Journal article
Genome-wide machine learning analysis of anosmia and ageusia with COVID-19
PloS one, Vol.21(8), e0355832
08/01/2026
DOI: 10.1371/journal.pone.0355832
PMCID: PMC13472366
PMID: 42594091
Abstract
The COVID-19 pandemic has caused substantial worldwide disruptions in health, economy, and society, manifesting symptoms such as loss of smell (anosmia) and loss of taste (ageusia), that can result in prolonged sensory impairment. Establishing the host genetic etiology of anosmia and ageusia in COVID-19 will aid in the overall understanding of the sensorineural aspect of the disease and contribute to possible treatments or cures. By using human genome sequencing data from the University of Iowa (UI) COVID-19 cohort (N = 187) and the National Institute of Health All of Us (AoU) Research Program COVID-19 cohort (N = 947), we investigated the genetics of anosmia and/or ageusia by employing feature selection techniques to construct a novel variant and gene prioritization pipeline, utilizing machine learning methods for the classification of patients. Models were assessed using a permutation-based variable importance (PVI) strategy for final prioritization of candidate variants and genes. The highest held-out test set area under the receiver operating characteristic (AUROC) curve for models and datasets from the UI cohort was 0.735 and 0.798 for the variant and gene analysis respectively and for the AoU cohort was 0.687 for the variant analysis. Our analysis prioritized several novel and known candidate host genetic factors involved in immune response, neuronal signaling, and calcium signaling supporting previously proposed hypotheses for anosmia/ageusia in COVID-19.
Details
- Title: Subtitle
- Genome-wide machine learning analysis of anosmia and ageusia with COVID-19
- Creators
- Lucas Pietan - University of IowaElizabeth Phillippi - University of IowaMarcelo Melo - University of IowaHatem El-Shanti - University of IowaBrian J Smith - University of IowaBenjamin Darbro - University of IowaTerry Braun - University of IowaThomas Casavant - University of Iowa
- Resource Type
- Journal article
- Publication Details
- PloS one, Vol.21(8), e0355832
- DOI
- 10.1371/journal.pone.0355832
- PMID
- 42594091
- PMCID
- PMC13472366
- NLM abbreviation
- PLoS One
- ISSN
- 1932-6203
- eISSN
- 1932-6203
- Publisher
- PloS
- Grant note
- University of Iowa Stead Family Department of Pediatrics departmental funds University of Iowa Stead Family Department of Pediatrics start-up funds University of Iowa Interdisciplinary Genetics T32 Predoctoral Training Grant: T32 GM 008629 Integrated DNA Technologies Bioinformatics Fellowship Program
This research was funded by the University of Iowa Interdisciplinary Genetics T32 Predoctoral Training Grant, grant number T32 GM 008629 (LP), the Integrated DNA Technologies (https://www.idtdna.com) Bioinformatics Fellowship Program (LP) and the University of Iowa Stead Family Department of Pediatrics departmental funds (HE, BD). The University of Iowa genome sequencing was funded in part by the University of Iowa Stead Family Department of Pediatrics start-up funds for Hatem El-Shanti (HE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
- Language
- English
- Date published
- 08/01/2026
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Stead Family Department of Pediatrics; Biostatistics; Medical Genetics and Genomics; Holden Comprehensive Cancer Center
- Record Identifier
- 9985218543002771
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