How can genetics help us understand self-harm?

By Kai Lim

The arrival of the genomics revolution is changing many aspects of the world, including the study of psychiatry and mental health [1]. As the cost of genotyping declines over time, more people can be recruited in genomics studies, and we will be able to understand more about the genetic risks for different mental health conditions with the increased statistical power. Nonetheless, before we delve further into this topic, I want to note that having high genetic risks does not necessarily mean one will experience a mental health episode. Environmental factors definitely play a role. Genetic influence for complex conditions is probabilistic, not deterministic [1].

Chicken or egg: What is causing self-harm?

Self-harm can be defined as “any act of self-injury or self-poisoning carried out by an individual, regardless of intention or motivation” [2]. Many risk factors for self-harm have been identified in observational studies, such as having a psychiatric diagnosis [3], having certain personality traits [4], alcohol or drug abuse [5,6], and so on. We may tend to assume that these risk factors are causing or paving the path towards self-harm. However, these risk factors were often identified in the form of observed associations in classical epidemiological studies, and associations do not necessarily mean causation. For example, the association between alcohol abuse and self-harm may be due to a third factor, such as family conflicts, which lead a person into both alcohol abuse and self-harm [7, 8]. The web of causality behind self-harm may be more complex than we thought.

Genetic studies can help us understand the causal relationships behind the aetiology of self-harm. For example, if the genetic propensity for alcohol dependence is also linked with self-harm, we can infer that the genetic predisposition for alcohol dependence increases the risk for alcohol dependence, and subsequently increases the risk for self-harm. In this way, a plausible causal relationship can be inferred.

Polygenic score: A genetic proxy for risk factors of self-harm

Polygenic scores, or sometimes also known as polygenic risk scores, can be used to index the genetic predisposition for these risk factors. Here is a video [9] that explains more about polygenic (risk) score:

Although polygenic score is not ready to be used in clinical settings, it can be utilised as a research tool. In our recently published paper [10], we used polygenic scores as genetic proxies for 24 risk factors of self-harm. Using the DNA information from the UK Biobank participants, we investigated whether genetic predispositions for different risk factors are associated with increased risk of self-harm. These risk factors come from different domains, such as mental health vulnerabilities (e.g. schizophrenia), cognitive traits (e.g. education attainment), personality traits (e.g. neuroticism), substance use (e.g. alcohol dependence), and physical traits (e.g., BMI). Out of these 24 risk factors, we found that polygenic scores for the following are significantly associated with self-harm:

  • Major depressive disorder
  • Schizophrenia
  • Attention deficit/hyperactivity disorder (ADHD)
  • Bipolar disorder
  • Alcohol dependence disorder
  • Lifetime cannabis use

From the list above, it seems like those from the mental health domain are more likely to be causal risk factors for self-harm. However, there are several limitations of using polygenic scores in causal inference [see 10]. A more stringent method, known as Mendelian Randomisation (MR) was used to further investigate the causal relationships.

Mendelian Randomisation – a “randomised controlled trial” conducted by our genes

What is MR? Let’s look at alcohol dependence again as an example. There are genetic variants found to be increasing the risk of alcohol dependence [11]. In MR, we can investigate if having these genetic variants can lead to a higher risk of self-harm. MR and polygenic scoring do share some similarities, but MR is a more stringent method in causal inference. One of the benefits of using MR is that it mimics the randomised controlled trial (RCT), which is the gold standard for studying causal relationships [12]. To elaborate, inheriting a genetic variant for a higher or lower risk of alcohol dependence is like being randomly assigned into the “alcohol dependence” group or the control group since birth (it is important to note again, though, that having high genetic risk doesn’t necessarily mean one will definitely develop alcohol dependence, but only increases one’s risk of developing it). In real life, assigning someone to consume alcohol excessively would be unethical. MR is a natural experiment that allows us to study the causal relationship in a way that mimics an RCT.

