Analysing The Risk Of Suicide By Assessing Genetics And Clinical Factors

Suicide is a leading cause of death and disability worldwide and cause significant emotional distress for families and society. To reduce suicidal behaviours effectively, it is crucial to identify high-risk individuals so that they can be provided with support and preventative measures in a timely way.

Mental illnesses, social isolation, and traumatic events increase the risk of suicide. Additionally, there is a strong genetic component to suicidal behaviours, with genetic predisposition explaining 43% of the risk for suicide attempts or completions and 36% of the risk for suicidal thoughts. However, there is limited knowledge as to how these factors can be used to identify individuals who are at high-risk, especially among diverse populations.

The project

Dr Brenda Cabrera-Mendoza plans to better understand the complex interplay between genetics and clinical risk factors, and how these contribute towards suicidal behaviours.

The aim is to improve the prediction accuracy of individual suicidal risk across different demographics, therefore enhancing screening programmes to identify at-risk individuals.

The process

Dr Cabrera-Mendoza and her team will analyse large scale genetic and clinical datasets (Including genetic markers, electronic health records and mental health questionnaires) to identify key determinants of suicidal behaviour. These datasets will be of diverse people from different backgrounds, including participants from European, African, Asian and Hispanic descent.

This information will then be used to train different machine-learning algorithms to find the combination of factors that better predict each suicidal behaviour.

Finally, Dr Cabrera-Mendoza and her team will build an open source web-application with their data and findings, allowing other researchers to further validate and improve their algorithm.

The potential

If we can improve the identification of individuals who are at high-risk of suicide, we can help them to access support and interventions before they reach crisis point. Ultimately saving thousands of lives.

Additionally, the web-app will help researchers to better understand the complex interplay between genes and other clinical factors meaning we can speed up and improve future research.