Human Islet Genotyping Initiative (HIGI)
HIGI provides a summary of the genetic characteristics of each donor, and generates partitioned genetic risk (pGRS) scores for type 1 and type 2 diabetes, along with comprehensive, genomic ancestry data for each islet donor, which can also be found in the RDR. These pGRS are visualized on the IIDP Research Data Repository (RDR), to provide an overview of the genetic contributions of a variety of physiological processes (e.g. islet cell, liver, adipose dysfunction) to each individual donor’s diabetes risk.
Current Pipeline
Genotyping, Data Cleaning, and Genetic Variable Generation
References
1. Luckett AM, Oram RA, Deutsch AJ, Ortega HI, Fraser DP, Ashok K, et al. Standardized Measurement of Type 1 Diabetes Polygenic Risk Across Multi-ancestry Population Cohorts."Diabetes care 2025;48(6):e81–e3. doi: 10.2337/dc25-0142 PMID:40267362.
2. Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature genetics 2024;627(8003):347–57. doi: 10.1038/s41586-024-07019-6 PMID:38374256.
3. Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Huerta-Chagoya A, et al. Multi-ancestry polygenic mechanisms of type 2 diabetes. Nat Med 2024;30(4):1065–74. doi: 10.1038/s41591-024-02865-35 PMID:38443691.
T1D Genetic Risk Score
Risk score normalization applies a static normalization based on the theoretical minimum and maximum genetic risk contributions ranging from no risk alleles to all risk alleles present. This procedure maps PRS values onto a 0–1 scale. Risk is presented relative to the current data freeze of IIDP donors (e.g. >19%). The donor’s risk total genetic risk for T1D is broken down into risk from non-HLA and different HLA classes.
References
1. Luckett AM, Oram RA, Deutsch AJ, Ortega HI, Fraser DP, Ashok K, et al. Standardized Measurement of Type 1 Diabetes Polygenic Risk Across Multi-ancestry Population Cohorts."Diabetes care 2025;48(6):e81–e3. doi: 10.2337/dc25-0142 PMID:40267362.
T2D Genetic Risk Score
Normalized Global Risk Score is now based on 1,232,226 variants in the full genome wide multi-ancestry analysis from Suzuki et al 2024. Risk Score normalization applies a static normalization based on the theoretical minimum and maximum genetic risk contributions ranging from no risk alleles to all risk alleles present.
Soft Clustering: These contain multi-ancestry PRS and pPS constructed using T2D index variants defined in the soft clustering framework from Smith et al. Under the soft clustering framework, genetic variants can contribute to multiple clusters with different weights, allowing for overlap and continuity across genetic mechanisms. The corresponding pPS are computed specifically under this soft clustering framework.
Hard Clustering:
These contain multi-ancestry PRS and pPS constructed using T2D index variants defined in the hard clustering framework from Suzuki et al. Under the hard clustering framework, each genetic variant is uniquely assigned to a single cluster, representing relatively discrete genetic mechanisms. The corresponding pPS are computed within this same clustering framework and are provided alongside the PRS.
References
1. Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature genetics 2024;627(8003):347–57. doi: 10.1038/s41586-024-07019-6 PMID:38374256.
2. Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Huerta-Chagoya A, et al. Multi-ancestry polygenic mechanisms of type 2 diabetes. Nat Med 2024;30(4):1065–74. doi: 10.1038/s41591-024-02865-35 PMID:38443691.
(Archived) HIGI Pipeline Overview
Sample Distribution, Preparation, and Genotyping
(Archived) Pipeline
Genotyping, Data Cleaning, and Genetic Variable Generation
References
1. Sharp, Seth A et al. "Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis." Diabetes care vol. 42,2 (2019): 200-207. doi:10.2337/dc18-1785 30655379.
2. Mahajan, Anubha et al. "Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation." Nature genetics vol. 54,5 (2022): 560-572. doi:10.1038/s41588-022-01058-3 35551307.
3. DiCorpo, Daniel et al. "Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts." Diabetes care vol. 45,3 (2022): 674-683. doi:10.2337/dc21-1395 35085396.