PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations

Large-scale real-world data analyses of cancer risks among patients with rheumatoid arthritis

Rheumatoid arthritis (RA) affects 24.5 million people worldwide and has been associated with increased cancer risks. However, the extent to which the observed risks are related to the pathophysiology of rheumatoid arthritis or its treatments is unknown. Leveraging nationwide health insurance claims data with 85.97 million enrollees across 8 years, we identified 92 864 patients without cancers at the time of rheumatoid arthritis diagnoses. We matched 68 415 of these patients with participants without rheumatoid arthritis by sex, race, age and inferred health and economic status and compared their risks of developing all cancer types. By 12 months after the diagnosis of rheumatoid arthritis, rheumatoid arthritis patients were 1.21 (95% confidence interval [CI] [1.14, 1.29]) times more likely to develop any cancer compared with matched enrollees without rheumatoid arthritis. In particular, the risk of developing lymphoma is 2.08 (95% CI [1.67, 2.58]) times higher in the rheumatoid arthritis group, and the risk of developing lung cancer is 1.69 (95% CI [1.32, 2.13]) times higher. We further identified the five most commonly used drugs in treating rheumatoid arthritis, and the log-rank test showed none of them is implicated with a significantly increased cancer risk compared with rheumatoid arthritis patients without that specific drug. Our study suggested that the pathophysiology of rheumatoid arthritis, rather than its treatments, is implicated in the development of subsequent cancers. Our method is extensible to investigating the connections among drugs, diseases and comorbidities at scale.

MultiGML: Multimodal graph machine learning for prediction of adverse drug events

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.

Clinical consequences of a polygenic predisposition to benign lower white blood cell counts: Consequences of benign WBC count genetics

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is undefined. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio=0.55 per standard deviation increase in PGSWBC [95%CI, 0.30 - 0.94], p=0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n=1,724, hazard ratio [HR]=0.78 [0.69 - 0.88], p=4.0×10-5) or immunosuppressant (n=354, HR=0.61 [0.38 - 0.99], p=0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n=1,466, HR=0.62 [0.44 - 0.87], p=0.006). Collectively, these findings suggest that a WBC count polygenic score identifies individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Phenomics as an approach to Comparative Developmental Physiology

The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.

Clinical diagnoses associated with a positive antinuclear antibody test in patients with and without autoimmune disease

BACKGROUND

Antinuclear antibodies (ANA) are antibodies present in several autoimmune disorders. However, a large proportion of the general population (20%) also have a positive test; very few of these individuals will develop an autoimmune disease, and the clinical impact of a positive ANA in them is not known. Thus, we test the hypothesis that ANA + test reflects a state of immune dysregulation that alters risk for some clinical disorders in individuals without an autoimmune disease.

METHODS

We performed high throughput association analyses in a case-control study using real world data from the de-identified electronic health record (EHR) system from Vanderbilt University Medical Center. The study population included individuals with an ANA titer ≥ 1:80 at any time (ANA +) and those with negative results (ANA-). The cohort was stratified into sub-cohorts of individuals with and without an autoimmune disease. A phenome-wide association study (PheWAS) adjusted by sex, year of birth, race, and length of follow-up was performed in the study cohort and in the sub-cohorts. As secondary analyses, only clinical diagnoses after ANA testing were included in the analyses.

RESULTS

The cohort included 70,043 individuals: 49,546 without and 20,497 with an autoimmune disease, 26,579 were ANA + and 43,464 ANA-. In the study cohort and the sub-cohort with autoimmune disease, ANA + was associated (P ≤ 5 × 10-5) with 88 and 136 clinical diagnoses respectively, including lupus (OR ≥ 5.4, P ≤ 7.8 × 10-202) and other autoimmune diseases and complications. In the sub-cohort without autoimmune diseases, ANA + was associated with increased risk of Raynaud's syndrome (OR ≥ 2.1) and alveolar/perialveolar-related pneumopathies (OR ≥ 1.4) and decreased risk of hepatitis C, tobacco use disorders, mood disorders, convulsions, fever of unknown origin, and substance abuse disorders (OR ≤ 0.8). Analyses including only diagnoses after ANA testing yielded similar results.

CONCLUSION

A positive ANA test, in addition to known associations with autoimmune diseases, Raynaud's phenomenon, and idiopathic fibrosing alveolitis related disorders, is associated with decreased prevalence of several non-autoimmune diseases.

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records

INTRODUCTION

This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs).

METHODS

A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested.

RESULTS

The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified.

DISCUSSION

We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.

PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis

Motivation

Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations.

Results

PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia.

Availability and implementation

The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME.

Data availability statement

The data underlying this article are available in the article and in its online web application or supplementary material.

The genetic and phenotypic correlates of mtDNA copy number in a multi-ancestry cohort

Mitochondrial DNA copy number (mtCN) is often treated as a proxy for mitochondrial (dys-) function and disease risk. Pathological changes in mtCN are common symptoms of rare mitochondrial disorders, but reported associations between mtCN and common diseases vary across studies. To understand the biology of mtCN, we carried out genome- and phenome-wide association studies of mtCN in 30,666 individuals from the Penn Medicine BioBank (PMBB)-a diverse cohort of largely African and European ancestry. We estimated mtCN in peripheral blood using exome sequence data, taking cell composition into account. We replicated known genetic associations of mtCN in the PMBB and found that their effects are highly correlated between individuals of European and African ancestry. However, the heritability of mtCN was much higher among individuals of largely African ancestry ( h 2 = 0.3 ) compared with European ancestry individuals( h 2 = 0.1 ) . Admixture mapping suggests that there are undiscovered variants underlying mtCN that are differentiated in frequency between individuals with African and European ancestry. We show that mtCN is associated with many health-related phenotypes. We discovered robust associations between mtDNA copy number and diseases of metabolically active tissues, such as cardiovascular disease and liver damage, that were consistent across African and European ancestry individuals. Other associations, such as epilepsy and prostate cancer, were only discovered in either individuals with European or African ancestry but not both. We show that mtCN-phenotype associations can be sensitive to blood cell composition and environmental modifiers, explaining why such associations are inconsistent across studies. Thus, mtCN-phenotype associations must be interpreted with care.

Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort

Background

Many psychiatric outcomes are thought to share a common etiological pathway reflecting behavioral disinhibition, generally referred to as externalizing disorders (EXT). Recent genome-wide association studies (GWAS) have demonstrated the overlap between EXT and important aspects of veterans' health, such as suicide-related behaviors, substance use disorders, and other medical conditions.

Methods

We conducted a series of phenome-wide association studies (PheWAS) of polygenic scores (PGS) for EXT, and comorbid psychiatric problems (depression, schizophrenia, and suicide attempt) in an ancestrally diverse cohort of U.S. veterans (N = 560,824), using diagnostic codes from electronic health records. We conducted ancestry-specific PheWASs of EXT PGS in the European, African, and Hispanic/Latin American ancestries. To determine if associations were driven by risk for other comorbid problems, we performed a conditional PheWAS, covarying for comorbid psychiatric problems (European ancestries only). Lastly, to adjust for unmeasured confounders we performed a within-family analysis of significant associations from the main PheWAS in full-siblings (N = 12,127, European ancestries only).

Results

The EXT PGS was associated with 619 outcomes across all bodily systems, of which, 188 were independent of risk for comorbid problems of PGS. Effect sizes ranged from OR = 1.02 (95% CI = 1.01, 1.03) for overweight/obesity to OR = 1.44 (95% CI = 1.42, 1.47) for viral hepatitis C. Of the significant outcomes 73 (11.9%) and 26 (4.5%) were significant in the African and Hispanic/Latin American results, respectively. Within-family analyses uncovered robust associations between EXT and consequences of substance use disorders, including liver disease, chronic airway obstruction, and viral hepatitis C.

Conclusion

Our results demonstrate a shared polygenic basis of EXT across populations of diverse ancestries and independent of risk for other psychiatric problems. The strongest associations with EXT were for diagnoses related to substance use disorders and their sequelae. Overall, we highlight the potential negative consequences of EXT for health and functioning in the US veteran population.

Health conditions in autism: Defining the trajectory from adolescence to early adulthood

Autistic adults, as compared to non-autistic adults, have increased rates of nearly all medical and psychiatric conditions. Many of these conditions begin in childhood, although few longitudinal studies have been conducted to examine prevalence rates of these conditions from adolescence into early adulthood. In this study, we analyze the longitudinal trajectory of health conditions in autistic youth, compared to age and sex-matched non-autistic youth, transitioning from adolescence into early adulthood in a large integrated health care delivery system. The percent and modeled prevalence of common medical and psychiatric conditions increased from age 14 to 22 years, with autistic youth having a higher prevalence of most conditions than non-autistic youth. The most prevalent conditions in autistic youth at all ages were obesity, neurological disorders, anxiety, and ADHD. The prevalence of obesity and dyslipidemia rose at a faster rate in autistic youth compared to non-autistic youth. By age 22, autistic females showed a higher prevalence of all medical and psychiatric conditions compared to autistic males. Our findings emphasize the importance of screening for medical and psychiatric conditions in autistic youth, coupled with health education targeted at this population, to mitigate the development of adverse health outcomes in autistic adults.

Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy

IgA nephropathy (IgAN) is a progressive form of kidney disease defined by glomerular deposition of IgA. Here we performed a genome-wide association study of 10,146 kidney-biopsy-diagnosed IgAN cases and 28,751 controls across 17 international cohorts. We defined 30 genome-wide significant risk loci explaining 11% of disease risk. A total of 16 loci were new, including TNFSF4/TNFSF18, REL, CD28, PF4V1, LY86, LYN, ANXA3, TNFSF8/TNFSF15, REEP3, ZMIZ1, OVOL1/RELA, ETS1, IGH, IRF8, TNFRSF13B and FCAR. The risk loci were enriched in gene orthologs causing abnormal IgA levels when genetically manipulated in mice. We also observed a positive genetic correlation between IgAN and serum IgA levels. High polygenic score for IgAN was associated with earlier onset of kidney failure. In a comprehensive functional annotation analysis of candidate causal genes, we observed convergence of biological candidates on a common set of inflammatory signaling pathways and cytokine ligand-receptor pairs, prioritizing potential new drug targets.

Genome-wide association study of thoracic aortic aneurysm and dissection in the Million Veteran Program

The current understanding of the genetic determinants of thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies of rare, Mendelian forms of disease. Here, we conducted a genome-wide association study (GWAS) of TAAD, testing ~25 million DNA sequence variants in 8,626 participants with and 453,043 participants without TAAD in the Million Veteran Program, with replication in an independent sample of 4,459 individuals with and 512,463 without TAAD from six cohorts. We identified 21 TAAD risk loci, 17 of which have not been previously reported. We leverage multiple downstream analytic methods to identify causal TAAD risk genes and cell types and provide human genetic evidence that TAAD is a non-atherosclerotic aortic disorder distinct from other forms of vascular disease. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and that it is not solely inherited through protein-altering variants of large effect size.

Comorbidity genetic risk and pathways impact SARS-CoV-2 infection outcomes

Understanding the genetic risk and mechanisms through which SARS-CoV-2 infection outcomes and comorbidities interact to impact acute and long-term sequelae is essential if we are to reduce the ongoing health burdens of the COVID-19 pandemic. Here we use a de novo protein diffusion network analysis coupled with tissue-specific gene regulatory networks, to examine putative mechanisms for associations between SARS-CoV-2 infection outcomes and comorbidities. Our approach identifies a shared genetic aetiology and molecular mechanisms for known and previously unknown comorbidities of SARS-CoV-2 infection outcomes. Additionally, genomic variants, genes and biological pathways that provide putative causal mechanisms connecting inherited risk factors for SARS-CoV-2 infection and coronary artery disease and Parkinson's disease are identified for the first time. Our findings provide an in depth understanding of genetic impacts on traits that collectively alter an individual's predisposition to acute and post-acute SARS-CoV-2 infection outcomes. The existence of complex inter-relationships between the comorbidities we identify raises the possibility of a much greater post-acute burden arising from SARS-CoV-2 infection if this genetic predisposition is realised.

