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Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p-value < 5x10-8), which validated 12 previously reported loci for proinsulin and 10 additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher vs. lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait loci (eQTL) data, suggesting candidate genes including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal, but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms predisposing to disease.

Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.

Aims/hypothesis Determining how high BMI at different time points influences the risk of developing type 2 diabetes and affects insulin secretion and insulin sensitivity is critical. Methods By estimating childhood BMI in 441,761 individuals in the UK Biobank, we identified which genetic variants had larger effects on adulthood BMI than on childhood BMI, and vice versa. All genome-wide significant genetic variants were then used to separate the independent genetic effects of high childhood BMI from those of high adulthood BMI on the risk of type 2 diabetes and insulin-related phenotypes using Mendelian randomisation. We performed two-sample MR using external studies of type 2 diabetes, and oral and intravenous measures of insulin secretion and sensitivity. Results We found that a childhood BMI that was one standard deviation (1.97 kg/m2) higher than the mean, corrected for the independent genetic liability to adulthood BMI, was associated with a protective effect for seven measures of insulin sensitivity and secretion, including increased insulin sensitivity index (β=0.15; 95% CI 0.067, 0.225; p=2.79Å~10−4) and reduced fasting glucose levels (β=−0.053; 95% CI −0.089, −0.017; p=4.31Å~10−3). However, there was little to no evidence of a direct protective effect on type 2 diabetes (OR 0.94; 95% CI 0.85, 1.04; p=0.228) independently of genetic liability to adulthood BMI. Conclusions/interpretation Our results provide evidence of the protective effect of higher childhood BMI on insulin secretion and sensitivity, which are crucial intermediate diabetes traits. However, we stress that our results should not currently lead to any change in public health or clinical practice, given the uncertainty regarding the biological pathway of these effects and the limitations of this type of study

ObjectiveGenome-wide association studies (GWAS) have identified >100 risk loci for systemic lupus erythematosus (SLE), but the disease genes at most loci remain unclear, hampering translation of these genetic discoveries. We aimed to prioritise genes underlying the 110 SLE loci that were identified in the latest East Asian GWAS meta-analysis.MethodsWe built gene expression predictive models in blood B cells, CD4+and CD8+T cells, monocytes, natural killer cells and peripheral blood cells of 105 Japanese individuals. We performed a transcriptome-wide association study (TWAS) using data from the latest genome-wide association meta-analysis of 208 370 East Asians and searched for candidate genes using TWAS and three data-driven computational approaches.ResultsTWAS identified 171 genes for SLE (p–5); 114 (66.7%) showed significance only in a single cell type; 127 (74.3%) were in SLE GWAS loci. TWAS identified a strong association betweenCD83and SLE (p–8). Meta-analysis of genetic associations in the existing 208 370 East Asian and additional 1498 cases and 3330 controls found a novel single-variant association at rs72836542 (OR=1.11, p=4.5×10–9) aroundCD83. For the 110 SLE loci, we identified 276 gene candidates, including 104 genes at recently-identified SLE novel loci. We demonstrated in vitro that putative causal variant rs61759532 exhibited an allele-specific regulatory effect onACAP1, and that presence of the SLE risk allele decreasedACAP1expression.ConclusionsCell-level TWAS in six types of immune cells complemented SLE gene discovery and guided the identification of novel genetic associations. The gene findings shed biological insights into SLE genetic associations.

Abstract Ischemic stroke is one of the most severe neurological disorders with limited therapeutic strategies. The utilization of nanoparticle drug delivery systems is a burgeoning field and has been widely investigated. Among these, biomimetic drug delivery systems composed of biogenic membrane components and synthetic nanoparticles have been extensively highlighted in recent years. Biomimetic membrane camouflage presents an effective strategy to prolong circulation, reduce immunogenicity and enhance targeting. For one thing, biomimetic nanoparticles reserve the physical and chemical properties of intrinsic nanoparticle. For another, the biological functions of original source cells are completely inherited. Compared to conventional surface modification methods, this approach is more convenient and biocompatible. In this review, membrane-based nanoparticles derived from different donor cells were exemplified. The prospect of future biomimetic nanoparticles in ischemic stroke therapy was discussed. Graphic abstract

