A phenome-wide association study of polygenic scores for attention deficit hyperactivity disorder across two genetic ancestries in electronic health record data.

Testing the association between genetic scores for Attention Deficit Hyperactivity Disorder (ADHD) and health conditions, can help us better understand its complex etiology. Electronic health records linked to genetic data provide an opportunity to test whether genetic scores for ADHD correlate with ADHD and additional health outcomes in a health care context across different age groups. We generated polygenic scores (ADHD-PGS) trained on summary statistics from the latest genome-wide association study of ADHD (N = 55,374) and applied them to genome-wide data from 12,383 unrelated individuals of African-American ancestry and 66,378 unrelated individuals of European ancestry from the Vanderbilt Biobank. Overall, only Tobacco use disorder (TUD) was associated with ADHD-PGS in the African-American ancestry group (Odds ratio [95% confidence intervals] = 1.23[1.16-1.31], p = 9.3 × 10-09 ). Eighty-six phenotypes were associated with ADHD-PGS in the European ancestry individuals, including ADHD (OR[95%CIs] = 1.22[1.16-1.29], p = 3.6 × 10-10 ), and TUD (OR[95%CIs] = 1.22[1.19-1.25], p = 2.8 × 10-46 ). We then stratified outcomes by age (ages 0-11, 12-18, 19-25, 26-40, 41-60, and 61-100). Our results suggest that ADHD polygenic scores are associated with ADHD diagnoses early in life and with an increasing number of health conditions throughout the lifespan (even in the absence of ADHD diagnosis). This study reinforces the utility of applying trait-specific PGSs to biobank data, and performing exploratory sensitivity analyses, to probe relationships among clinical conditions.

Sex-specific genetic predictors of Alzheimer's disease biomarkers.

Cerebrospinal fluid (CSF) levels of amyloid-ß 42 (Aß42) and tau have been evaluated as endophenotypes in Alzheimer's disease (AD) genetic studies. Although there are sex differences in AD risk, sex differences have not been evaluated in genetic studies of AD endophenotypes. We performed sex-stratified and sex interaction genetic analyses of CSF biomarkers to identify sex-specific associations. Data came from a previous genome-wide association study (GWAS) of CSF Aß42 and tau (1527 males, 1509 females). We evaluated sex interactions at previous loci, performed sex-stratified GWAS to identify sex-specific associations, and evaluated sex interactions at sex-specific GWAS loci. We then evaluated sex-specific associations between prefrontal cortex (PFC) gene expression at relevant loci and autopsy measures of plaques and tangles using data from the Religious Orders Study and Rush Memory and Aging Project. In Aß42, we observed sex interactions at one previous and one novel locus: rs316341 within SERPINB1 (p = 0.04) and rs13115400 near LINC00290 (p = 0.002). These loci showed stronger associations among females (ß = - 0.03, p = 4.25 × 10-8; ß = 0.03, p = 3.97 × 10-8) than males (ß = - 0.02, p = 0.009; ß = 0.01, p = 0.20). Higher levels of expression of SERPINB1, SERPINB6, and SERPINB9 in PFC was associated with higher levels of amyloidosis among females (corrected p values < 0.02) but not males (p > 0.38). In total tau, we observed a sex interaction at a previous locus, rs1393060 proximal to GMNC (p = 0.004), driven by a stronger association among females (ß = 0.05, p = 4.57 × 10-10) compared to males (ß = 0.02, p = 0.03). There was also a sex-specific association between rs1393060 and tangle density at autopsy (pfemale = 0.047; pmale = 0.96), and higher levels of expression of two genes within this locus were associated with lower tangle density among females (OSTN p = 0.006; CLDN16 p = 0.002) but not males (p ≥ 0.32). Results suggest a female-specific role for SERPINB1 in amyloidosis and for OSTN and CLDN16 in tau pathology. Sex-specific genetic analyses may improve understanding of AD's genetic architecture.

Cross-EHR validation of antidepressant response algorithm and links with genetics of psychiatric traits.