Ayurgenomics-based frameworks in precision and integrative medicine: Translational opportunities

In today's globalized and flat world, a patient can access and seek multiple health and disease management options. A digitally enabled participatory framework that allows an evidence-based informed choice is likely to assume an immense importance in the future. In India, traditional knowledge systems, like Ayurveda, coexist with modern medicine. However, due to limited crosstalk between the clinicians of both disciplines, a patient attempts integrative medicine by seeking both options independently with limited understanding and evidence. There is a need for an integrative medicine platform with a formalized approach, which allows practitioners from the two diverse systems to crosstalk, coexist, and coevolve for an informed cross-referral that benefits the patients. To be successful, this needs frameworks that enable the bridging of disciplines through a common interface with shared ontologies. Ayurgenomics is an emerging discipline that explores the principles and practices of Ayurveda combined with genomics approaches for mainstream integration. The present review highlights how in conjunction with different disciplines and technologies this has provided frameworks for (1) the discovery of molecular correlates to build ontological links between the two systems, (2) the discovery of biomarkers and targets for early actionable interventions, (3) understanding molecular mechanisms of drug action from its usage perspective in Ayurveda with applications in repurposing, (4) understanding the network and P4 medicine perspective of Ayurveda through a common organizing principle, (5) non-invasive stratification of healthy and diseased individuals using a compendium of system-level phenotypes, and (6) developing evidence-based solutions for practice in integrative medicine settings. The concordance between the two contrasting streams has been built through extensive explorations and iterations of the concepts of Ayurveda and genomic observations using state-of-the-art technologies, computational approaches, and model system studies. These highlight the enormous potential of a trans-disciplinary approach in evolving solutions for personalized interventions in integrative medicine settings.

The broad impact of cell death genes on the human disease phenome

Apoptotic, necroptotic, and pyroptotic cell death pathways are attractive and druggable targets for many human diseases, however the tissue specificity of these pathways and the relationship between these pathways and human disease is poorly characterized. Understanding the impact of modulating cell death gene expression on the human phenome could inform clinical investigation of cell death pathway-modulating therapeutics in human disorders by identifying novel trait associations and by detecting tissue-specific side effect profiles. We analyzed the expression profiles of an array of 44 cell death genes across somatic tissues in GTEx v8 and investigated the relationship between tissue-specific genetically determined expression of 44 cell death genes and the human phenome using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank V3 (n ~500,000). We evaluated 513 traits encompassing ICD-10 defined diagnoses and hematologic traits (blood count labs). Our analysis revealed hundreds of significant (FDR<0.05) associations between cell death gene expression and diverse human phenotypes, which were independently validated in another large-scale biobank. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits and necroptosis gene associations enriched for erythroid traits (e.g., Reticulocyte count, FDR=0.004). This suggests that immunogenic cell death pathways play an important role in regulating erythropoiesis and reinforces the paradigm that apoptosis pathway genes are critical for white blood cell and platelet development. Of functionally analogous genes, for instance pro-survival BCL2 family members, trait/direction-of-effect relationships were heterogeneous across blood traits. Overall, these results suggest that even functionally similar and/or orthologous cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.

Whole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles

Genetic studies have identified numerous regions associated with plasma fibrinogen levels in Europeans, yet missing heritability and limited inclusion of non-Europeans necessitates further studies with improved power and sensitivity. Compared with array-based genotyping, whole genome sequencing (WGS) data provides better coverage of the genome and better representation of non-European variants. To better understand the genetic landscape regulating plasma fibrinogen levels, we meta-analyzed WGS data from the NHLBI's Trans-Omics for Precision Medicine (TOPMed) program (n=32,572), with array-based genotype data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (n=131,340) imputed to the TOPMed or Haplotype Reference Consortium panel. We identified 18 loci that have not been identified in prior genetic studies of fibrinogen. Of these, four are driven by common variants of small effect with reported MAF at least 10% higher in African populations. Three ( SERPINA1, ZFP36L2 , and TLR10) signals contain predicted deleterious missense variants. Two loci, SOCS3 and HPN , each harbor two conditionally distinct, non-coding variants. The gene region encoding the protein chain subunits ( FGG;FGB;FGA ), contains 7 distinct signals, including one novel signal driven by rs28577061, a variant common (MAF=0.180) in African reference panels but extremely rare (MAF=0.008) in Europeans. Through phenome-wide association studies in the VA Million Veteran Program, we found associations between fibrinogen polygenic risk scores and thrombotic and inflammatory disease phenotypes, including an association with gout. Our findings demonstrate the utility of WGS to augment genetic discovery in diverse populations and offer new insights for putative mechanisms of fibrinogen regulation.

Key Points

Largest and most diverse genetic study of plasma fibrinogen identifies 54 regions (18 novel), housing 69 conditionally distinct variants (20 novel).Sufficient power achieved to identify signal driven by African population variant.Links to (1) liver enzyme, blood cell and lipid genetic signals, (2) liver regulatory elements, and (3) thrombotic and inflammatory disease.

Polygenic prediction of preeclampsia and gestational hypertension

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Insights into human health from phenome- and genome-wide analyses of UK Biobank retinal optical coherence tomography phenotypes

The human retina is a complex multi-layered tissue which offers a unique window into systemic health and disease. Optical coherence tomography (OCT) is widely used in eye care and allows the non-invasive, rapid capture of retinal measurements in exquisite detail. We conducted genome- and phenome-wide analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed phenome-wide association analyses, associating retinal thicknesses with 1,866 incident ICD-based conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association analyses, identifying inherited genetic markers which influence the retina, and replicated our associations among 6,313 individuals from the LIFE-Adult Study. And lastly, we performed comparative association of phenome- and genome- wide associations to identify putative causal links between systemic conditions, retinal layer thicknesses, and ocular disease. Independent associations with incident mortality were detected for photoreceptor thinning and ganglion cell complex thinning. Significant phenotypic associations were detected between retinal layer thinning and ocular, neuropsychiatric, cardiometabolic and pulmonary conditions. Genome-wide association of retinal layer thicknesses yielded 259 loci. Consistency between epidemiologic and genetic associations suggested putative causal links between thinning of the retinal nerve fiber layer with glaucoma, photoreceptor segment with AMD, as well as poor cardiometabolic and pulmonary function with PS thinning, among other findings. In conclusion, retinal layer thinning predicts risk of future ocular and systemic disease. Furthermore, systemic cardio-metabolic-pulmonary conditions promote retinal thinning. Retinal imaging biomarkers, integrated into electronic health records, may inform risk prediction and potential therapeutic strategies.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

Polygenic risk affects the penetrance of monogenic kidney disease

Background

Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. Most forms of monogenic kidney diseases have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors.

