Genomic Data From More Than 41,000 People Shed New Light on Bipolar Disorder
• Research Highlight
In the largest genome-wide association study of bipolar disorder to date, researchers found about twice as many genetic locations associated with bipolar disorder as reported in previous studies. These and other genome-wide findings help improve our understanding of the biological origins of bipolar disorder and suggest some promising genes for further research.
The study, led by the Psychiatric Genomics Consortium bipolar disorder working group, is published in Nature Genetics . The Psychiatric Genomics Consortium is a global collaborative effort consisting of more than 800 investigators, including researchers in the National Institute of Mental Health (NIMH) Intramural Research Program and extramural scientists conducting NIMH-supported research.
Bipolar disorder is a mental illness characterized by episodes of mania and depression that can seriously impair day-to-day functioning. Affecting up to 50 million people worldwide, bipolar disorder is a major public health concern. Although evidence suggests that genes play an important role in the development of bipolar disorder, researchers still do not have a clear understanding of the disorder’s specific biological causes. Examining common genetic variations in the genomes (or complete set of DNA) of people with bipolar disorder is a way that scientists can home in on the genetic factors that are likely to play a causal role in the disorder.
For this study, the researchers analyzed genomic data from 57 groups of participants across Europe, North America, and Australia. These cohorts included individuals receiving clinical care for bipolar disorder and individuals classified as having bipolar disorder based on data from health registries, electronic health records, or repositories. The total combined sample included 41,917 individuals with bipolar disorder and 371,549 individuals without bipolar disorder.
The researchers used an approach known as a genome-wide association study (GWAS) , which allowed them to identify common genetic variations that are more likely to occur in people with bipolar disorder. Identifying these variations can provide important clues about the biological pathways and processes that are likely to be involved in the disorder.
According to the GWAS results, a total of 64 genomic locations, or risk loci , were associated with bipolar disorder even after accounting for all the variations studied across the genome. These 64 risk loci included 33 that had not been reported in previous bipolar disorder studies. Among the novel loci, the researchers found that bipolar disorder was associated with the major histocompatibility complex, which is a large group of genes involved in immune function. They also found a correlation between bipolar disorder and loci linked to other psychiatric disorders, including schizophrenia, major depression, and childhood-onset disorders such as attention-deficit/hyperactivity disorder (ADHD).
The study findings also revealed genome-wide genetic overlaps, or correlations, between bipolar disorder and certain traits. For example, the results showed a genetic correlation between bipolar disorder and both alcohol use and smoking, as well as genetic correlations with some aspects of sleep (daytime sleepiness, insomnia, and sleep duration).
The researchers also compared genetic overlap between the two types of bipolar disorder: bipolar I disorder (which includes manic episodes and, typically, depressive episodes) and bipolar II disorder (which includes depressive episodes and hypomanic episodes). As expected, the results indicated a substantial but incomplete genetic overlap between the two types. Comparing the two types and their associations with other psychiatric disorders, the researchers found that bipolar I disorder showed a stronger genetic correlation with schizophrenia, whereas bipolar II disorder was more closely correlated with major depression. Additional studies with detailed trait data for large cohorts will be essential for further understanding the genetic components of these bipolar disorder types.
Drawing from the GWAS results, the researchers found that the 64 risk loci contained at least 161 individual genes. Some of these genes play a role in how neurons signal to each other in the brain. Some of these genes are also known to be targets for certain types of drugs currently used to treat bipolar disorder, such as antipsychotics, mood stabilizers, and antiepileptics. And some genes are known to be targets for other drug types, including calcium channel blockers (typically used to treat high blood pressure) and certain anesthetics.
The researchers then used an analytic technique called “fine-mapping” to connect risk loci with specific genes that are most likely to play a causal role in bipolar disorder. This technique identified 15 genes with the strongest evidence, which suggests they are promising candidates for further study.
Overall, the study findings confirmed many of the risk loci and genetic correlations reported in previous studies. But the study also represents an advance for the field, as a 1.5-fold increase in the number of participants effectively doubled the number of loci identified as associated with bipolar disorder. According to the researchers, this marks an “inflection point” in discovery because it indicates that the number of loci identified will continue to increase rapidly with the addition of new cohorts.
Taken together, these findings establish a more detailed picture of the genetic factors that underlie bipolar disorder and suggest possible biological targets for new treatments.
Mullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., Charney, A. W., Drange, O. K., Gandal, M. J., Hagenaars, S. P., Ikeda, M., Kamitaki, N., Kim, M., Krebs, K., Panagiotaropoulou, G.,…Andreassen, O.A. (2021). Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature Genetics, 53,817–829. https://doi.org/10.1038/s41588-021-00857-4