Objective: Antidepressants are commonly prescribed medications in the United States, however, factors underlying response are poorly understood. Electronic health records (EHRs) provide a cost-effective way to create and test response algorithms on large, longitudinal cohorts. We describe a new antidepressant response algorithm, validation in two independent EHR databases, and genetic associations with antidepressant response. Method: We deployed the algorithm in EHRs at Vanderbilt University Medical Center (VUMC), the All of Us Research Program, and the Mass General Brigham Healthcare System (MGB) and validated response outcomes with patient health questionnaire (PHQ) scores. In a meta-analysis across all sites, worse antidepressant response associated with higher PHQ-8 scores (beta = 0.20, p-value = 1.09 × 10-18). Results: We used polygenic scores to investigate the relationship between genetic liability of psychiatric disorders and response to first antidepressant trial across VUMC and MGB. After controlling for depression diagnosis, higher polygenic scores for depression, schizophrenia, bipolar, and cross-disorders associated with poorer response to the first antidepressant trial (depression: p-value = 2.84 × 10-8, OR = 1.07; schizophrenia: p-value = 5.93 × 10-4, OR = 1.05; bipolar: p-value = 1.99 × 10-3, OR = 1.04; cross-disorders: p-value = 1.03 × 10-3, OR = 1.05). Conclusions: Overall, we demonstrate our antidepressant response algorithm can be deployed across multiple EHR systems to increase sample size of genetic and epidemiologic studies of antidepressant response.

Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci.

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.

A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner.

We deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4× mean depth) and deep whole exome (30-40× mean depth) data in a single sequencing run. This technology is cost-effective, empowers most genomic discoveries possible with deep whole genome sequencing, and provides an unbiased method to capture the diversity of common SNP variation across the globe. To evaluate this new technology at scale, we applied BGE to sequence >53,000 samples from the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which included participants across African, African American, and Latin American populations. We evaluated the accuracy of BGE imputed genotypes against raw genotype calls from the Illumina Global Screening Array. All PUMAS cohorts had R 2 concordance ≥95% among SNPs with MAF≥1%, and never fell below ≥90% R 2 for SNPs with MAF<1%. Furthermore, concordance rates among local ancestries within two recently admixed cohorts were consistent among SNPs with MAF≥1%, with only minor deviations in SNPs with MAF<1%. We also benchmarked the discovery capacity of BGE to access protein-coding copy number variants (CNVs) against deep whole genome data, finding that deletions and duplications spanning at least 3 exons had a positive predicted value of ~90%. Our results demonstrate BGE scalability and efficacy in capturing SNPs, indels, and CNVs in the human genome at 28% of the cost of deep whole-genome sequencing. BGE is poised to enhance access to genomic testing and empower genomic discoveries, particularly in underrepresented populations.

Anti-Inflammatory Action of Antidepressants: Investigating the Longitudinal Effect of Antidepressants on White Blood Cell Count.

Introduction: Antidepressants have documented anti-inflammatory effects on pro-inflammatory biomarkers. However, the long-term effects of antidepressants on inflammatory markers and the effects of different antidepressant classes on pro-inflammatory biomarkers are largely unexplored. Here, we evaluate the short- and long-term effects of all antidepressant classes on a clinical immune marker, white blood cell count (WBC). Methods: Using a retrospective study design, we extracted WBC count and prescription medications from electronic health records at Vanderbilt University Medical Center. We created a longitudinal model to evaluate the short- and long-term effects of these medications on WBC count. We validated our longitudinal model using two known anti-inflammatory medications, biologic immunosuppressants, and chemotherapy, and one medication class without known immunomodulatory properties, contraceptives. We used the longitudinal model to determine the effects of antidepressant use on WBC count stratified by drug class. Results: Biologic immunosuppressant and chemotherapy use was associated with decreased WBC count, but contraceptive use did not associate with changes in WBC count, validating our longitudinal modeling approach. All antidepressant classes were associated with decreased WBC count in the long-term cohorts. SSRI and atypical use also associated with decreased WBC count in the short-term cohort. Conclusions: Using electronic health record data, we show all antidepressant classes exhibit anti-inflammatory effects on a clinical immune marker, WBC count. Additionally, our results indicate that in some cases the anti-inflammatory effects of antidepressants persist over at least a 1-year time frame. Our work contributes to the immunomodulatory knowledge of antidepressants and motivates future studies investigating alternative therapeutic routes for antidepressants.

Meta-Analyses of Genome-Wide Association Studies for Postpartum Depression.