Methods

Using the UK Biobank (N=469,835 participants) and the All of Us (N=98,622 participants) datasets, we examined two most common forms of monogenic kidney disorders, autosomal dominant polycystic kidney disease (ADPKD) caused by deleterious variants in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN caused by deleterious variants in COL4A3, COL4A4, or COL4A5 genes). We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 or kidney failure) and controls (eGFR >90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The effects of the genome-wide polygenic score (GPS) for CKD were tested in monogenic variant carriers and non-carriers using logistic regression controlling for age, sex, diabetes, and genetic ancestry.

Results

As expected, the carriers of known pathogenic and rare predicted loss-of-function variants in PKD1 or PKD2 had a high risk of CKD (ORmeta=17.1, 95% CI: 11.1-26.4, P=1.8E-37). The GPS was comparably predictive of CKD in both ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55-3.37, P=2.6E-05) and non-carriers (ORmeta=1.72 per SD, 95% CI=1.69-1.76, P< E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN variant carriers (ORmeta=1.78, 95% CI=1.22-2.58, P=2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68-1.73 P

Conclusions

Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by differences in polygenic risk profiles. Accounting for polygenic factors has the potential to improve risk stratification in monogenic kidney disease and may have implications for genetic counseling.

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Key Words

• APOL1
• GWAS
• chronic kidney disease (CKD)
• polygenic risk score (PRS)

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MESH Headings Collapse

Grants

• OT2 OD026556 NIH HHS
• U2C OD023196 NIH HHS
• OT2 OD026551 NIH HHS
• U24 OD023121 NIH HHS
• OT2 OD026552 NIH HHS
• OT2 OD026549 NIH HHS
• OT2 OD025337 NIH HHS
• OT2 OD025277 NIH HHS
• OT2 OD026555 NIH HHS
• OT2 OD026550 NIH HHS
• OT2 OD026553 NIH HHS
• OT2 OD023205 NIH HHS
• U01 HG008680 NHGRI NIH HHS
• OT2 OD025276 NIH HHS
• OT2 OD026557 NIH HHS
• U24 OD023163 NIH HHS
• OT2 OD023206 NIH HHS
• R01 LM013061 NLM NIH HHS
• U24 OD023176 NIH HHS
• OT2 OD026548 NIH HHS
• OT2 OD025315 NIH HHS
• K25 DK128563 NIDDK NIH HHS
• OT2 OD026554 NIH HHS
• UL1 TR001873 NCATS NIH HHS

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Affiliation(s)

Atlas Khan Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Ning Shang Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Jordan G. Nestor Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Chunhua Weng Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
George Hripcsak Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
Peter C. Harris Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, USA
Ali G. Gharavi Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
Krzysztof Kiryluk Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY

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Multi-ancestry GWAS of Fuchs corneal dystrophy highlights roles of laminins, collagen, and endothelial cell regulation

Fuchs endothelial corneal dystrophy (FECD) is a leading indication for corneal transplantation, but its molecular pathophysiology remains poorly understood. We performed genome-wide association studies (GWAS) of FECD in the Million Veteran Program (MVP) and meta-analyzed with the previous largest FECD GWAS, finding twelve significant loci (eight novel). We further confirmed the TCF4 locus in admixed African and Hispanic/Latino ancestries, and found an enrichment of European-ancestry haplotypes at TCF4 in FECD cases. Among the novel associations are low frequency missense variants in laminin genes LAMA5 and LAMB1 which, together with previously reported LAMC1, form laminin-511 (LM511). AlphaFold 2 protein modeling suggests that mutations at LAMA5 and LAMB1 may destabilize LM511 by altering inter-domain interactions or extracellular matrix binding. Finally, phenome-wide association scans and co-localization analyses suggest that the TCF4 CTG18.1 trinucleotide repeat expansion leads to dysregulation of ion transport in the corneal endothelium and has pleiotropic effects on renal function.

Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.

A multi-site randomized trial of a clinical decision support intervention to improve problem list completeness

OBJECTIVE

To improve problem list documentation and care quality.

MATERIALS AND METHODS

We developed algorithms to infer clinical problems a patient has that are not recorded on the coded problem list using structured data in the electronic health record (EHR) for 12 clinically significant heart, lung, and blood diseases. We also developed a clinical decision support (CDS) intervention which suggests adding missing problems to the problem list. We evaluated the intervention at 4 diverse healthcare systems using 3 different EHRs in a randomized trial using 3 predetermined outcome measures: alert acceptance, problem addition, and National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) clinical quality measures.

RESULTS

There were 288 832 opportunities to add a problem in the intervention arm and the problem was added 63 777 times (acceptance rate 22.1%). The intervention arm had 4.6 times as many problems added as the control arm. There were no significant differences in any of the clinical quality measures.

DISCUSSION

The CDS intervention was highly effective at improving problem list completeness. However, the improvement in problem list utilization was not associated with improvement in the quality measures. The lack of effect on quality measures suggests that problem list documentation is not directly associated with improvements in quality measured by National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) quality measures. However, improved problem list accuracy has other benefits, including clinical care, patient comprehension of health conditions, accurate CDS and population health, and for research.

CONCLUSION

An EHR-embedded CDS intervention was effective at improving problem list completeness but was not associated with improvement in quality measures.

Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

BACKGROUND

Heart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs. Therefore, it is crucial to identify patients with HFrEF who are at high risk of subsequent events after HF hospitalization.

OBJECTIVE

Machine learning (ML) has been used to predict HF-related outcomes. The objective of this study was to compare different ML prediction models and feature construction methods to predict 30-, 90-, and 365-day hospital readmissions and worsening HF events (WHFEs).

