Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

<div><p>Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.</p></div

DataVault: Baillie Lab Longterm Storage Vault A

## Access ## This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh staff. External users may request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: https://www.ed.ac.uk/is/research-support/datavault .This vault contains archived information from the Baillie lab (from 2014-2022) Original files were transferred from: lx05, Eddie

Additional file 1 of Rapidly improving ARDS differs clinically and biologically from persistent ARDS

Additional file 1. Figure E1. Study Design. All patients were enrolled in the Early Assessment of Renal and Lung Injury (EARLI) cohort from November 2008 to May 2018. We analyzed data from 215 patients who met Berlin criteria for ARDS on day 1 or 2 of the study, were endotracheally intubated at the time of meeting Berlin criteria, and had plasma biomarker measurements available. Patients met criteria for rapidly improving ARDS if any of the following criteria were met: (i) Pao2:Fio2 > 300 or (ii) Spo2:Fio2 > 315 on the day following diagnosis of ARDS (day 2) or (iii) unassisted breathing by day 2 and for the next 48 hours (defined as absence of endotracheal intubate on day 2 through day 4). Table E1. Comorbidities were compared in patients with RIARDS versus persistent ARDS. Cirrhosis was more commonly identified in persistent ARDS. Other comorbidities were not significantly different between each group. Table E2. Concomitant medical conditions were compared in patients with RIARDS versus persistent ARDS. Hypertensive crisis at time of enrollment was more common in patients with RIARDS compared to those with persistent ARDS. Table E3. Type of steroids administered over the first 48 hours of ARDS diagnosis in patients with RIARDS compared to those with persistent ARDS. Table E4. Sensitivity analysis focused on patients with severe ARDS (defined by a PaO2:FiO2 equal to or less than 100 at time of enrollment). Vasopressor-dependent shock was more commonly seen in patients with severe persistent ARDS compared to severe RIARDS. Hospital mortality was significantly higher while ICU-free days was lower in those with severe persistent ARDS compared to severe RIARDS. Table E5. Sensitivity analysis focused on patients with severe ARDS. Patient comorbidities did not differ significantly between RIARDS and persistent disease among those with severe ARDS. Table E6. Sensitivity analysis focused on patients with severe ARDS. Concomitant medical conditions did not differ significantly between RIARDS and persistent disease among those with severe ARDS. Table E7. Sensitivity analysis focused on patients with severe ARDS. Ventilatory parameters did not differ significantly between RIARDS and persistent disease among those with severe ARDS. Table E8. Sensitivity analysis focused on patients with severe ARDS (defined by a PaO2:FiO2 equal to or less than 100 at time of enrollment). Plasma inflammatory biomarkers were significantly higher in those with severe persistent ARDS compared to severe RIARDS. Table E9. Sensitivity analysis comparing RIARDS and persistent disease among cases allocated to the hyperinflammatory phenotype. Similar to the results seen in the overall cohort, compared to patients with hyperinflammatory persistent ARDS, patients with hyperinflammatory RIARDS had significantly lower in-hospital mortality at 28 days and higher ICU-free days. However, contrary to results seen in the overall cohort, vasopressor-dependent shock on day 1 was equally prevalent. Severe hypoxemia was more commonly seen in persistent ARDS and not appreciated in RIARDS. Table E10. Sensitivity analysis comparing RIARDS and persistent disease among cases allocated to the hyperinflammatory phenotype. Microbiology and medications received did not differ between each group. Table E11. Sensitivity analysis comparing RIARDS and persistent disease among cases allocated to the hyperinflammatory phenotype. No significant differences were found in plasma inflammatory biomarker concentration between patients with RIARDS and persistent ARDS. Table E12. Total counts of missing values. Ventilatory parameters stratified by persistent ARDS versus RIARDS. Table E13. Total counts of missing values. Biomarkers stratified by persistent ARDS versus RIARDS. Table E14. Total counts of missing values. Comorbidities and concomitant medical conditions stratified by persistent ARDS versus RIARDS

Examples of detail of chromosomal regions surrounding regulatory regions significantly coexpressed in ulcerative colitis (TSS+/-150Mb).

<p>(a) Region surrounding IL10 (b) Region surrounding C1orf106. Top panel: Coloured rectangles show genomic location of individual regulatory regions (promoters or enhancers). Height of regulatory regions on y-axis depicts the coexpression score assigned to this regulatory region; groups of regulatory regions considered as a single unit (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005934#sec002" target="_blank">Methods</a>) share the same colour. Black circles show GWAS p-values for individual SNPs. Red circles show causative probabilities estimated by Huang <i>et al</i> for specific variants, where available. Bottom panel: genomic locations of known protein coding transcripts in sense (green) and antisense (purple).</p

Results of coexpression analysis for a range of human traits for which high-quality data are available: Crohn's disease, ulcerative colitis, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, height, systolic blood pressure (SBP) and diastolic blood pressure (DBP).

<p>Results of coexpression analysis for a range of human traits for which high-quality data are available: Crohn's disease, ulcerative colitis, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, height, systolic blood pressure (SBP) and diastolic blood pressure (DBP).</p

Optimisation of GWAS p-value threshold.

<p>Coexpression signals are shown for six different –log₁₀(p) bins for GWAS p values from a single study of Crohn’s disease. From each bin, 800 SNPs were selected at random. No signal for coexpression is detected at weak p-values.</p

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease - Fig 3

<p><b>(a) Enrichment (y axis, observed <i>SNPs per Mb</i>: expected <i>SNPs per Mb</i>) at increasing search window sizes (x axis) upstream and downstream from the transcription start site (TSS) for increasingly strong GWAS signals (z axis, −<i>log</i></b>_<b>10</b><b><i>p</i>).</b> (b) Change in coexpression signal using different subsets of the FANTOM5 dataset, using the Crohn’s disease GWAS as the input set. Q:Q plots of observed:expected NDA scores obtained using a given subset of samples (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005934#sec002" target="_blank">methods</a> for full description of each subset). Rows indicate the subset of regulatory regions used in each analysis. Percentage of significantly coexpressed entities (hits, <i>FDR</i> < 0.05) and <i>p</i>-value (Kolmogorov-Smirnov test) comparing observed (blue) and expected (red) distributions are shown below each plot.</p