Human genetics uncovers MAP3K15 as an obesity-independent therapeutic target for diabetes

Abstract

We performed collapsing analyses on 454,796 UK Biobank (UKB) exomes to detect gene-level associations with diabetes. Recessive carriers of nonsynonymous variants in MAP3K15 were 30% less likely to develop diabetes ( P = 5.7 × 10 −10 ) and had lower glycosylated hemoglobin (β = −0.14 SD units, P = 1.1 × 10 −24 ). These associations were independent of body mass index, suggesting protection against insulin resistance even in the setting of obesity. We replicated these findings in 96,811 Admixed Americans in the Mexico City Prospective Study ( P < 0.05)Moreover, the protective effect of MAP3K15 variants was stronger in individuals who did not carry the Latino-enriched SLC16A11 risk haplotype ( P = 6.0 × 10 −4 ). Separately, we identified a Finnish-enriched MAP3K15 protein-truncating variant associated with decreased odds of both type 1 and type 2 diabetes ( P < 0.05) in FinnGen. No adverse phenotypes were associated with protein-truncating MAP3K15 variants in the UKB, supporting this gene as a therapeutic target for diabetes.

Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

“…First, by virtue of focusing on coding variants, the observed associations more often provide a direct causal link between variants in a gene and a metabolic trait. 35 , 45 , 47 , 48 Moreover, collapsing analyses draw their power from aggregating the signals of multiple rare variants (allelic series), which tend to be less impacted by local linkage disequilibrium structure, and are thus more likely to be enriched for causal genetic variants. Second, associations involving putative functional variants can also often provide clues to the desired therapeutic modulation, e.g., upregulation or downregulation of the target gene product, to mimic the protective effect for a given disease.…”

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

“…Signaling pathways include GPCR ligand binding [89], neutrophil degranulation [90], immune system [91], metabolism of lipids [92] and signal transduction [93] made great contribution to the development of IPF. MAP3K15 [94], PRTN3 [95], CX3CR1 [96], AGRP (agouti related neuropeptide) [97], MPO (myeloperoxidase) [98], CD5L [99], S100A8 [100], NPR3 [101], VEGFD (vascular endothelial growth factor D) [102], CXCL11 [103], IL1A [104], CBS (cystathionine beta-synthase) [105], WNT7A [106], SCD (stearoyl-CoA desaturase) [107], LRP2 [108], SLC6A4 [109], BDNF (brain derived neurotrophic factor) [110], CXCL10 [111], ANGPTL7 [112], S100A9 [113], NPY1R [114], IL1B [115], GPIHBP1 [116], CYP1B1 [117], CD36 [118], MACROD2 [119], TRIB3 [120], SPX (spexin hormone) [121], PCSK9 [122], GPD1 [123], CDH13 [124], FFAR4 [125], FGF2 [126], FASN (fatty acid synthase) [127], DGAT2 [128], DACH1 [129], PNPLA3 [130], FGF9 [131], SLC7A11 [132], CLIC5 [133], VIP (vasoactive intestinal peptide) [134], SMAD6 [135], BMPR2 [136], APOA1 [137], INSIG1 [138], TLR3 [139], NLRP12 [140], ADRB1 [141], TLR8 [142], GATA3 [143], CCR2 [144], TLR7 [145], CCRL2 [146], BMPER (BMP binding endothelial regulator) [147], CAV1 [148], TFPI (tissue factor pathway inhibitor) [149], FADS1 [150], SUCNR1 [151], CADM2 [152], SLC19A3 [153], SGCG (sarcoglycan gamma) [154], ADH1B [155], NEGR1 [156], HSD17B12 [157], OXTR (oxytocin receptor) [158] and ANKK1 […”

Comparative analysis of 136,401 Admixed Americans and 419,228 Europeans reveals ancestry-specific genetic determinants of clonal haematopoiesis

“…As the rare variants found to be associated with CH were ancestry-specific, it was not surprising that cross-ancestry exome-wide association meta-analysis did not identify additional risk variants. Utilising a gene-level collapsing test, which aggregates all qualifying rare germline variants for a given gene, may increase statistical power by testing the combined effect of rare variants 33,37 . As in previous work 33 , we used eleven different qualifying variant (QV) models to maximise discovery across potential genetic architectures (see Methods).…”

“…The model was fitted separately for each of the 22 autosomes and chromosome X. In step 2 of REGENIE, the germline variants (imputed with TOPMED reference panel) 22 and WES-identified variants 37 for GWAS and ExWAS, respectively, were tested for association with each CH phenotype using Firth logistic regression based on the additive model. Age, sex, and the first ten genetic principal components were included as covariates in both steps of REGENIE while the LOCO predictions were additionally included as covariate in step 2 of REGENIE.…”