Here is an animation [13] that further explains what MR is:

In our MR analyses, we found that the effects of genetic variants for schizophrenia, major depressive disorder and ADHD are also significantly associated with self-harm, suggesting that they are the most plausible causal risk factors for self-harm. Again, this highlights the roles of mental health problems in increasing the risks for self-harm.

What can we learn from this study?

Out of many risk factors, those from the mental health vulnerabilities domain seem to be the most plausible causal risk factors for self-harm, rather than those from other domains such as personality traits or education attainment. Schizophrenia appears to be the most plausible one, followed by ADHD and major depressive disorder. This suggests that targeting symptoms related to these mental health problems is important in intervening self-harm. Drugs used in targeting psychiatric symptoms may also be repurposed to help individuals who self-harm.

It is important to emphasise here that MR relies on certain assumptions, which are further discussed in our paper [10]. Hence, rather than seeing MR as a magic pill in answering the chicken or egg question, we should see MR as another study design to triangulate the evidence for a putative causal relationship. Hence, having converging evidence from both MR studies and also classical epidemiological studies can boost our confidence in understanding the origins of self-harm. In the future, with more powerful genetic studies available, we may be able to identify more plausible causal risk factors for self-harm.



1. Plomin, R. (2018). Blueprint: How DNA makes us who we are. Penguin Books Ltd.

2. Hawton, K., Harriss, L., Hall, S., Simkin, S., Bale, E., & Bond, A. (2003). Deliberate self-harm in Oxford, 1990-2000: A time of change in patient characteristics. Psychological Medicine, 33(6), 987–995.

3. Vaughn, M. G., Salas-Wright, C. P., DeLisi, M., & Larson, M. (2015). Deliberate self-harm and the nexus of violence, victimization, and mental health problems in the United States. Psychiatry Research, 225(3), 588–595.

4. Brezo, J., Paris, J., & Turecki, G. (2006). Personality traits as correlates of suicidal ideation, suicide attempts, and suicide completions: A systematic review. Acta Psychiatrica Scandinavica, 113(3), 180–206.

5. Darvishi, N., Farhadi, M., Haghtalab, T., & Poorolajal, J. (2015). Alcohol-related risk of suicidal ideation, suicide attempt, and completed suicide: A meta-analysis. PloS One, 10(5), e0126870.

6. Borges, G., Bagge, C. L., & Orozco, R. (2016). A literature review and meta-analyses of cannabis use and suicidality. Journal of Affective Disorders, 195, 63–74.

7. Grossman, D. C., Milligan, B. C., & Deyo, R. A. (1991). Risk factors for suicide attempts among Navajo adolescents. American Journal of Public Health, 81(7), 870–874.

8. Joiner, T. E. (2010). Myths about suicide. Harvard University Press.

9. Illumina. (2019, May 8). What are polygenic risk scores? [Video]. Youtube.

10. Lim, K. X., Rijsdijk, F., Hagenaars, S. P., Socrates, A., Choi, S. W., Coleman, J. R. I., Glanville, K. P., Lewis, C. M., & Pingault, J.-B. (2020). Studying individual risk factors for self-harm in the UK Biobank: A polygenic scoring and Mendelian randomisation study. PLOS Medicine, 17(6), e1003137.

11. Walters, R. K., Polimanti, R., Johnson, E. C., McClintick, J. N., Adams, M. J., Adkins, A. E., Aliev, F., Bacanu, S.-A., Batzler, A., Bertelsen, S., Biernacka, J. M., Bigdeli, T. B., Chen, L.-S., Clarke, T.-K., Chou, Y.-L., Degenhardt, F., Docherty, A. R., Edwards, A. C., Fontanillas, P., … Agrawal, A. (2018). Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nature Neuroscience, 21(12), 1656–1669.

12. Davies, N. M., Holmes, M. V., & Davey Smith, G. (2018). Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ, k601.

13. TARG Bristol. (2017, August 22). A two minute primer on mendelian randomisation [Video]. Youtube.

*Featuring Photo by National Cancer Institute on Unsplash.

Kai Lim (@kxlim) is a PhD student based at the Social, Genetic and Developmental Psychiatry Centre, King’s College London (


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.