Canadian COVID-19 host genetics cohort replicates known severity associations.

The HostSeq initiative recruited 10,059 Canadians infected with SARS-CoV-2 between March 2020 and March 2023, obtained clinical information on their disease experience and whole genome sequenced (WGS) their DNA. We analyzed the WGS data for genetic contributors to severe COVID-19 (considering 3,499 hospitalized cases and 4,975 non-hospitalized after quality control). We investigated the evidence for replication of loci reported by the International Host Genetics Initiative (HGI); analyzed the X chromosome; conducted rare variant gene-based analysis and polygenic risk score testing. Population stratification was adjusted for using meta-analysis across ancestry groups. We replicated two loci identified by the HGI for COVID-19 severity: the LZTFL1/SLC6A20 locus on chromosome 3 and the FOXP4 locus on chromosome 6 (the latter with a variant significant at P < 5E-8). We found novel significant associations with MRAS and WDR89 in gene-based analyses, and constructed a polygenic risk score that explained 1.01% of the variance in severe COVID-19. This study provides independent evidence confirming the robustness of previously identified COVID-19 severity loci by the HGI and identifies novel genes for further investigation.

Association of Preinjury Medical Diagnoses With Pediatric Persistent Postconcussion Symptoms in Electronic Health Records.

OBJECTIVE: To identify risk factors and generate hypotheses for pediatric persistent postconcussion symptoms (PPCS). SETTING: A regional healthcare system in the Southeastern United States. PARTICIPANTS: An electronic health record-based algorithm was developed and validated to identify PPCS cases and controls from an institutional database of more than 2.8 million patients. PPCS cases (n = 274) were patients aged 5 to 18 years with PPCS-related diagnostic codes or with PPCS key words identified by natural language processing of clinical notes. Age, sex, and year of index event-matched controls (n = 1096) were patients with mild traumatic brain injury codes only. Patients with moderate or severe traumatic brain injury were excluded. All patients used our healthcare system at least 3 times 180 days before their injury. DESIGN: Case-control study. MAIN MEASURES: The outcome was algorithmic classification of PPCS. Exposures were all preinjury medical diagnoses assigned at least 180 days before the injury. RESULTS: Cases and controls both had a mean of more than 9 years of healthcare system use preinjury. Of 221 preinjury medical diagnoses, headache disorder was associated with PPCS after accounting for multiple testing (odds ratio [OR] = 2.9; 95% confidence interval [CI]: 1.6-5.0; P = 2.1e-4). Six diagnoses were associated with PPCS at a suggestive threshold for statistical significance (false discovery rate P < .10): gastritis/duodenitis (OR = 2.8; 95% CI: 1.6-5.1; P = 5.0e-4), sleep disorders (OR = 2.3; 95% CI: 1.4-3.7; P = 7.4e-4), abdominal pain (OR = 1.6; 95% CI: 1.2-2.2; P = 9.2e-4), chronic sinusitis (OR = 2.8; 95% CI: 1.5-5.2; P = 1.3e-3), congenital anomalies of the skin (OR = 2.9; 95% CI: 1.5-5.5; P = 1.9e-3), and chronic pharyngitis/nasopharyngitis (OR = 2.4; 95% CI: 1.4-4.3; P = 2.5e-3). CONCLUSIONS: These results support the strong association of preinjury headache disorders with PPCS. An association of PPCS with prior gastritis/duodenitis, sinusitis, and pharyngitis/nasopharyngitis suggests a role for chronic inflammation in PPCS pathophysiology and risk, although results could equally be attributable to a higher likelihood of somatization among PPCS cases. Identified risk factors should be investigated further and potentially considered during the management of pediatric mild traumatic brain injury cases.

Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions.

Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.

Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease.

BACKGROUND: Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations. METHODS: A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center's (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank. RESULTS: Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB. CONCLUSIONS: Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.