A genome wide association study of chronic spontaneous urticaria risk and heterogeneity.

BACKGROUND: Chronic spontaneous urticaria (CSU) is a dermatologic condition that is characterized by spontaneous, pruritic hives and/or angioedema that persist for six weeks or longer with no identifiable trigger. Anti-histamines and second line therapies such as omalizumab are effective for some CSU patients, but others remain symptomatic with significant impact on quality of life. This variable response to treatment and autoantibodies levels across patients highlight clinically heterogeneous subgroups. OBJECTIVE: We aimed to highlight pathways involved in CSU by investigating the genetics of CSU risk and subgroups. METHODS: We performed a genome-wide association study (GWAS) of 679 CSU patients and 4,446 controls and a GWAS of Chronic Urticaria (CU) index, which measures IgG autoantibodies levels, by comparing 447 CU-index low to 183 CU-index high patients. We also tested whether polygenic scores for autoimmune-related disorders associate with CSU risk and CU-index. RESULTS: We identified two loci significantly associated with disease risk. The strongest association maps to position 56 of HLA-DQA1 (P=1.69x10-9), where the arginine residue was associated with increased risk (OR=1.64). The second association signal colocalizes with expression-quantitative trait loci for ITPKB in whole blood (probabilitycolocalization=0.997). The arginine residue at position 56 of HLA-DQA1 was also associated with increased risk of CU-index high (P=6.15x10-5, OR=1.86), while the ITKPB association was not (P=0.64). Polygenic scores for three autoimmune-related disorders (hypothyroidism, type-1 diabetes, and vitiligo) are associated with CSU risk and CU-index (P<2.34x10-3, OR>1.72). CONCLUSION: This GWAS of CSU identifies two genome-wide significant loci and highlights the shared genetics between CU-index and autoimmune disorders.

Drug repurposing against COVID-19 by GWAS

This note is prepared by the authors of a recent publication on shared genetic architecture of drug response based on summary statistics from genome-wide association studies (GWAS) to propose a drug repurposing approach for the treatment of coronavirus COVID-19. The authors proposed that in silico studies may be preceded by analyzing shared genetic architecture of drug response based on existing GWAS.Copyright © 2020, Health Biotechnology and Biopharma.

Viral links . . . and other stories

Genetics of multiple sclerosis severity A genome-wide association study in 2000 individuals with relapsing remitting multiple sclerosis failed to identify any genetic variants with a powerful influence on clinical outcomes. A machine learning algorithm using information about 60 000 single nucleotide variants did have some predictive value, but it could explain only a small proportion of the variability in severity of the disease. Brain derived neurotrophic factor, a protein that has been shown to boost neuroplasticity, synapse formation, and cognitive performance in animal studies, may be involved.

The Genetic Risk for COVID-19 Severity Is Associated With Defective Immune Responses.

Recent genome-wide association studies (GWASs) of COVID-19 patients of European ancestry have identified genetic loci significantly associated with disease severity. Here, we employed the detailed clinical, immunological and multi-omics dataset of the Human Functional Genomics Project (HFGP) to explore the physiological significance of the host genetic variants that influence susceptibility to severe COVID-19. A genomics investigation intersected with functional characterization of individuals with high genetic risk for severe COVID-19 susceptibility identified several major patterns: i. a large impact of genetically determined innate immune responses in COVID-19, with ii. increased susceptibility for severe disease in individuals with defective cytokine production; iii. genetic susceptibility related to ABO blood groups is probably mediated through the von Willebrand factor (VWF) and endothelial dysfunction. We further validated these identified associations at transcript and protein levels by using independent disease cohorts. These insights allow a physiological understanding of genetic susceptibility to severe COVID-19, and indicate pathways that could be targeted for prevention and therapy.

Proposing a “Brain Health Checkup (BHC)” as a Global Potential “Standard of Care” to Overcome Reward Dysregulation in Primary Care Medicine: Coupling Genetic Risk Testing and Induction of “Dopamine Homeostasis”

In 2021, over 100,000 people died prematurely from opioid overdoses. Neuropsychiatric and cognitive impairments are underreported comorbidities of reward dysregulation due to genetic antecedents and epigenetic insults. Recent genome-wide association studies involving millions of subjects revealed frequent comorbidity with substance use disorder (SUD) in a sizeable meta-analysis of depression. It found significant associations with the expression of NEGR1 in the hypothalamus and DRD2 in the nucleus accumbens, among others. However, despite the rise in SUD and neuropsychiatric illness, there are currently no standard objective brain assessments being performed on a routine basis. The rationale for encouraging a standard objective Brain Health Check (BHC) is to have extensive data available to treat clinical syndromes in psychiatric patients. The BHC would consist of a group of reliable, accurate, cost-effective, objective assessments involving the following domains: Memory, Attention, Neuropsychiatry, and Neurological Imaging. Utilizing primarily PUBMED, over 36 years of virtually all the computerized and written-based assessments of Memory, Attention, Psychiatric, and Neurological imaging were reviewed, and the following assessments are recommended for use in the BHC: Central Nervous System Vital Signs (Memory), Test of Variables of Attention (Attention), Millon Clinical Multiaxial Inventory III (Neuropsychiatric), and Quantitative Electroencephalogram/P300/Evoked Potential (Neurological Imaging). Finally, we suggest continuing research into incorporating a new standard BHC coupled with qEEG/P300/Evoked Potentials and genetically guided precision induction of “dopamine homeostasis” to diagnose and treat reward dysregulation to prevent the consequences of dopamine dysregulation from being epigenetically passed on to generations of our children.