METHODS

We used the Veradigm PINNACLE outpatient registry linked to Symphony Health's Integrated Dataverse data from July 1, 2013, to September 30, 2017. Adults with a confirmed diagnosis of HFrEF and HF-related hospitalization were included. WHFEs were defined as HF-related hospitalizations or outpatient intravenous diuretic use within 1 year of the first HF hospitalization. We used different approaches to construct ML features from clinical codes, including frequencies of clinical classification software (CCS) categories, Bidirectional Encoder Representations From Transformers (BERT) trained with CCS sequences (BERT + CCS), BERT trained on raw clinical codes (BERT + raw), and prespecified features based on clinical knowledge. A multilayer perceptron neural network, extreme gradient boosting (XGBoost), random forest, and logistic regression prediction models were applied and compared.

RESULTS

A total of 30,687 adult patients with HFrEF were included in the analysis; 11.41% (3184/27,917) of adults experienced a hospital readmission within 30 days of their first HF hospitalization, and nearly half (9231/21,562, 42.81%) of the patients experienced at least 1 WHFE within 1 year after HF hospitalization. The prediction models and feature combinations with the best area under the receiver operating characteristic curve (AUC) for each outcome were XGBoost with CCS frequency (AUC=0.595) for 30-day readmission, random forest with CCS frequency (AUC=0.630) for 90-day readmission, XGBoost with CCS frequency (AUC=0.649) for 365-day readmission, and XGBoost with CCS frequency (AUC=0.640) for WHFEs. Our ML models could discriminate between readmission and WHFE among patients with HFrEF. Our model performance was mediocre, especially for the 30-day readmission events, most likely owing to limitations of the data, including an imbalance between positive and negative cases and high missing rates of many clinical variables and outcome definitions.

CONCLUSIONS

We predicted readmissions and WHFEs after HF hospitalizations in patients with HFrEF. Features identified by data-driven approaches may be comparable with those identified by clinical domain knowledge. Future work may be warranted to validate and improve the models using more longitudinal electronic health records that are complete, are comprehensive, and have a longer follow-up time.

Using language models and ontology topology to perform semantic mapping of traits between biomedical datasets

MOTIVATION

Human traits are typically represented in both the biomedical literature and large population studies as descriptive text strings. Whilst a number of ontologies exist, none of these perfectly represent the entire human phenome and exposome. Mapping trait names across large datasets is therefore time-consuming and challenging. Recent developments in language modelling have created new methods for semantic representation of words and phrases, and these methods offer new opportunities to map human trait names in the form of words and short phrases, both to ontologies and to each other. Here, we present a comparison between a range of established and more recent language modelling approaches for the task of mapping trait names from UK Biobank to the Experimental Factor Ontology (EFO), and also explore how they compare to each other in direct trait-to-trait mapping.

RESULTS

In our analyses of 1191 traits from UK Biobank with manual EFO mappings, the BioSentVec model performed best at predicting these, matching 40.3% of the manual mappings correctly. The BlueBERT-EFO model (finetuned on EFO) performed nearly as well (38.8% of traits matching the manual mapping). In contrast, Levenshtein edit distance only mapped 22% of traits correctly. Pairwise mapping of traits to each other demonstrated that many of the models can accurately group similar traits based on their semantic similarity.

AVAILABILITY AND IMPLEMENTATION

Our code is available at https://github.com/MRCIEU/vectology.

Identifying the potential causal role of insomnia symptoms on 11,409 health-related outcomes: a phenome-wide Mendelian randomisation analysis in UK Biobank

BACKGROUND

Insomnia symptoms are widespread in the population and might have effects on many chronic conditions and their risk factors but previous research has focused on select hypothesised associations/effects rather than taking a systematic hypothesis-free approach across many health outcomes.

METHODS

We performed a Mendelian randomisation (MR) phenome-wide association study (PheWAS) in 336,975 unrelated white-British UK Biobank participants. Self-reported insomnia symptoms were instrumented by a genetic risk score (GRS) created from 129 single-nucleotide polymorphisms (SNPs). A total of 11,409 outcomes from UK Biobank were extracted and processed by an automated pipeline (PHESANT) for the MR-PheWAS. Potential causal effects (those passing a Bonferroni-corrected significance threshold) were followed up with two-sample MR in MR-Base, where possible.

RESULTS

Four hundred thirty-seven potential causal effects of insomnia symptoms were observed for a diverse range of outcomes, including anxiety, depression, pain, body composition, respiratory, musculoskeletal and cardiovascular traits. We were able to undertake two-sample MR for 71 of these 437 and found evidence of causal effects (with directionally concordant effect estimates across main and sensitivity analyses) for 30 of these. These included novel findings (by which we mean not extensively explored in conventional observational studies and not previously explored using MR based on a systematic search) of an adverse effect on risk of spondylosis (OR [95%CI] = 1.55 [1.33, 1.81]) and bronchitis (OR [95%CI] = 1.12 [1.03, 1.22]), among others.

CONCLUSIONS

Insomnia symptoms potentially cause a wide range of adverse health-related outcomes and behaviours. This has implications for developing interventions to prevent and treat a number of diseases in order to reduce multimorbidity and associated polypharmacy.

Mapping anorexia nervosa genes to clinical phenotypes

BACKGROUND

Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes.

METHODS

Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations.

RESULTS

Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex.

CONCLUSIONS

Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.

Returning integrated genomic risk and clinical recommendations: The eMERGE study

PURPOSE

Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk.

METHODS

To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results.

RESULTS

GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022.

CONCLUSION

Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.

Systematic comparisons between Lyme disease and post-treatment Lyme disease syndrome in the U.S. with administrative claims data

BACKGROUND

Post-treatment Lyme disease syndrome (PTLDS) is used to describe Lyme disease patients who have the infection cleared by antibiotic but then experienced persisting symptoms of pain, fatigue, or cognitive impairment. Currently, little is known about the cause or epidemiology of PTLDS.

METHODS

We conducted a data-driven study with a large nationwide administrative dataset, which consists of more than 98 billion billing and 1.4 billion prescription records between 2008 and 2016, to identify unique aspects of PTLDS that could have diagnostic and etiologic values. We defined PTLDS based on its symptomatology and compared the demographic, longitudinal changes of comorbidity, and antibiotic prescriptions between patients who have Lyme with absence of prolonged symptoms (APS) and PTLDS.

