Vitamin D-related polymorphisms and vitamin D levels as risk biomarkers of COVID-19 infection severity
Ana Teresa Freitas, Conceição Calhau, Gonçalo Antunes, Beatriz Araújo, Matilde Bandeira, Sofia Barreira, Filipa Bazenga, Sandra Braz, Daniel Caldeira, Susana Constantino Rosa Santos, Ana Faria, Daniel Faria, Marta Fraga, Beatriz Nogueira-Garcia, Lúcia Gonçalves, Pavlo Kovalchuk, Luísa Lacerda, Hugo Lopes, Daniel Luís, Fábio Medeiros, Ana M P Melo, José Melo-Cristino, Ana Miranda, Clara Pereira, Ana Teresa Pinto, João Pinto, Helena Proença, Angélica Ramos, João P. R. Rato, Filipe Rocha, Júlio César Rocha, André Moreira-Rosário, Helena Vazão, Yuliya Volovetska, João-Tiago Guimarães, Fausto Pinto
doi:10.1101/2021.03.22.21254032
Biotechnology SA, declare that the company developed a genetic test, the MyVitDGenes®, that was used to evaluate all the polymorphisms under analysis in this work. All other authors have declared that no conflict of interest exists.
Data analysis. Clinical history, genotypic and phenotypic data was evaluated using statistical, machine learning and polygenic risk scores methodologies. Vitamin D polymorphisms prevalence in different populations was obtained from 1000 Genomes database (https://www.internationalgenome.org/) and from HeartGenetics's research database for the Portuguese population, with more than 8,000 Portuguese individuals. Regarding the methodological approach, the following steps were undertaken: 1. Data cleaning and validation: All variables were analysed for outliers and missing values. Some discrepancies, such as different units of measure and data entry errors, were identified and fixed. No imputation was made. Regarding data transformation, both disease severity and vitamin D levels were categorized in different levels, and the genetic variants were aggregated in PRSs.
Descriptive analysis: A complete, graphical descriptive analysis of the data was created for all variables of interest as univariate analysis. Data are presented as numbers or percentages for categorical variables, while continuous variables are shown as mean and standard deviation, and median and interquartile range (25th percentile -75th percentile).
Analysis of data distribution: The data normality was accessed using Shapiro-Wilk test and D'Agostino Pearson's test. Statistical normality testing is relevant in order to set up the category of statistical methods (parametric or non-parametric) used in further..
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