Genome-Wide Meta-Analysis Identifies Multiple Novel Rare Variants to Predict Common Human Infectious Diseases Risk

Abstract

Infectious diseases still threaten global human health, and host genetic factors have been indicated as determining risk factors for observed variations in disease susceptibility, severity, and outcome. We performed a genome-wide meta-analysis on 4624 subjects from the 10,001 Dalmatians cohort, with 14 infection-related traits. Despite a rather small number of cases in some instances, we detected 29 infection-related genetic associations, mostly belonging to rare variants. Notably, the list included the genes CD28, INPP5D, ITPKB, MACROD2, and RSF1, all of which have known roles in the immune response. Expanding our knowledge on rare variants could contribute to the development of genetic panels that could assist in predicting an individual's life-long susceptibility to major infectious diseases. In addition, longitudinal biobanks are an interesting source of information for identifying the host genetic variants involved in infectious disease susceptibility and severity. Since infectious diseases continue to act as a selective pressure on our genomes, there is a constant need for a large consortium of biobanks with access to genetic and environmental data to further elucidate the complex mechanisms behind host-pathogen interactions and infectious disease susceptibility.

Keywords: genome-wide association study; hepatitis; infection; meningitis; pneumonia; rare variant; tuberculosis.

Figure 1

Pleiotropic network of identified loci (light grey oval shaped) with various infectious traits (dark grey octagon shaped) after a genome-wide meta-analysis of 4624 participants from the 10,001 Dalmatians biobank. Associations with various complex traits from other published GWA scans are shown in rounded rectangles, and KEGG pathway enrichment with an FDR cutoff of 1% for each of the infectious traits is depicted outside of the curved line (WHR—waist-to-hip ratio; BMI—body mass index; LDL—low-density lipoprotein; eGFR—estimated glomerular filtration rate).

Figure 2

Validation of GWAS candidate loci using differentially expressed genes (FDR < 0.05) from publicly available RNA-seq studies (GEO accession IDs: pneumonia GSE196399, tuberculosis GSE94438 and COVID-19 GSE223885).