Association Between the LZTFL1 rs11385942 Polymorphism and COVID-19 Severity in Colombian Population

Abstract

Genetic and non-genetic factors are responsible for the high interindividual variability in the response to SARS-CoV-2. Although numerous genetic polymorphisms have been identified as risk factors for severe COVID-19, these remain understudied in Latin-American populations. This study evaluated the association of non-genetic factors and three polymorphisms: ACE rs4646994, ACE2 rs2285666, and LZTFL1 rs11385942, with COVID severity and long-term symptoms by using a case-control design. The control group was composed of asymptomatic/mild cases (n = 61) recruited from a private laboratory, while the case group was composed of severe/critical patients (n = 63) hospitalized in the Hospital Universitario Mayor-Méderi, both institutions located in Bogotá, Colombia. Clinical follow up and exhaustive revision of medical records allowed us to assess non-genetic factors. Genotypification of the polymorphism of interest was performed by amplicon size analysis and Sanger sequencing. In agreement with previous reports, we found a statistically significant association between age, male sex, and comorbidities, such as hypertension and type 2 diabetes mellitus (T2DM), and worst outcomes. We identified the polymorphism LZTFL1 rs11385942 as an important risk factor for hospitalization (p < 0.01; OR = 5.73; 95% CI = 1.2-26.5, under the allelic test). Furthermore, long-term symptoms were common among the studied population and associated with disease severity. No association between the polymorphisms examined and long-term symptoms was found. Comparison of allelic frequencies with other populations revealed significant differences for the three polymorphisms investigated. Finally, we used the statistically significant genetic and non-genetic variables to develop a predictive logistic regression model, which was implemented in a Shiny web application. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC = 0.86; 95% confidence interval 0.79-0.93). These results suggest that LZTFL1 rs11385942 may be a potential biomarker for COVID-19 severity in addition to conventional non-genetic risk factors. A better understanding of the impact of these genetic risk factors may be useful to prioritize high-risk individuals and decrease the morbimortality caused by SARS-CoV2 and future pandemics.

Keywords: ACE; ACE2; COVID-19; LZTFL1; host genetics; infection severity.

Figure 1

Flowchart of the study participants.

Figure 2

Adjusted score distribution for cases and controls and ROC curve. (A) Box plot of the adjusted scores categorized by cases and controls. (B) Distribution and regression model for adjusted scores. Clinical outcome 0 corresponds to non-hospitalization and 1 to hospitalization. (C) Receiver operating characteristic (ROC) curve. (D) Score comparison clinical model vs. complete model. Dashed lines cutoff value ±0.05.