OBJECTIVE: Postpartum depression (PPD) is a common subtype of major depressive disorder (MDD) that is more heritable, yet is understudied in psychiatric genetics. The authors conducted meta-analyses of genome-wide association studies (GWASs) to investigate the genetic architecture of PPD. METHOD: Meta-analyses were conducted on 18 cohorts of European ancestry (17,339 PPD cases and 53,426 controls), one cohort of East Asian ancestry (975 cases and 3,780 controls), and one cohort of African ancestry (456 cases and 1,255 controls), totaling 18,770 PPD cases and 58,461 controls. Post-GWAS analyses included 1) single-nucleotide polymorphism (SNP)-based heritability ([Formula: see text]), 2) genetic correlations between PPD and other phenotypes, and 3) enrichment of the PPD GWAS findings in 27 human tissues and 265 cell types from the mouse central and peripheral nervous system. RESULTS: No SNP achieved genome-wide significance in the European or the trans-ancestry meta-analyses. The [Formula: see text] of PPD was 0.14 (SE=0.02). Significant genetic correlations were estimated for PPD with MDD, bipolar disorder, anxiety disorders, posttraumatic stress disorder, insomnia, age at menarche, and polycystic ovary syndrome. Cell-type enrichment analyses implicate inhibitory neurons in the thalamus and cholinergic neurons within septal nuclei of the hypothalamus, a pattern that differs from MDD. CONCLUSIONS: While more samples are needed to reach genome-wide levels of significance, the results presented confirm PPD as a polygenic and heritable phenotype. There is also evidence that despite a high correlation with MDD, PPD may have unique genetic components. Cell enrichment results suggest GABAergic neurons, which converge on a common mechanism with the only medication approved by the U.S. Food and Drug Administration for PPD (brexanolone).

Proposing a Sex-Adjusted Sodium-Adjusted MELD Score for Liver Transplant Allocation.

Importance: Liver allocation is determined by the model for end-stage liver disease (MELD), a scoring system based on 4 laboratory measurements. During the MELD era, sex disparities in liver transplant have increased and there are no modifications to MELD based on sex. Objective: To use laboratory values stored in electronic health records to describe population-level sex differences in all MELD laboratory values (in healthy individuals and patients with liver disease) and propose a sex adjustment. Design, Setting, and Participants: A retrospective cohort study was conducted from March 2019 to April 2020 to evaluate sex differences in laboratory values in liver transplant patients, patients with liver disease who did not undergo transplant, and healthy controls. Primary analyses were conducted in Vanderbilt University Medical Center (VUMC)'s deidentified electronic health record system. Replication analyses were conducted in the All of Us Research Program. Simulations of a sex-adjusted sodium-adjusted MELD (MELDNa) score were completed using liver transplant waiting list data from the liver simulated allocation modeling system. Patients who regularly used VUMC with measurements for any MELDNa component laboratory were included in the analyses. Analysis took place from November 2019 to March 2021. Exposures: Electronic health record-reported sex. Main Outcomes and Measure: Creatinine, bilirubin, international normalized ratio, and sodium levels. Results: The VUMC sample was composed of 623 931 individuals (359 976 [57.7%] female) with a median (IQR) age of 44 (23-61) years. All component MELDNa laboratory values and calculated MELDNa scores yielded significant sex differences within VUMC (mean [SD] creatinine: male, 0.99 [0.39] mg/dL; female, 0.79 [0.30] mg/dL; P < .001; bilirubin: male, 0.76 [0.83] mg/dL; female, 0.58 [0.64] mg/dL; P < .001; international normalized ratio of prothrombin rate: male, 1.24 [0.42]; female, 1.20 [0.40]; P < .001; sodium: male, 139.00 [2.36] mEq/L; female, 139.03 [2.28] mEq/L; P < .001), resulting in MELDNa scoring that disadvantaged female individuals. This pattern persisted when the sample was divided into healthy controls, individuals with liver disease who did not undergo transplant, and patients who did undergo liver transplant. Female transplant patients had a greater number of decompensation traits (mean [SD]: male, 1.34 [1.11]; female, 1.60 [1.09]; P = .005), despite having lower MELDNa scores (mean [SD]: male, 21.72 [6.11]; female, 20.21 [6.15]; P = .005), indicating MELDNa scores are not accurately representing disease severity in female individuals. In simulations, the sex-adjusted MELDNa score modestly increased female transplant rate and decreased overall death. Conclusions and Relevance: These results demonstrate pervasive sex differences in all laboratory values used in MELDNa scoring and highlight the need and utility of a sex-adjustment to the MELDNa protocol.

Pancreatic adenocarcinoma in liver transplant recipients: a case series.

BACKGROUND: Malignancy is one of the known leading causes of death among long-term liver transplantation (LT) survivors. Pancreatic cancer has an incidence of 7.6/100,000 in North America and constitutes a diagnostic challenge post-LT. METHODS: This is a single-center, retrospective review of the electronic health records (EHRs) of LT recipients with pancreatic adenocarcinoma (1990-2019). The prevalence of pancreatic adenocarcinoma in our institutional non-LT population was assessed using an institutional de-identified database (Synthetic Derivative). RESULTS: Six out of 2,232 (0.27%) LT recipients were diagnosed with pancreatic adenocarcinoma. Median age at diagnosis was 66.0 years (IQR, 57.8-71.8 years). Median time from LT to pancreatic adenocarcinoma diagnosis was 8.9 years (IQR, 4.7-16.2 years), the median size on imaging was 3.2 cm (IQR, 3.1-4.7 cm), and all tumors were located on the head of the pancreas. Three patients underwent surgical resection (one with adjuvant chemotherapy), two underwent palliative care, and one palliative chemotherapy with gemcitabine and abraxane. Over a median follow-up of 220.5 days (IQR, 144.8-399.5 days), all six patients died due to disease progression (100%). Pancreatic adenocarcinoma was diagnosed in 5,033 out of 2,484,772 (0.20%) individuals in the Synthetic Derivative. CONCLUSIONS: Our findings identified an increased incidence of pancreatic adenocarcinoma following LT compared to the general population.