Determining how high body-mass index (BMI) at different time points influences the risk of developing type two diabetes (T2D), and affects insulin secretion and insulin sensitivity, is critical. By estimating childhood BMI in 441,761 individuals in the UK Biobank, we identified which genetic variants had larger effects on adulthood BMI than on childhood BMI, and vice-versa. All genome-wide significant genetic variants were then used to separate the independent genetic effects of high childhood BMI from high adulthood BMI on the risk of T2D and insulin related phenotypes using Mendelian randomisation and studies of T2D, and oral and intravenous measures of insulin secretion and sensitivity. We found that a 1.s.d. (= 1.97kg/m2) higher childhood BMI, corrected for the independent genetic liability to adulthood BMI, was associated with a protective effect for seven measures of insulin sensitivity and secretion, including an increased insulin sensitivity index (β = 0.15 [0.067, 0.225], p = 2.79×10−4), and reduced fasting glucose (β = -0.053 [-0.089, -0.017], p = 4.31×10−3). There was however little to no evidence of a direct protective effect on T2D (OR = 0.94 [0.85 - 1.04], p = 0.228), independently of genetic liability to adulthood BMI. Our results thus cumulatively provide evidence of the protective effect of higher childhood BMI on insulin secretion and sensitivity, which are crucial intermediate diabetes traits. However, we stress that our results should not currently lead to any change in public health or clinical practice, given the uncertainty in biological pathway of these effects, and the limitations of this type of study.Research in ContextHigh BMI in adulthood is associated with higher risk of type two diabetes, coupled with lower insulin sensitivity and secretion.Richardson et al [2020] used genetics to show that high BMI in childhood does not appear to increase the risk of type diabetes independently from its effect on adult BMI.We asked: does high childhood BMI affect insulin related traits such as fasting glucose and insulin sensitivity, independently of adulthood BMI?We used genetics to show that high childhood BMI has a protective effect on seven insulin sensitivity and secretion traits, including fasting glucose and measures of insulin sensitivity and secretion, independently of adulthood BMI.Our work has the potential to turn conventional understanding on its head – high BMI in childhood improves insulin sensitivity (when adjusting for knock on effects to high adult BMI) and opens up important questions about plasticity in childhood and compensatory mechanisms.

Background: Vitamin D level has been reported to be associated with psoriasis, atopic dermatitis, and vitiligo. However, its causal relationship with the risk of these three diseases remains unclear. Methods: We obtained genome-wide association statistics for three measures of circulating vitamin D levels (25(OH)D in 120,618 individuals, and 25(OH)D3 and epimeric form C3-epi-25(OH)D3 in 40,562 individuals) and for the diseases psoriasis (3871 cases and 333,288 controls), atopic dermatitis (21,399 cases and 95,464 controls), and vitiligo (4680 cases and 39,586 controls). We performed Mendelian randomization using inverse-variance weighted, weighted median, MR-Egger, and MR-pleiotropy residual sum and outlier methods. We carried out sensitivity analyses to evaluate the robustness of the results. Results: We showed that elevated vitamin D levels protected individuals from developing psoriasis (OR = 0.995, p = 8.84 × 10−4 for 25(OH)D; OR = 0.997, p = 1.81 × 10−3 for 25(OH)D3; and OR = 0.998, p = 0.044 for C3-epi-25(OH)D3). Genetically predicted risk of atopic dermatitis increased the levels of 25(OH)D (OR = 1.040, p = 7.14 × 10−4) and 25(OH)D3 (OR = 1.208, p = 0.048). A sensitivity analysis suggested the robustness of these causal associations. Conclusions: This study reported causal relationships between circulating vitamin D levels and the risk of psoriasis, atopic dermatitis, and vitiligo. These findings provide potential disease intervention and monitoring targets.

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Transcriptome-wide association studies (TWAS) and colocalization analysis are complementary integrative genetic association approaches routinely used to identify functional units underlying complex traits in post-genome-wide association study (post-GWAS) analyses. Recent studies suggest that both approaches are individually imperfect, but joint usage can yield robust and powerful inference results. This paper introduces a new statistical framework, INTACT, to perform probabilistic integration of TWAS and colocalization evidence for implicating putative causal genes. This procedure is flexible and can work with a wide range of existing TWAS and colocalization approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly-desirable feature, we describe an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated TWAS and colocalization analysis results. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. Finally, we apply the proposed methods to the GTEx data and a variety of GWAS summary statistics derived from complex and molecular traits previously analyzed by Hukku et al. and Sinnott-Armstrong et al. We find empirical evidence that the proposed methods improve and complement existing putative gene implication methods and are advantageous in evaluating and identifying key gene sets and biological pathways.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease involving multiple immune cells. To elucidate SLE pathogenesis, it is essential to understand the dysregulated gene expression pattern linked to various clinical statuses with a high cellular resolution. Here, we conducted a large-scale transcriptome study with 6,386 RNA sequencing data covering 27 immune cell types from 136 SLE and 89 healthy donors. We profiled two distinct cell-type-specific transcriptomic signatures: disease-state and disease-activity signatures, reflecting disease establishment and exacerbation, respectively. We then identified candidate biological processes unique to each signature. This study suggested the clinical value of disease-activity signatures, which were associated with organ involvement and therapeutic responses. However, disease-activity signatures were less enriched around SLE risk variants than disease-state signatures, suggesting that current genetic studies may not well capture clinically vital biology. Together, we identified comprehensive gene signatures of SLE, which will provide essential foundations for future genomic and genetic studies.