학술논문
Human Genome Polymorphisms and Computational Intelligence Approach Revealed a Complex Genomic Signature for COVID-19 Severity in Brazilian Patients
Document Type
article
Author
André Filipe Pastor; Cássia Docena; Antônio Mauro Rezende; Flávio Rosendo da Silva Oliveira; Marília de Albuquerque Sena; Clarice Neuenschwander Lins de Morais; Cristiane Campello Bresani-Salvi; Luydson Richardson Silva Vasconcelos; Kennya Danielle Campelo Valença; Carolline de Araújo Mariz; Carlos Brito; Cláudio Duarte Fonseca; Cynthia Braga; Christian Robson de Souza Reis; Ernesto Torres de Azevedo Marques; Bartolomeu Acioli-Santos
Source
Viruses, Vol 15, Iss 3, p 645 (2023)
Subject
Language
English
ISSN
1999-4915
Abstract
We present a genome polymorphisms/machine learning approach for severe COVID-19 prognosis. Ninety-six Brazilian severe COVID-19 patients and controls were genotyped for 296 innate immunity loci. Our model used a feature selection algorithm, namely recursive feature elimination coupled with a support vector machine, to find the optimal loci classification subset, followed by a support vector machine with the linear kernel (SVM-LK) to classify patients into the severe COVID-19 group. The best features that were selected by the SVM-RFE method included 12 SNPs in 12 genes: PD-L1, PD-L2, IL10RA, JAK2, STAT1, IFIT1, IFIH1, DC-SIGNR, IFNB1, IRAK4, IRF1, and IL10. During the COVID-19 prognosis step by SVM-LK, the metrics were: 85% accuracy, 80% sensitivity, and 90% specificity. In comparison, univariate analysis under the 12 selected SNPs showed some highlights for individual variant alleles that represented risk (PD-L1 and IFIT1) or protection (JAK2 and IFIH1). Variant genotypes carrying risk effects were represented by PD-L2 and IFIT1 genes. The proposed complex classification method can be used to identify individuals who are at a high risk of developing severe COVID-19 outcomes even in uninfected conditions, which is a disruptive concept in COVID-19 prognosis. Our results suggest that the genetic context is an important factor in the development of severe COVID-19.