Elevated Polygenic Burden for Autism Spectrum Disorder Is Associated With the Broad Autism Phenotype in Mothers of Individuals With Autism Spectrum Disorder.

BACKGROUND: Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder that encompasses a complex and heterogeneous set of traits. Subclinical traits that mirror the core features of ASD, referred to as the broad autism phenotype (BAP), have been documented repeatedly in unaffected relatives and are believed to reflect underlying genetic liability to ASD. The BAP may help inform the etiology of ASD by allowing the stratification of families into more phenotypically and etiologically homogeneous subgroups. This study explores polygenic scores related to the BAP. METHODS: Phenotypic and genotypic information were obtained from 2614 trios from the Simons Simplex Collection. Polygenic scores of ASD (ASD-PGSs) were generated across the sample to determine the shared genetic overlap between the BAP and ASD. Maternal and paternal ASD-PGSs were explored in relation to BAP traits and their child's ASD symptomatology. RESULTS: Maternal pragmatic language was related to child's social communicative atypicalities. In fathers, rigid personality was related to increased repetitive behaviors in children. Maternal (but not paternal) ASD-PGSs were related to the pragmatic language and rigid BAP domains. CONCLUSIONS: Associations emerged between parent and child phenotypes, with more associations emerging in mothers than in fathers. ASD-PGS associations emerged with BAP in mothers only, highlighting the potential for a female protective factor, and implicating the polygenic etiology of ASD-related phenotypes in the BAP.

Use of the PsycheMERGE Network to Investigate the Association Between Depression Polygenic Scores and White Blood Cell Count.

Importance: Although depression is a common psychiatric disorder, its underlying biological basis remains poorly understood. Pairing depression polygenic scores with the results of clinical laboratory tests can reveal biological processes involved in depression etiology and in the physiological changes resulting from depression. Objective: To characterize the association between depression polygenic scores and an inflammatory biomarker, ie, white blood cell count. Design, Setting, and Participants: This genetic association study was conducted from May 19, 2019, to June 5, 2021, using electronic health record data from 382 452 patients across 4 health care systems. Analyses were conducted separately in each health care system and meta-analyzed across all systems. Primary analyses were conducted in Vanderbilt University Medical Center's biobank. Replication analyses were conducted across 3 other PsycheMERGE sites: Icahn School of Medicine at Mount Sinai, Mass General Brigham, and the Million Veteran Program. All patients with available genetic data and recorded white blood cell count measurements were included in the analyses. Primary analyses were conducted in individuals of European descent and then repeated in a population of individuals of African descent. Exposures: Depression polygenic scores. Main Outcomes and Measures: White blood cell count. Results: Across the 4 PsycheMERGE sites, there were 382 452 total participants of European ancestry (18.7% female; median age, 57.9 years) and 12 383 participants of African ancestry (61.1% female; median age, 39.0 [range, birth-90.0 years]). A laboratory-wide association scan revealed a robust association between depression polygenic scores and white blood cell count (ß, 0.03; SE, 0.004; P = 1.07 × 10-17), which was replicated in a meta-analysis across the 4 health care systems (ß, 0.03; SE, 0.002; P = 1.03 × 10-136). Mediation analyses suggested a bidirectional association, with white blood cell count accounting for 2.5% of the association of depression polygenic score with depression diagnosis (95% CI, 2.2%-20.8%; P = 2.84 × 10-70) and depression diagnosis accounting for 9.8% of the association of depression polygenic score with white blood cell count (95% CI, 8.4%-11.1%; P = 1.78 × 10-44). Mendelian randomization provided additional support for an association between increased white blood count and depression risk, but depression modeled as the exposure showed no evidence of an influence on white blood cell counts. Conclusions and Relevance: This genetic association study found that increased depression polygenic scores were associated with increased white blood cell count, and suggests that this association may be bidirectional. These findings highlight the potential importance of the immune system in the etiology of depression and may motivate future development of clinical biomarkers and targeted treatment options for depression.

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.

A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.

Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.

Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits.

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU, combining alcohol use disorder and problematic drinking, in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n = 67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in conserved and regulatory genomic regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other traits.