Bipolar Disorder Whitepaper

Bipolar Disorder Whitepaper
Orchid's team of genetic experts has developed a genetic risk score (GRS) for bipolar disorder.
Written by Orchid Team
Orchid has developed advanced genetic risk scores (GRS) for a variety of diseases. Here we present our data on our GRS of bipolar disorder.

Bipolar Disorder

Bipolar disorder is a psychiatric disorder characterized by extreme changes in mood, energy level, and activity ranging from emotional highs (manic) to emotional lows (depression). There are two main types of bipolar disorder: bipolar I disorder and bipolar II disorder [1]. The exact cause of the disease is unknown but certain changes in the brain have been associated with it [2]. Multiple studies give an estimated range of heritability of between 79 and 93% [3]. One study, conducted on 19,124 pairs of twins from Finland, estimated bipolar I disorder to be 93% heritable [4].

Genetic risk score (GRS) 

A genetic risk score (GRS) quantifies the degree to which an individual’s genetics increases their likelihood of developing a specific disease. The GRS for bipolar disorder includes 286,021 variants and was developed based on the variants identified in a study that analyzed genomes of 51,710 individuals of European ancestry [5]. The study included 20,352 cases (individuals with bipolar disorder) and 31,358 healthy controls.

Table 1: Discovery cohort statistics. Variants in GRS and sample number used in the bipolar disorder GWAS.

Clinical Impact and Prevalence 

Around 4.4% of U.S. adults [6] will experience bipolar disorder in their lifetime. Although it can be diagnosed at any age, bipolar disorder is typically diagnosed in the teen years through the early 20s [7]. People with bipolar disorder have a lifespan that is 8-15 years shorter than people without this disorder [8] and are more likely to have other mental health issues like anxiety, attention-deficit /hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), and substance use disorders/dual diagnosis [9]. If left untreated, there can be problems related to drug and alcohol use, suicide or suicide attempts, legal or financial problems, damaged relationships, and poor school performance [7]. An effective treatment plan for bipolar disorder usually involves medication, such as mood stabilizers, antidepressants, and antipsychotics, as well as psychotherapy [10].

Performant bipolar disorder risk stratification   

Validated using a large cohort of individuals with known disease status 

Within the UK Biobank cohort, adults in the 99th percentile of genetic risk have a 1.66% absolute risk of bipolar disorder, compared to 0.45% for the baseline rate. Baseline rate is the prevalence of the disease in the entire reference population.

Figure 1: Risk gradient for bipolar disorder. Each blue dot represents a percentile of Genetic Risk Score, with its percent prevalence in UK Biobank self-reported White British in the y-axis. The black line represents the predicted prevalence from a logistic regression derived from the data.

In the UK Biobank, cases were identified using self-reported bipolar disorder and relevant ICD-9 and -10 diagnosis codes. See supplementary table for more details. In the validation, prevalence of the disease increased with GRS. Using our phenotype definition, in our sample of self-reported white British, there were 1855 cases of bipolar disorder and 406,665 controls (prevalence of 0.45%).

Table 2: Prevalence and odds ratios of bipolar disorder in elevated genetic risk subgroups. Adults at the tail end of GRS distribution were at an elevated risk for and had higher odds for the disease in comparison to the baseline rate of 0.45%.

Identification of adults at 3-4 times the baseline risk of bipolar disorder 

Adults in the 99th percentile of genetic risk have a 1.66% risk of developing bipolar disorder. The odds ratio for adults in the 99th percentile of genetic risk was 3.7. 

Comparison to Published Benchmarks

Orchid’s model achieves comparable stratification performance with an AUC of 0.624 compared to the benchmark at 0.65.

We compared the performance of our model as validated on the UK Biobank with the performance of a similar model in Mullins et al [11]. In the first column, we give the results for our predictor with the phenotype as described above. In the second, we report the metrics for the best-performing predictor in Mullins et al using the numbers reported in their paper.

Table 3: Accuracy metric comparison. Our model compared to reference.

 1Mullins et al[11]


  1. NIMH–Bipolar Disorder [cited 2 Aug 2022] Available: 
  2. Hibar DP, Westlye LT, Doan NT, Jahanshad N, et. al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol. Psychiatry 2018; 23: 932–942. 
  3. Barnett JH, Smoller JW. The genetics of bipolar disorder. Neuroscience. 2009;164(1): 331-343. 
  4. Kieseppä T, Partonen T, Haukka J, Kaprio J, Lönnqvist J. High concordance of bipolar I disorder in a nationwide sample of twins. Am J Psychiatry. 2004;161(10):1814-1821. 
  5. Stahl EA, Breen G, Forstner AJ, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51(5):793-803. 
  6. NIMH–Bipolar Disorder Statistics [cited 2 Aug 2022] Available: ttps://
  7. Mayo Clinic–Bipolar Disorder–Symptoms & Causes [cited 2 Aug 2022] Available: 
  8. Chan J, Wong C, Yung N, Chen E, & Chang W. Excess mortality and life-years lost in people with bipolar disorder: An 11-year population-based cohort study. Epidemiology and Psychiatric Sciences 2021; 30: E39. DOI: 
  9. Cleveland Clinic–Bipolar Disorder [cited 2 Aug 2022] Available:
  10. NIMH–Bipolar Disorder–Treatments and Therapies [cited 2 Aug 2022] Available:
  11. Mullins N, Forstner AJ, O'Connell KS, et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet. 2021; 53(6):817-829.

Appendix: Disease case identification and number of cases in UK Biobank

*Type 1 diabetes was defined as a combination the following inclusion and exclusion criteria:

  • Self-diagnosed diabetes (any type)
  • No self-diagnosed Type 2 diabetes
  • Age of diabetes onset between 0 and 20 years
  • Started insulin within one year of diagnosis of diabetes
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