FINDINGS

The age and temporal distributions were similar between Lyme APS and PTLDS. The PTLDS-to-Lyme APS case ratio was 3.42%. The co-occurrence of 3 out of 19 chronic conditions were significantly higher in PTLDS versus Lyme APS-odds ratio and 95% CI for anemia, hyperlipidemia, and osteoarthrosis were 1.46 (1.11-1.92), 1.39 (1.15-1.68), and 1.62 (1.23-2.12) respectively. We did not find significant differences between PTLDS and Lyme APS for the number of types of antibiotics prescribed (incidence rate ratio = 1.009, p = 0.90) and for the prescription of each of the five antibiotics (FDR adjusted p values 0.72-0.95).

INTERPRETATION

PTLDS cases have more codes corresponding to anemia, hyperlipidemia, and osteoarthrosis compared to Lyme APS. Our finding of hyperlipidemia is consistent with a dysregulation of fat metabolism reported by other researchers, and further investigation should be conducted to understand the potential biological relationship between the two.

FUNDING

Steven & Alexandra Cohen Foundation, Global Lyme Alliance, and the Pazala Foundation; National Institutes of Health R01ES032470.

Health Conditions Associated with Alzheimer's Disease and Vascular Dementia

OBJECTIVE

We examined medical records to determine health conditions associated with dementia at varied intervals prior to dementia diagnosis in participants from the Baltimore Longitudinal Study of Aging (BLSA).

METHODS

Data were available for 347 Alzheimer's disease (AD), 76 vascular dementia (VaD), and 811 control participants without dementia. Logistic regressions were performed associating International Classification of Diseases, 9th Revision (ICD-9) health codes with dementia status across all time points, at 5 and 1 year(s) prior to dementia diagnosis, and at the year of diagnosis, controlling for age, sex, and follow-up length of the medical record.

RESULTS

In AD, the earliest and most consistent associations across all time points included depression, erectile dysfunction, gait abnormalities, hearing loss, and nervous and musculoskeletal symptoms. Cardiomegaly, urinary incontinence, non-epithelial skin cancer, and pneumonia were not significant until 1 year before dementia diagnosis. In VaD, the earliest and most consistent associations across all time points included abnormal electrocardiogram (EKG), cardiac dysrhythmias, cerebrovascular disease, non-epithelial skin cancer, depression, and hearing loss. Atrial fibrillation, occlusion of cerebral arteries, essential tremor, and abnormal reflexes were not significant until 1 year before dementia diagnosis.

INTERPRETATION

These findings suggest that some health conditions are associated with future dementia beginning at least 5 years before dementia diagnosis and are consistently seen over time, while others only reach significance closer to the date of diagnosis. These results also show that there are both shared and distinctive health conditions associated with AD and VaD. These results reinforce the need for medical intervention and treatment to lessen the impact of health comorbidities in the aging population. ANN NEUROL 2023;93:805-818.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Multiancestry Genome-Wide Association Study of Aortic Stenosis Identifies Multiple Novel Loci in the Million Veteran Program

BACKGROUND

Calcific aortic stenosis (CAS) is the most common valvular heart disease in older adults and has no effective preventive therapies. Genome-wide association studies (GWAS) can identify genes influencing disease and may help prioritize therapeutic targets for CAS.

METHODS

We performed a GWAS and gene association study of 14 451 patients with CAS and 398 544 controls in the Million Veteran Program. Replication was performed in the Million Veteran Program, Penn Medicine Biobank, Mass General Brigham Biobank, BioVU, and BioMe, totaling 12 889 cases and 348 094 controls. Causal genes were prioritized from genome-wide significant variants using polygenic priority score gene localization, expression quantitative trait locus colocalization, and nearest gene methods. CAS genetic architecture was compared with that of atherosclerotic cardiovascular disease. Causal inference for cardiometabolic biomarkers in CAS was performed using Mendelian randomization and genome-wide significant loci were characterized further through phenome-wide association study.

RESULTS

We identified 23 genome-wide significant lead variants in our GWAS representing 17 unique genomic regions. Of the 23 lead variants, 14 were significant in replication, representing 11 unique genomic regions. Five replicated genomic regions were previously known risk loci for CAS (PALMD, TEX41, IL6, LPA, FADS) and 6 were novel (CEP85L, FTO, SLMAP, CELSR2, MECOM, CDAN1). Two novel lead variants were associated in non-White individuals (P<0.05): rs12740374 (CELSR2) in Black and Hispanic individuals and rs1522387 (SLMAP) in Black individuals. Of the 14 replicated lead variants, only 2 (rs10455872 [LPA], rs12740374 [CELSR2]) were also significant in atherosclerotic cardiovascular disease GWAS. In Mendelian randomization, lipoprotein(a) and low-density lipoprotein cholesterol were both associated with CAS, but the association between low-density lipoprotein cholesterol and CAS was attenuated when adjusting for lipoprotein(a). Phenome-wide association study highlighted varying degrees of pleiotropy, including between CAS and obesity at the FTO locus. However, the FTO locus remained associated with CAS after adjusting for body mass index and maintained a significant independent effect on CAS in mediation analysis.

CONCLUSIONS

We performed a multiancestry GWAS in CAS and identified 6 novel genomic regions in the disease. Secondary analyses highlighted the roles of lipid metabolism, inflammation, cellular senescence, and adiposity in the pathobiology of CAS and clarified the shared and differential genetic architectures of CAS with atherosclerotic cardiovascular diseases.

Phenome-wide Association Analysis of Substance Use Disorders in a Deeply Phenotyped Sample

BACKGROUND

Substance use disorders (SUDs) are associated with a variety of co-occurring psychiatric disorders and other SUDs, which partly reflects genetic pleiotropy. Polygenic risk scores (PRSs) and phenome-wide association studies are useful in evaluating pleiotropic effects. However, the comparatively low prevalence of SUDs in population samples and the lack of detailed information available in electronic health records limit these data sets' informativeness for such analyses.

METHODS

We used the deeply phenotyped Yale-Penn sample (n = 10,610 with genetic data; 46.3% African ancestry, 53.7% European ancestry) to examine pleiotropy for 4 major substance-related traits: alcohol use disorder, opioid use disorder, smoking initiation, and lifetime cannabis use. The sample includes both affected and control subjects interviewed using the Semi-Structured Assessment for Drug Dependence and Alcoholism, a comprehensive psychiatric interview.

