Dataset Information

A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns.

ABSTRACT:

Background

Post COVID-19 condition (PCC) is known to affect a large proportion of COVID-19 survivors. Robust study design and methods are needed to understand post-COVID-19 diagnosis patterns in all survivors, not just those clinically diagnosed with PCC.

Methods

We applied a case-crossover Phenome-Wide Association Study (PheWAS) in a retrospective cohort of COVID-19 survivors, comparing the occurrences of 1,671 diagnosis-based phenotype codes (PheCodes) pre- and post-COVID-19 infection periods in the same individual using a conditional logistic regression. We studied how this pattern varied by COVID-19 severity and vaccination status, and we compared to test negative and test negative but flu positive controls.

Results

In 44,198 SARS-CoV-2-positive patients, we foundenrichment in respiratory,circulatory, and mental health disorders post-COVID-19-infection. Top hits included anxiety disorder (p = 2.8e-109, OR = 1.7 [95 % CI: 1.6-1.8]), cardiac dysrhythmias (p = 4.9e-87, OR = 1.7 [95 % CI: 1.6-1.8]), and respiratory failure, insufficiency, arrest (p = 5.2e-75, OR = 2.9 [95 % CI: 2.6-3.3]). In severe patients, we found stronger associations with respiratory and circulatory disorders compared to mild/moderate patients. Fully vaccinated patients had mental health and chronic circulatory diseases rise to the top of the association list, similar to the mild/moderate cohort. Both control groups (test negative, test negative and flu positive) showed a different pattern of hits to SARS-CoV-2 positives.

Conclusions

Patients experience myriad symptoms more than 28 days after SARS-CoV-2 infection, but especially respiratory, circulatory, and mental health disorders. Our case-crossover PheWAS approach controls for within-person confounders that are time-invariant. Comparison to test negatives and test negative but flu positive patients with a similar design helped identify enrichment specific to COVID-19. This design may be applied other emerging diseases with long-lasting effects other than a SARS-CoV-2 infection. Given the potential for bias from observational data, these results should be considered exploratory. As we look into the future, we must be aware of COVID-19 survivors' healthcare needs.

SUBMITTER: Haupert SR

PROVIDER: S-EPMC9595430 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns.

Haupert Spencer R SR Shi Xu X Chen Chen C Fritsche Lars G LG Mukherjee Bhramar B

Journal of biomedical informatics 20221023

<h4>Background</h4>Post COVID-19 condition (PCC) is known to affect a large proportion of COVID-19 survivors. Robust study design and methods are needed to understand post-COVID-19 diagnosis patterns in all survivors, not just those clinically diagnosed with PCC.<h4>Methods</h4>We applied a case-crossover Phenome-Wide Association Study (PheWAS) in a retrospective cohort of COVID-19 survivors, comparing the occurrences of 1,671 diagnosis-based phenotype codes (PheCodes) pre- and post-COVID-19 inf ...[more]

PMID: 36283580

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Project description:BackgroundWe explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy.ResultsIn this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 "Disorder of Lipoid metabolism" (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 "Acquired Hypothyroidism" (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 "Complications peculiar to certain specified procedures" (pdiscovery = 8.65x103, preplicating = 4.16x10-3).ConclusionIn conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.

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Dataset Information

A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns.

Background

Methods

Results

Conclusions

Publications

A Case-Crossover Phenome-wide association study (PheWAS) for understanding Post-COVID-19 diagnosis patterns.

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