and medical). Further, blood spots and register data from 8000 matched controls with no suicidal outcomes will be available through collaboration.
In aim 2, we will identify register-based risk factors and genetic variants associated with suicide by genotyping all DNA samples from cases and matched controls and performing a case-control genome-wide association study meta-analysis.
In aim 3, we will combine hundreds of candidate suicide predictors from the national registers (covering demographics, socioeconomic status, In aim 3, we will combine hundreds of candidate suicide predictors from the national registers (covering demographics, socioeconomic status, electronic medical records, criminality) with genetic predictors (polygenic risk scores for suicide, depression, impulsivity, and substance misuse) using both established quantitative modelling and newer machine learning approaches.