SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci

Summary: We created a fast, robust and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol. Availability and implementation: http://broadinstitute.org/mpg/snpsea Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Computational and experimental analyses of retrotransposon-associated minisatellite DNAs in the soybean genome

BACKGROUND: Retrotransposons are mobile DNA elements that spread through genomes via the action of element-encoded reverse transcriptases. They are ubiquitous constituents of most eukaryotic genomes, especially those of higher plants. The pericentromeric regions of soybean (Glycine max) chromosomes contain \u3e3,200 intact copies of the Gmr9/GmOgre retrotransposon. Between the 3\u27 end of the coding region and the long terminal repeat, this retrotransposon family contains a polymorphic minisatellite region composed of five distinct, interleaved minisatellite families. To better understand the possible role and origin of retrotransposon-associated minisatellites, a computational project to map and physically characterize all members of these families in the G. max genome, irrespective of their association with Gmr9, was undertaken. METHODS: A computational pipeline was developed to map and analyze the organization and distribution of five Gmr9-associated minisatellites throughout the soybean genome. Polymerase chain reaction amplifications were used to experimentally assess the computational outputs. RESULTS: A total of 63,841 copies of Gmr9-associated minisatellites were recovered from the assembled G. max genome. Ninety percent were associated with Gmr9, an additional 9% with other annotated retrotransposons, and 1% with uncharacterized repetitive DNAs. Monomers were tandemly interleaved and repeated up to 149 times per locus. CONCLUSIONS: The computational pipeline enabled a fast, accurate, and detailed characterization of known minisatellites in a large, downloaded DNA database, and PCR amplification supported the general organization of these arrays

Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways.

The molecular and cellular processes that lead to renal damage and to the heterogeneity of lupus nephritis (LN) are not well understood. We applied single-cell RNA sequencing (scRNA-seq) to renal biopsies from patients with LN and evaluated skin biopsies as a potential source of diagnostic and prognostic markers of renal disease. Type I interferon (IFN)-response signatures in tubular cells and keratinocytes distinguished patients with LN from healthy control subjects. Moreover, a high IFN-response signature and fibrotic signature in tubular cells were each associated with failure to respond to treatment. Analysis of tubular cells from patients with proliferative, membranous and mixed LN indicated pathways relevant to inflammation and fibrosis, which offer insight into their histologic differences. In summary, we applied scRNA-seq to LN to deconstruct its heterogeneity and identify novel targets for personalized approaches to therapy

The immune cell landscape in kidneys of patients with lupus nephritis.

Lupus nephritis is a potentially fatal autoimmune disease for which the current treatment is ineffective and often toxic. To develop mechanistic hypotheses of disease, we analyzed kidney samples from patients with lupus nephritis and from healthy control subjects using single-cell RNA sequencing. Our analysis revealed 21 subsets of leukocytes active in disease, including multiple populations of myeloid cells, T cells, natural killer cells and B cells that demonstrated both pro-inflammatory responses and inflammation-resolving responses. We found evidence of local activation of B cells correlated with an age-associated B-cell signature and evidence of progressive stages of monocyte differentiation within the kidney. A clear interferon response was observed in most cells. Two chemokine receptors, CXCR4 and CX3CR1, were broadly expressed, implying a potentially central role in cell trafficking. Gene expression of immune cells in urine and kidney was highly correlated, which would suggest that urine might serve as a surrogate for kidney biopsies

Synoviocyte-targeted therapy synergizes with TNF inhibition in arthritis reversal

Fibroblast-like synoviocytes (FLS) are joint-lining cells that promote rheumatoid arthritis (RA) pathology. Current disease-modifying antirheumatic agents (DMARDs) operate through systemic immunosuppression. FLS-targeted approaches could potentially be combined with DMARDs to improve control of RA without increasing immunosuppression. Here, we assessed the potential of immunoglobulin-like domains 1 and 2 (Ig1&2), a decoy protein that activates the receptor tyrosine phosphatase sigma (PTPRS) on FLS, for RA therapy. We report that PTPRS expression is enriched in synovial lining RA FLS and that Ig1&2 reduces migration of RA but not osteoarthritis FLS. Administration of an Fc-fusion Ig1&2 attenuated arthritis in mice without affecting innate or adaptive immunity. Furthermore, PTPRS was down-regulated in FLS by tumor necrosis factor (TNF) via a phosphatidylinositol 3-kinase–mediated pathway, and TNF inhibition enhanced PTPRS expression in arthritic joints. Combination of ineffective doses of TNF inhibitor and Fc-Ig1&2 reversed arthritis in mice, providing an example of synergy between FLS-targeted and immunosuppressive DMARD therapies.publishedVersio

A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases

There is growing evidence of shared risk alleles between complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing between all individuals (whole-group pleiotropy), or a subset of individuals within a genetically heterogeneous cohort (subgroup heterogeneity). BUHMBOX is a well-powered statistic distinguishing between these two situations using genotype data. We observed a shared genetic basis between 11 autoimmune diseases and type 1 diabetes (T1D, p0.2, 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (p<10−9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (pBUHMBOX=0.008, 2,406 seronegative RA cases). We also observed a shared genetic basis between major depressive disorder (MDD) and schizophrenia (p<10−4) that was not explained by subgroup heterogeneity (pBUHMBOX=0.28 in 9,238 MDD cases)

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.

We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes

Rheumatoid arthritis is a prototypical autoimmune disease that causes joint inflammation and destruction1. There is currently no cure for rheumatoid arthritis, and the effectiveness of treatments varies across patients, suggesting an undefined pathogenic diversity1,2. Here, to deconstruct the cell states and pathways that characterize this pathogenic heterogeneity, we profiled the full spectrum of cells in inflamed synovium from patients with rheumatoid arthritis. We used multi-modal single-cell RNA-sequencing and surface protein data coupled with histology of synovial tissue from 79 donors to build single-cell atlas of rheumatoid arthritis synovial tissue that includes more than 314,000 cells. We stratified tissues into six groups, referred to as cell-type abundance phenotypes (CTAPs), each characterized by selectively enriched cell states. These CTAPs demonstrate the diversity of synovial inflammation in rheumatoid arthritis, ranging from samples enriched for T and B cells to those largely lacking lymphocytes. Disease-relevant cell states, cytokines, risk genes, histology and serology metrics are associated with particular CTAPs. CTAPs are dynamic and can predict treatment response, highlighting the clinical utility of classifying rheumatoid arthritis synovial phenotypes. This comprehensive atlas and molecular, tissue-based stratification of rheumatoid arthritis synovial tissue reveal new insights into rheumatoid arthritis pathology and heterogeneity that could inform novel targeted treatments