High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease – published online 20/12/2022

Hyunkyung Kim, Kenneth E. Westerman, Kirk Smith, Joshua Chiou, Joanne B. Cole, Timothy Majarian, Marcin von Grotthuss, Soo Heon Kwak, Jaegil Kim, Josep M. Mercader, Jose C. Florez, Kyle Gaulton, Alisa K. Manning, Miriam S. Udler

Genome-wide association studies (GWAS) have identified hundreds of loci associated with type 2 diabetes; however, clinical translation of findings has been challenging. In this issue, Kim et al (https://doi.org/10.1007/s00125-022-05848-6) describe a high-throughput pipeline using GWAS summary statistics to perform physiologically informed clustering of 323 independent type 2 diabetes loci and identified ten genetic clusters. These clusters represent subsets of diabetes risk variants that are most similar to each other based on their associations with disease-related traits. The ten clusters included both previously captured and novel clusters, and displayed tissue-specific enrichment of epigenomic marks. Cluster-based polygenic scores were associated with distinct clinical outcomes. The authors also demonstrated application of the pipeline to two other metabolic diseases. They conclude that their high-throughput clustering approach can produce robust findings and identify potential genetic subtypes of disease.

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