Using GWAS SNPs to Determine Association between COVID-19 and Comorbid Diseases

Abstract

This paper analyzed the associations between COVID severity conditions and 7 comorbid diseases-Breast Cancer, Chronic Kidney Disease, Chronic Obstructive Pulmonary Disease, Coronary Artery Disease, Lung Cancer, Obesity, and Type 2 Diabetes. Genome-wide Association Study (GWAS) results were used to obtain Single-nucleotide Polymorphisms (SNP) for each disease. QQ plots were then used to compare the distribution of p-values for the GWAS SNPs with a uniform distribution to allow for the filtering out of insignificant SNPs. A co-association algorithm, based off of a fixed position window, was then applied to the filtered GWAS data to identify the degree of influence each of the 7 diseases have on the 3 COVID severities. A haplotype-block-based algorithm was then applied to identify specific genes that drives observed comorbidity patterns. Results showed that Chronic Kidney Diseases or Obesity are better predictors for COVID Infection, but not good predictors for Severe or Hospitalized COVID. It was also found that TRPC7, EXOC6, RNGTT, XPO7, PEX19, EDC4 and 7 more genes display recurring patterns of coassociation with multiple comorbid disease and clear inclination towards specific severity conditions.