RESULTS

In African ancestry individuals, PRS for alcohol use disorder, and in European individuals, PRS for alcohol use disorder, opioid use disorder, and smoking initiation were associated with their respective primary DSM diagnoses. These PRSs were also associated with additional phenotypes involving the same substance. Phenome-wide association study analyses of PRS in European individuals identified associations across multiple phenotypic domains, including phenotypes not commonly assessed in phenome-wide association study analyses, such as family environment and early childhood experiences.

CONCLUSIONS

Smaller, deeply phenotyped samples can complement large biobank genetic studies with limited phenotyping by providing greater phenotypic granularity. These efforts allow associations to be identified between specific features of disorders and genetic liability for SUDs, which help to inform our understanding of the pleiotropic pathways underlying them.

Patient-specific comorbidities as prognostic variables for survival in myelofibrosis

Treatment decisions in primary myelofibrosis (PMF) are guided by numerous prognostic systems. Patient-specific comorbidities have influence on treatment-related survival and are considered in clinical contexts but have not been routinely incorporated into current prognostic models. We hypothesized that patient-specific comorbidities would inform prognosis and could be incorporated into a quantitative score. All patients with PMF or secondary myelofibrosis with available DNA and comprehensive electronic health record (EHR) data treated at Vanderbilt University Medical Center between 1995 and 2016 were identified within Vanderbilt's Synthetic Derivative and BioVU Biobank. We recapitulated established PMF risk scores (eg, Dynamic International Prognostic Scoring System [DIPSS], DIPSS plus, Genetics-Based Prognostic Scoring System, Mutation-Enhanced International Prognostic Scoring System 70+) and comorbidities through EHR chart extraction and next-generation sequencing on biobanked peripheral blood DNA. The impact of comorbidities was assessed via DIPSS-adjusted overall survival using Bonferroni correction. Comorbidities associated with inferior survival include renal failure/dysfunction (hazard ratio [HR], 4.3; 95% confidence interval [95% CI], 2.1-8.9; P = .0001), intracranial hemorrhage (HR, 28.7; 95% CI, 7.0-116.8; P = 2.83e-06), invasive fungal infection (HR, 41.2; 95% CI, 7.2-235.2; P = 2.90e-05), and chronic encephalopathy (HR, 15.1; 95% CI, 3.8-59.4; P = .0001). The extended DIPSS model including all 4 significant comorbidities showed a significantly higher discriminating power (C-index 0.81; 95% CI, 0.78-0.84) than the original DIPSS model (C-index 0.73; 95% CI, 0.70-0.77). In summary, we repurposed an institutional biobank to identify and risk-classify an uncommon hematologic malignancy by established (eg, DIPSS) and other clinical and pathologic factors (eg, comorbidities) in an unbiased fashion. The inclusion of comorbidities into risk evaluation may augment prognostic capability of future genetics-based scoring systems.

Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction.

The genetic architecture of pain intensity in a sample of 598,339 U.S. veterans

Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids played a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 125 independent genetic loci, 82 of which are novel. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level, and cognitive traits. Integration of the GWAS findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, beta-blockers, and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

Associations of ABO and Rhesus D blood groups with phenome-wide disease incidence: A 41-year retrospective cohort study of 482,914 patients

Background

Whether natural selection may have attributed to the observed blood group frequency differences between populations remains debatable. The ABO system has been associated with several diseases and recently also with susceptibility to COVID-19 infection. Associative studies of the RhD system and diseases are sparser. A large disease-wide risk analysis may further elucidate the relationship between the ABO/RhD blood groups and disease incidence.

Methods

We performed a systematic log-linear quasi-Poisson regression analysis of the ABO/RhD blood groups across 1,312 phecode diagnoses. Unlike prior studies, we determined the incidence rate ratio for each individual ABO blood group relative to all other ABO blood groups as opposed to using blood group O as the reference. Moreover, we used up to 41 years of nationwide Danish follow-up data, and a disease categorization scheme specifically developed for diagnosis-wide analysis. Further, we determined associations between the ABO/RhD blood groups and the age at the first diagnosis. Estimates were adjusted for multiple testing.

Results

The retrospective cohort included 482,914 Danish patients (60.4% females). The incidence rate ratios (IRRs) of 101 phecodes were found statistically significant between the ABO blood groups, while the IRRs of 28 phecodes were found statistically significant for the RhD blood group. The associations included cancers and musculoskeletal-, genitourinary-, endocrinal-, infectious-, cardiovascular-, and gastrointestinal diseases.

Conclusions

We found associations of disease-wide susceptibility differences between the blood groups of the ABO and RhD systems, including cancer of the tongue, monocytic leukemia, cervical cancer, osteoarthrosis, asthma, and HIV- and hepatitis B infection. We found marginal evidence of associations between the blood groups and the age at first diagnosis.

Funding

Novo Nordisk Foundation and the Innovation Fund Denmark.

Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder

Objective

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed mental and physical health problems, prior pharmacological treatments, and aggregate genetic factors has potential to inform risk stratification and mitigation strategies.

Methods

In this study of 3,942 SCZ and 5,414 BPI patients receiving VA care, self-reported SB and ideation were assessed using the Columbia Suicide Severity Rating Scale (C-SSRS). These cross-sectional data were integrated with electronic health records (EHR), and compared by lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. Polygenic scores (PGS) for traits related to psychiatric disorders, substance use, and cognition were constructed using available genomic data, and exploratory genome-wide association studies were performed to identify and prioritize specific loci.

Results

Only 20% of veterans who self-reported SB had a corroborating ICD-9/10 code in their EHR; and among those who denied prior behaviors, more than 20% reported new-onset SB at follow-up. SB were associated with a range of psychiatric and non-psychiatric diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking, suicide attempt, and major depressive disorder were also associated with attempt and ideation.

Conclusions

Among individuals with a diagnosed mental illness, a GWAS for SB did not yield any significant loci. Self-reported SB were strongly associated with clinical variables across several EHR domains. Overall, clinical and polygenic analyses point to sequelae of substance-use related behaviors and other psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in clinical settings, underscoring the value of regular screening based on direct, in-person assessments, especially among high-risk individuals.

