Dataset of single nucleotide polymorphisms of immune-associated genes in patients with SARS-CoV-2 infection
1 January 2023·,,,,,,,,,,,,·
0 min read
Nikoletta Katsaouni
Pablo Llavona
Yascha Khodamoradi
Ann-Kathrin Otto
Stephanie Körber
Christof Geisen
Christian Seidl
Maria J. G. T. Vehreschild
Sandra Ciesek
Jörg Ackermann
Ina Koch
Marcel H. Schulz
Daniela S. Krause
Abstract
The SARS-CoV-2 pandemic has affected nations globally leading to illness, death, and economic downturn. Why disease severity, ranging from no symptoms to the requirement for extracorporeal membrane oxygenation, varies between patients is still incompletely understood. Consequently, we aimed at understanding the impact of genetic factors on disease severity in infection with SARS-CoV-2. Here, we provide data on demographics, ABO blood group, human leukocyte antigen (HLA) type, as well as next-generation sequencing data of genes in the natural killer cell receptor family, the renin-angiotensin-aldosterone and kallikrein-kinin systems and others in 159 patients with SARS-CoV-2 infection, stratified into seven categories of disease severity. We provide single-nucleotide polymorphism (SNP) data on the patients and a protein structural analysis as a case study on a SNP in the SIGLEC7 gene, which was significantly associated with the clinical score. Our data represent a resource for correlation analyses involving genetic factors and disease severity and may help predict outcomes in infections with future SARS-CoV-2 variants and aid vaccine adaptation.
Type
Publication
PloS One
Authors
Nikoletta Katsaouni
(she/her)
Postdoc
I am a bioinformatics researcher working at the intersection of AI, genomics, and personalised medicine. My research focuses on developing machine-learning approaches to integrate complex biomedical data and support patient-specific prediction, therapeutic decision-making, and therapy selection.
My broader goal is to bring AI closer to truly personalised medicine — because when it comes to biology, one size rarely fits all.