Multi-omics characterization of type 2 diabetes associated genetic variation.
Ravi MandlaKimberly LorenzXianyong YinOzvan BocherAlicia Huerta-ChagoyaAna Luiza ArrudaAnthony PironSusanne HornKen SuzukiKonstantinos HatzikotoulasLorraine SouthamHenry TaylorKaiyuan YangKarin HrovatinYue TongMaria LytriviNigel W RaynerJames B MeigsMark I McCarthyAnubha MahajanMiriam S UdlerCassandra N SpracklenMichael BoehnkeMarijana VujkovicJerome I RotterDecio L EizirikMiriam CnopHeiko LickertAndrew P MorrisEleftheria ZegginiBenjamin F VoightJosep Maria MercaderPublished in: medRxiv : the preprint server for health sciences (2024)
Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis -effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.
Keyphrases
- genome wide
- copy number
- dna methylation
- type diabetes
- genome wide association study
- regulatory t cells
- dendritic cells
- poor prognosis
- type iii
- ms ms
- genome wide association
- high resolution
- binding protein
- bioinformatics analysis
- glycemic control
- high density
- emergency department
- insulin resistance
- minimally invasive
- single cell
- metabolic syndrome
- genome wide identification
- adipose tissue
- drug induced
- transcription factor
- small molecule
- single molecule
- skeletal muscle
- immune response
- climate change
- machine learning
- human health
- drug delivery
- weight loss
- protein protein