The role of the X chromosome in infectious diseases

Many infectious diseases in humans present with a sex bias. This bias arises from a combination of environmental factors, hormones and genetics. In this study, we review the contribution of the X chromosome to the genetic factor associated with infectious diseases. First, we give an overview of the X-linked genes that have been described in the context of infectious diseases and group them in four main pathways that seem to be dysregulated in infectious diseases: nuclear factor kappa-B, interleukin 2 and interferon γ cascade, toll-like receptors and programmed death ligand 1. Then, we review the infectious disease associations in existing genome-wide association studies (GWAS) from the GWAS Catalog and the Pan-UK Biobank, describing the main associations and their possible implications for the disease. Finally, we highlight the importance of including the X chromosome in GWAS analysis and the importance of sex-specific analysis

Analysis of inflammatory protein profiles in the circulation of COVID-19 patients identifies patients with severe disease phenotypes

Background: The coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) can present with a broad range of clinical manifestations, ranging from asymptomatic to severe multiple organ failure. The severity of the disease can vary depending on factors such as age, sex, ethnicity, and pre-existing medical conditions. Despite multiple efforts to identify reliable prognostic factors and biomarkers, the predictive capacity of these markers for clinical outcomes remains poor. Circulating proteins, which reflect the active mechanisms in an individual, can be easily measured in clinical practice and therefore may be useful as biomarkers for COVID-19 disease severity. In this study, we sought to identify protein biomarkers and endotypes for COVID-19 severity and evaluate their reproducibility in an independent cohort.Methods: We investigated a cohort of 153 Greek patients with confirmed SARS-CoV-2 infection in which plasma protein levels were measured using the Olink Explore 1536 panel, which consists of 1472 proteins. We compared the protein profiles from severe and moderate COVID-19 patients to identify proteins associated with disease severity. To evaluate the reproducibility of our findings, we compared the protein profiles of 174 patients with comparable COVID-19 severities in a US COVID-19 cohort to identify proteins consistently correlated with COVID-19 severity in both groups.Results: We identified 218 differentially regulated proteins associated with severity, 20 proteins were also replicated in an external cohort which we used for validation. Moreover, we performed unsupervised clustering of patients based on 97 proteins with the highest log2 fold changes in order to identify COVID-19 endotypes. Clustering of patients based on differentially regulated proteins revealed the presence of three clinical endotypes. While endotypes 2 and 3 were enriched for severe COVID-19 patients, endotypes 3 represented the most severe form of the disease.Conclusions: These results suggest that identified circulating proteins may be useful for identifying COVID-19 patients with worse outcomes, and this potential utility may extend to other populations. Trial registration: NCT04357366.</p

snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions

Motivation: Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed.Results: We developed an R package snpEnrichR for SNP co-localization analysis. The software integrates different tools for random SNP generation and genome co-localization analysis to automatize and help users to create custom SNP co-localization analysis. We show via an example that including proxy SNPs in SNP co-localization analysis enhances the sensitivity of co-localization detection.Availability: The software is available at https://github.com/kartiek/snpEnrichR.</p

Impact of Human Genetic Variation on C-Reactive Protein Concentrations and Acute Appendicitis

BACKGROUND: Acute appendicitis is one of the most common abdominal emergencies worldwide. Both environmental and genetic factors contribute to the disease. C-reactive protein (CRP) is an important biomarker in the diagnosis of acute appendicitis. CRP concentrations are significantly affected by genetic variation. However, whether such genetic variation is causally related to appendicitis risk remains unclear. In this study, the causal relationship between single-nucleotide polymorphisms (SNPs) associated with circulating CRP concentrations and the risk and severity of acute appendicitis was investigated. METHODS: CRP concentrations in serum of appendicitis patients (n = 325) were measured. Appendicitis was categorized as complicated/uncomplicated and gangrenous/non-gangrenous. Imputed SNP data (n = 287) were generated. A genome-wide association study (GWAS) on CRP concentrations and appendicitis severity was performed. Intersection and colocalization of the GWAS results were performed with appendicitis and CRP-associated loci from the Pan-UKBB cohort. A functional-genomics approach to prioritize genes was employed. RESULTS: Thirteen percent of significant CRP quantitative trait loci (QTLs) that were previously identified in a large cohort of healthy individuals were replicated in our small patient cohort. Significant enrichment of CRP-QTLs in association with appendicitis was observed. Among these shared loci, the two top loci at chromosomes 1q41 and 8p23.1 were characterized. The top SNP at chromosome 1q41 is located within the promoter of H2.0 Like Homeobox (HLX) gene, which is involved in blood cell differentiation, and liver and gut organogeneses. The expression of HLX is increased in the appendix of appendicitis patients compared to controls. The locus at 8p23.1 contains multiple genes, including cathepsin B (CTSB), which is overexpressed in appendix tissue from appendicitis patients. The risk allele of the top SNP in this locus also increases CTSB expression in the sigmoid colon of healthy individuals. CTSB is involved in collagen degradation, MHC class II antigen presentation, and neutrophil degranulation. CONCLUSIONS: The results of this study prioritize HLX and CTSB as potential causal genes for appendicitis and suggest a shared genetic mechanism between appendicitis and CRP concentrations

Fine mapping of the celiac disease-associated LPP locus reveals a potential functional variant

Transplantation and immunomodulatio

Refined mapping of autoimmune disease associated genetic variants with gene expression suggests an important role for non-coding RNAs

Genome-wide association and fine-mapping studies in 14 autoimmune diseases (AID) have implicated more than 250 loci in one or more of these diseases. As more than 90% of AID-associated SNPs are intergenic or intronic, pinpointing the causal genes is challenging. We performed a systematic analysis to link 460 SNPs that are associated with 14 AID to causal genes using transcriptomic data from 629 blood samples. We were able to link 71 (39%) of the AID-SNPs to two or more nearby genes, providing evidence that for part of the AID loci multiple causal genes exist. While 54 of the AID loci are shared by one or more AID, 17% of them do not share candidate causal genes. In addition to finding novel genes such as ULK3, we also implicate novel disease mechanisms and pathways like autophagy in celiac disease pathogenesis. Furthermore, 42 of the AID SNPs specifically affected the expression of 53 non-coding RNA genes. To further understand how the non-coding genome contributes to AID, the SNPs were linked to functional regulatory elements, which suggest a model where AID genes are regulated by network of chromatin looping/non-coding RNAs interactions. The looping model also explains how a causal candidate gene is not necessarily the gene closest to the AID SNP, which was the case in nearly 50% of cases

Deconvolution of bulk blood eQTL effects into immune cell subpopulations

BACKGROUND: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). RESULTS: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. CONCLUSIONS: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution)