Type 1 Diabetes Whitepaper

Type 1 Diabetes Whitepaper
Orchid's team of genetic experts has developed a genetic risk score (GRS) for type 1 diabetes.
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 type 1 diabetes.

Type 1 Diabetes

Type 1 diabetes mellitus is a chronic condition where the pancreas produces little to no insulin and excessive amounts of glucose accumulates in the blood [1]. The precise cause of type I diabetes is unknown, but the disease is usually triggered by an autoimmune response that causes the immune system to target insulin-producing beta cells [2]. Genetics impacts the likelihood of developing the disease; an analysis conducted on 2 million children drawn from the Taiwan National Health Insurance Database found the heritability of type 1 diabetes to be approximately 66% [3].

Genetic risk score (GRS) 

A genetic risk score quantifies the degree to which an individual’s genetics increases their likelihood of developing a specific disease. The GRS for type I diabetes includes 64 variants and was developed based on the variants identified in a study that analyzed genomes of about 6481 individuals of European ancestry affected by type I diabetes. The study included 6481 cases (individuals with type I diabetes) and 9247 healthy controls(Sharp et al. 2019).

Our type 1 diabetes GRS has some special characteristics relative to our usual GRS. For these diseases, one specific loci, HLA DQ, confers a disproportionate share of genetic risk[4]. That is, they follow an oligogenic model, not a classic polygenic model. For that reason, the resulting GRS is not normally distributed.

Table 1: Discovery Cohort Statistics

Clinical Impact and Prevalence 

Approximately 1.6 million Americans are living with type I diabetes [5]. Type I diabetes is typically diagnosed between the ages of 4 and 14, though adulthood diagnoses do also occur [6]. The typical symptoms include increased thirst and urination, fatigue, weakness, irritability and other mood changes, and blurred vision [1]. The typical course of treatment for type 1 diabetes is insulin, administered regularly and prescribed by an endocrinologist [7].

Performant type 1 diabetes risk stratification   

Validated using a large cohort of real world people with type 1 diabetes 

Individuals in the 99th percentile of genetic risk have a 2.79% prevalence of type 1 diabetes, compared to the baseline rate. Baseline rate is the prevalence of the disease in the entire reference population. 

    

Figure 1: Risk gradient for type 1 diabetes. 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.  

Validation in UK Biobank. In the UK Biobank, cases were identified using self-reported type 1 diabetes and relevant ICD-9/ICD-10 diagnosis. See Supplementary Table 1. In the validation, prevalence of type 1 diabetes increased with GRS. We restricted our analysis to self-reported British individuals whose genetic ancestry matched their self-identification. With our phenotype definition there were 421 cases of type 1 diabetes and 408,099 controls.

Table 2:  Disease prevalence in elevated genetic risk subgroups for White British individuals. 

Identification of individuals at 27 times the baseline risk of type 1 diabetes

Individuals in the 99th percentile of genetic risk develop type 1 diabetes at 27 times the baseline rate. Baseline rate is the prevalence of the disease in the entire reference population. 

Comparison to Published Benchmarks

Orchid’s model achieves greater stratification performance with an AUC of 0.904 compared to the benchmark of 0.92. 

We compared the performance of our model as validated on the UK Biobank with the performance of the best model in Sharp et al (Sharp et al. 2019). The benchmarks in this table were generated on different datasets, so they are not precisely comparable.

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

1 Sharp et al (Sharp et al. 2019)

2 Because the PRS distribution for this phenotype is not normally distributed, OR per std is not an appropriate metric, and so we have not reported it.

3 This benchmark was achieved on different data than our AUC.

Citations

1. Mayo Clinic. Type 1 diabetes. [cited 7 Jan 2022]. Available: https://www.mayoclinic.org/diseases-conditions/type-1-diabetes/symptoms-causes/syc-20353011

2. Knip M, Siljander H, Ilonen J, Simell O, Veijola R. Role of humoral beta-cell autoimmunity in type 1 diabetes. Pediatr Diabetes. 2016;17 Suppl 22. doi:10.1111/pedi.12386

3. Kuo C-F, Chou I-J, Grainge MJ, Luo S-F, See L-C, Yu K-H, et al. Familial aggregation and heritability of type 1 diabetes mellitus and coaggregation of chronic diseases in affected families. Clin Epidemiol. 2018;10: 1447.

4. Sticht J, Álvaro-Benito M, Konigorski S. Type 1 Diabetes and the HLA Region: Genetic Association Besides Classical HLA Class II Genes. Front Genet. 2021;12: 683946.

5. American Diabetes Association. Statistics About Diabetes. [cited 7 Jan 2022]. Available: https://www.diabetes.org/resources/statistics/statistics-about-diabetes

6. Watts S. What Is Type 1 Diabetes? In: EndocrineWeb [Internet]. [cited 7 Jan 2022]. Available: https://www.endocrineweb.com/conditions/type-1-diabetes/type-1-diabetes

7. Healthline. A Complete List of Diabetes Medications. Available: https://www.healthline.com/health/diabetes/medications-list#type-1-diabetes

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