Plasma metabolomic signatures for copy number variants and COVID-19 risk loci in Northern Finland Populations

Author:

De Tisham1,Coin Lachlan2,Herberg Jethro1,Johnson Michael1,Jarvelin Marjo-Riitta1

Affiliation:

1. Imperial College London

2. The University of Melbourne

Abstract

Abstract

Here, we present genome-wide metabolomic signatures for copy-number variants (CNV) and single nucleotide polymorphisms (SNP) in two Finnish cohorts - The Northern Finland Birth Cohort 1966 (NFBC 1966) and NFBC 1986. This work builds upon our earlier study of characterising common CNVs in the TSPAN8 gene. Here, we have carried out an analysis of CNVs in over 9,300 individuals and characterised their dosage effect (CNV-metabolomic QTL) on 228 plasma lipoproteins and metabolites. We have reported reference (normal physiology) metabolomic signatures for up-to ~ 2.6 million COVID-19 GWAS results from the GRASP database, including for outcomes related to COVID-19 death, severity, and hospitalisation. Furthermore, by analysing two exemplar genes for COVID-19 severity namely LZTFL1 and OAS1, both reported to have Neanderthal ancestry, we have reported here two additional candidate genes for COVID-19 severity biology, namely 1) NFIX, a gene related to viral (adenovirus) replication and hematopoietic stem cells and 2) ACSL1, a known candidate gene for sepsis and bacterial inflammation. Based on our results and current literature we hypothesise that 1) charge imbalance across the cellular membrane between cations (Fe2+, Mg2+ etc) and anions (e.g., ROS, hydroxide ion from cellular Fenton reactions, superoxide etc), 2) iron trafficking within and between different cell types e.g., macrophages and 3) systemic oxidative stress response (e.g., lipid peroxidation mediated inflammation), together could be of relevance in severe COVID-19 cases. To conclude, our unique atlas of univariate and multivariate metabolomic signatures for CNVs (~ 7.2 million signatures) and SNPs (~ 0.7 million signatures) with deep annotations of various multi omics data sets provide an important reference knowledge base for human metabolism and diseases.

Publisher

Springer Science and Business Media LLC

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