Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs ...targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).
There is considerable interest in GIPR agonism to enhance the insulinotropic and extrapancreatic effects of GIP, thereby improving glycemic and weight control in type 2 diabetes (T2D) and obesity. ...Recent genetic epidemiological evidence has implicated higher GIPR-mediated GIP levels in raising coronary artery disease (CAD) risk, a potential safety concern for GIPR agonism. We therefore aimed to quantitatively assess whether the association between higher GIPR-mediated fasting GIP levels and CAD risk is mediated via GIPR or is instead the result of linkage disequilibrium (LD) confounding between variants at the
locus. Using Bayesian multitrait colocalization, we identified a
missense variant, rs1800437 (G allele; E354), as the putatively causal variant shared among fasting GIP levels, glycemic traits, and adiposity-related traits (posterior probability for colocalization PP
> 0.97; PP explained by the candidate variant PP
= 1) that was independent from a cluster of CAD and lipid traits driven by a known missense variant in
(rs7412; distance to E354 ∼770 Kb;
with E354 = 0.004; PP
> 0.99; PP
= 1). Further, conditioning the association between E354 and CAD on the residual LD with rs7412, we observed slight attenuation in association, but it remained significant (odds ratio OR per copy of E354 after adjustment 1.03; 95% CI 1.02, 1.04;
= 0.003). Instead, E354's association with CAD was completely attenuated when conditioning on an additional established CAD signal, rs1964272 (
with E354 = 0.27), an intronic variant in
(OR for E354 after adjustment for rs1964272: 1.01; 95% CI 0.99, 1.03;
= 0.06). We demonstrate that associations with GIP and anthropometric and glycemic traits are driven by genetic signals distinct from those driving CAD and lipid traits in the
region and that higher E354-mediated fasting GIP levels are not associated with CAD risk. These findings provide evidence that the inclusion of GIPR agonism in dual GIPR/GLP1R agonists could potentiate the protective effect of GLP-1 agonists on diabetes without undue CAD risk, an aspect that has yet to be assessed in clinical trials.
In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10
) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for ...nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.
During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. ...The developing Chinese Color Nest Project (devCCNP, 2013-2022), the first ten-year stage of the lifespan CCNP (2013-2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0-17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" in the Chinese Color Nest Project (CCNP) - Lifespan Brain-Mind Development Data Community ( https://ccnp.scidb.cn ) at the Science Data Bank.
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