Granulomas are complex cellular structures comprised predominantly of macrophages and lymphocytes that function to contain and kill invading pathogens. Here, we investigated single cell phenotypes associated with antimicrobial responses in human leprosy granulomas by applying single cell and spatial sequencing to leprosy biopsy specimens. We focused on reversal reactions (RR), a dynamic process in which some patients with disseminated lepromatous leprosy (L-lep) transition towards self-limiting tuberculoid leprosy (T-lep), mounting effective antimicrobial responses. We identified a set of genes encoding proteins involved in antimicrobial responses that are differentially expressed in RR versus L-lep lesions, and regulated by IFN-γ and IL-1β. By integrating the spatial coordinates of the key cell types and antimicrobial gene expression in RR and T-lep lesions, we constructed a map revealing the organized architecture of granulomas depicting compositional and functional layers by which macrophages, T cells, keratinocytes and fibroblasts can each contribute to the antimicrobial response.Nat Immunol.

27The transcriptome-wide association studies (TWAS) that test for association between the study 28 trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-29 eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. 30By using the gene expression imputation models fitted from reference datasets that have both 31 genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while 32 accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and 33 FUSION use parametric imputation models that have limitations for modeling the complex genetic 34 architecture of transcriptomic data. Therefore, to improve on this, we propose to use a Bayesian 35 method that assumes a data-driven nonparametric prior to impute gene expression. The 36nonparametric Bayesian method is flexible and general because it includes both of the parametric 37 imputation models used by PrediXcan and FUSION as special cases. Our simulation studies 38 2 showed that the nonparametric Bayesian model improved both imputation " for transcriptomic 39 data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian 40 method fitted transcriptomic imputation models for 57.6% more genes over PrediXcan, thus 41 improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred 42 for modeling the complex genetic architecture of transcriptomes and is expected to enhance 43 transcriptome-integrated genetic association studies. We implement our Bayesian approach in a 44 convenient software tool "TIGAR" (Transcriptome-Integrated Genetic Association Resource), 45 which imputes transcriptomic data and performs subsequent TWAS using individual-level or 46 summary-level GWAS data. 47 48 Introduction 49Genome-wide association studies (GWAS) have successfully identified thousands of 50 genetic risk loci for complex traits. However, the majority of these loci are located within noncoding 51 regions whose molecular mechanisms remain unknown 1-3 . Recent studies have shown that these 52 associated regions were enriched for regulatory elements such as enhancers (H3K27ac marks) 4; 53 5 and expression of quantitative trait loci (eQTL) 6; 7 , suggesting that the genetically regulated gene 54 expression might play a key role in the biological mechanisms of complex traits. Multiple studies 55 have recently generated rich transcriptomic datasets for diverse tissues of the human body, e.g., 56the Genotype-Tissue Expression (GTEx) project for 44 human tissues 6 , Genetic European 57Variation in Health and Disease (GEUVADIS) for lymphoblastoid cell lines 8 , Depression Genes 58 and Networks (DGN) for whole-blood samples 9 , and the North American Brain Expression 59 Consortium (NABEC) for cortex tissues 10 . Previous studies [11][12][13][14][15][16] have also shown that integrating 60 transcriptomic data in GWAS can help identify novel functional loci. 61The majority of GWAS projects do not possess tra...

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies (GWASs) to determine if commonly varying single nucleotide polymorphisms (SNPs) are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (HESS) methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn's disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations amongst the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

Background:Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases.Methods:We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically ‘favourable adiposity’ (FA) and ‘unfavourable adiposity’ (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases.Results:MR identified two sets of diseases. First, 11 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here, MR with the FA and UFA genetics showed opposing effects on risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism.Conclusions:Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthy.Funding:Diabetes UK, UK Medical Research Council, World Cancer Research Fund, National Cancer Institute.