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Multi-ancestry polygenic mechanisms of type 2 diabetes.

Kirk SmithAaron J DeutschCarolyn McGrailHyunkyung KimSarah HsuAlicia Huerta-ChagoyaRavi MandlaPhilip H SchroederKenneth E WestermanŁukasz SzczerbińskiTimothy D MajarianVarinderpal KaurAlice WilliamsonNoah A ZaitlenMelina ClaussnitzerJose C FlorezAlisa K ManningJosep Maria MercaderKyle J GaultonMiriam S Udler
Published in: Nature medicine (2024)
Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m - 2 in the European subpopulation and 24.2 (22.9-25.5) kg m - 2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m - 2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.
Keyphrases
  • genome wide
  • type diabetes
  • body mass index
  • single cell
  • copy number
  • cardiovascular disease
  • physical activity
  • weight gain
  • systematic review
  • rna seq
  • metabolic syndrome
  • skeletal muscle
  • machine learning
  • deep learning