Drug repurposing against COVID-19 by GWAS

This note is prepared by the authors of a recent publication on shared genetic architecture of drug response based on summary statistics from genome-wide association studies (GWAS) to propose a drug repurposing approach for the treatment of coronavirus COVID-19. The authors proposed that in silico studies may be preceded by analyzing shared genetic architecture of drug response based on existing GWAS. © 2020, Health Biotechnology and Biopharma.

COVID-19: impact on Public Health and hypothesis-driven investigations on genetic susceptibility and severity.

COVID-19 is a new complex multisystem disease caused by the novel coronavirus SARS-CoV-2. In slightly over 2 years, it infected nearly 500 million and killed 6 million human beings worldwide, causing an unprecedented coronavirus pandemic. Currently, the international scientific community is engaged in elucidating the molecular mechanisms of the pathophysiology of SARS-CoV-2 infection as a basis of scientific developments for the future control of COVID-19. Global exome and genome analysis efforts work to define the human genetics of protective immunity to SARS-CoV-2 infection. Here, we review the current knowledge regarding the SARS-CoV-2 infection, the implications of COVID-19 to Public Health and discuss genotype to phenotype association approaches that could be exploited through the selection of candidate genes to identify the genetic determinants of severe COVID-19.

Prioritisation of Candidate Genes Underpinning COVID-19 Host Genetic Traits Based on High-Resolution 3D Chromosomal Topology.

Genetic variants showing associations with specific biological traits and diseases detected by genome-wide association studies (GWAS) commonly map to non-coding DNA regulatory regions. Many of these regions are located considerable distances away from the genes they regulate and come into their proximity through 3D chromosomal interactions. We previously developed COGS, a statistical pipeline for linking GWAS variants with their putative target genes based on 3D chromosomal interaction data arising from high-resolution assays such as Promoter Capture Hi-C (PCHi-C). Here, we applied COGS to COVID-19 Host Genetic Consortium (HGI) GWAS meta-analysis data on COVID-19 susceptibility and severity using our previously generated PCHi-C results in 17 human primary cell types and SARS-CoV-2-infected lung carcinoma cells. We prioritise 251 genes putatively associated with these traits, including 16 out of 47 genes highlighted by the GWAS meta-analysis authors. The prioritised genes are expressed in a broad array of tissues, including, but not limited to, blood and brain cells, and are enriched for genes involved in the inflammatory response to viral infection. Our prioritised genes and pathways, in conjunction with results from other prioritisation approaches and targeted validation experiments, will aid in the understanding of COVID-19 pathology, paving the way for novel treatments.

Hypotheses and facts for genetic factors related to severe COVID-19.

Genome-wide association analysis allows the identification of potential candidate genes involved in the development of severe coronavirus disease 2019 (COVID-19). Hence, it seems that genetics matters here, as well. Nevertheless, the virus's nature, including its RNA structure, determines the rate of mutations leading to new viral strains with all epidemiological and clinical consequences. Given these observations, we herein comment on the current hypotheses about the possible role of the genes in association with COVID-19 severity. We discuss some of the major candidate genes that have been identified as potential genetic factors associated with the COVID-19 severity and infection susceptibility: HLA, ABO, ACE2, TLR7, ApoE, TYK2, OAS, DPP9, IFNAR2, CCR2, etc. Further study of genes and genetic variants will be of great benefit for the prevention and assessment of the individual risk and disease severity in different populations. These scientific data will serve as a basis for the development of clinically applicable diagnostic and prognostic tests for patients at high risk of COVID-19.

Using the PhenX Toolkit to Select Standard Measurement Protocols for Your Research Study.

The goals of PhenX (consensus measures for Phenotypes and eXposures) are to promote the use of standard measurement protocols and to help investigators identify opportunities for collaborative research and cross-study analysis, thus increasing the impact of individual studies. The PhenX Toolkit (https://www.phenxtoolkit.org/) offers high-quality, well-established measurement protocols to assess phenotypes and exposures in studies with human participants. The Toolkit contains protocols representing 29 research domains and 6 specialty collections of protocols that add depth to the Toolkit in specific research areas (e.g., COVID-19, Social Determinants of Health [SDoH], Blood Sciences Research [BSR], Mental Health Research [MHR], Tobacco Regulatory Research [TRR], and Substance Abuse and Addiction [SAA]). Protocols are recommended for inclusion in the PhenX Toolkit by Working Groups of domain experts using a consensus process that includes input from the scientific community. For each PhenX protocol, the Toolkit provides a detailed description, the rationale for inclusion, and supporting documentation. Users can browse protocols in the Toolkit, search the Toolkit using keywords, or use Browse Protocols Tree to identify protocols of interest. The PhenX Toolkit provides data dictionaries compatible with the database of Genotypes and Phenotypes (dbGaP), Research Electronic Data Capture (REDCap) data submission compatibility, and data collection worksheets to help investigators incorporate PhenX protocols into their study design. The PhenX Toolkit provides resources to help users identify published studies that used PhenX protocols. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Using the PhenX Toolkit to support or extend study design.

Statistical power in COVID-19 case-control host genomic study design.

The identification of genetic variation that directly impacts infection susceptibility to SARS-CoV-2 and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic, and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity, and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity in published COVID-19 study designs. We demonstrate that limited phenotypic data and exposure/infection information in the early stages of the pandemic significantly impact the ability to detect most genetic variants with moderate effect sizes, especially when studying susceptibility to SARS-CoV-2 infection. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies.