High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease
Authors' conclusion
Our approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.
Study context
- Population
- T2D loci from genome-wide association studies (GWAS) summary statistics for T2D and related traits (323 variants × 64 trait associations); independent cohort for polygenic score regressionindependent cohort
- Participants
- 25,419 participants
- Outcome
- Genetic clusters of T2D loci
Methodological detailshigh-throughput pipeline, Bayesian non-negative matrix factorisation (bNMF), epigenomic enrichment analysis
- High-throughput pipeline
- Bayesian non-negative matrix factorisation (bNMF)
- Epigenomic enrichment analysis
Main finding
As reported in the abstract
Clusters of genetic loci identified, recapturing the 5 prior clusters plus novel clusters (beta cell dysfunction, pronounced insulin secretion, alkaline phosphatase, lipoprotein A, SHBG levels)
10
Clusters related to insulin deficiency mechanisms
4
Clusters related to insulin resistance
5
Cluster with unclear mechanism
1
Further findings
Epigenomic enrichment
- Clusters displayed tissue-specific epigenomic enrichment; the 2 beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states
Cluster-specific polygenic scores
- Differentially associated with patient clinical characteristics and outcomes
Application to coronary artery disease and CKD
- Multiple clusters overlapping with T2D identified
Original publication
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