Phenome-wide association study of genetically predicted B vitamins and homocysteine biomarkers with multiple health and disease outcomes: analysis of the UK Biobank

BACKGROUND

Although a number of health outcomes such as CVDs, metabolic-related outcomes, neurological disorders, pregnancy outcomes, and cancers have been identified in relation to B vitamins, evidence is of uneven quality and volume, and there is uncertainty about putative causal relationships.

OBJECTIVES

To explore the effects of B vitamins and homocysteine on a wide range of health outcomes based on a large biorepository linking biological samples and electronic medical records.

METHODS

First, we performed a phenome-wide association study (PheWAS) to investigate the associations of genetically predicted plasma concentrations (genetic component of the circulating concentrations) of folate, vitamin B6, vitamin B12, and their metabolite homocysteine with a wide range of disease outcomes (including both prevalent and incident events) among 385,917 individuals in the UK Biobank. Second, 2-sample Mendelian randomization (MR) analysis was used to replicate any observed associations and detect causality. We considered MR P <0.05 as significant for replication. Third, dose-response, mediation, and bioinformatics analyses were carried out to examine any nonlinear trends and to disentangle the underlying mediating biological mechanisms for the identified associations.

RESULTS

In total, 1117 phenotypes were tested in each PheWAS analysis. After multiple corrections, 32 phenotypic associations of B vitamins and homocysteine were identified. Two-sample MR analysis supported that 3 of them were causal, including associations of higher plasma vitamin B6 with lower risk of calculus of kidney (OR: 0.64; 95% CI: 0.42, 0.97; P = 0.033), higher homocysteine concentration with higher risk of hypercholesterolemia (OR: 1.28, 95% CI: 1.04, 1.56; P = 0.018), and chronic kidney disease (OR: 1.32, 95% CI: 1.06, 1.63; P = 0.012). Significant nonlinear dose-response relationships were observed for the associations of folate with anemia, vitamin B12 with vitamin B-complex deficiencies, anemia and cholelithiasis, and homocysteine with cerebrovascular disease.

CONCLUSIONS

This study provides strong evidence for the associations of B vitamins and homocysteine with endocrine/metabolic and genitourinary disorders.

PPM1K-regulated impaired catabolism of branched-chain amino acids orchestrates polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is one of the most common diseases with the coexistence of reproductive malfunction and metabolic disorders. Previous studies have found increased branched chain amino acid (BCAA) levels in women with PCOS. However, it remains unclear whether BCAA metabolism is causally associated with the risk of PCOS.

METHODS

The changes of BCAA levels in the plasma and follicular fluids of PCOS women were detected. Mendelian randomization (MR) approaches were used to explore the potential causal association between BCAA levels and the risk of PCOS. The function of the gene coding the protein phosphatase Mg2+/Mn2+-dependent 1K (PPM1K) was further explored by using Ppm1k-deficient mouse model and PPM1K down-regulated human ovarian granulosa cells.

FINDINGS

BCAA levels were significantly elevated in both plasma and follicular fluids of PCOS women. Based on MR, a potential direct, causal role for BCAA metabolism was revealed in the pathogenesis of PCOS, and PPM1K was detected as a vital driver. Ppm1k-deficient female mice had increased BCAA levels and exhibited PCOS-like traits, including hyperandrogenemia and abnormal follicle development. A reduction in dietary BCAA intake significantly improved the endocrine and ovarian dysfunction of Ppm1k-/- female mice. Knockdown of PPM1K promoted the conversion of glycolysis to pentose phosphate pathway and inhibited mitochondrial oxidative phosphorylation in human granulosa cells.

INTERPRETATION

Ppm1k deficiency-impaired BCAA catabolism causes the occurrence and development of PCOS. PPM1K suppression disturbed energy metabolism homeostasis in the follicular microenvironment, which provided an underlying mechanism of abnormal follicle development.

FUNDING

This study was supported by the National Key Research and Development Program of China (2021YFC2700402, 2019YFA0802503), the National Natural Science Foundation of China (81871139, 82001503, 92057107), the CAMS Innovation Fund for Medical Sciences (2019-I2M-5-001), Key Clinical Projects of Peking University Third Hospital (BYSY2022043), the China Postdoctoral Science Foundation (2021T140600), and the Collaborative Innovation Program of Shanghai Municipal Health Commission (2020CXJQ01).

Developing a Phenotype Risk Score for Tic Disorders in a Large, Clinical Biobank

Importance

Tics are a common feature of early-onset neurodevelopmental disorders, characterized by involuntary and repetitive movements or sounds. Despite affecting up to 2% of young children and having a genetic contribution, the underlying causes remain poorly understood, likely due to the complex phenotypic and genetic heterogeneity among affected individuals.

Objective

In this study, we leverage dense phenotype information from electronic health records to identify the disease features associated with tic disorders within the context of a clinical biobank. These disease features are then used to generate a phenotype risk score for tic disorder.

Design

Using de-identified electronic health records from a tertiary care center, we extracted individuals with tic disorder diagnosis codes. We performed a phenome-wide association study to identify the features enriched in tic cases versus controls (N=1,406 and 7,030; respectively). These disease features were then used to generate a phenotype risk score for tic disorder, which was applied across an independent set of 90,051 individuals. A previously curated set of tic disorder cases from an electronic health record algorithm followed by clinician chart review was used to validate the tic disorder phenotype risk score.

Main Outcomes and Measures

Phenotypic patterns associated with a tic disorder diagnosis in the electronic health record.

Results

Our tic disorder phenome-wide association study revealed 69 significantly associated phenotypes, predominantly neuropsychiatric conditions, including obsessive compulsive disorder, attention-deficit hyperactivity disorder, autism, and anxiety. The phenotype risk score constructed from these 69 phenotypes in an independent population was significantly higher among clinician-validated tic cases versus non-cases.

Conclusions and Relevance

Our findings provide support for the use of large-scale medical databases to better understand phenotypically complex diseases, such as tic disorders. The tic disorder phenotype risk score provides a quantitative measure of disease risk that can be leveraged for the assignment of individuals in case-control studies or for additional downstream analyses.

Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping

OBJECTIVE

A previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP.

MATERIALS AND METHODS

We compared how each resource produced top-ranked concept unique identifiers (CUIs) by term frequency-inverse document frequency with Jaccard matrices comparing single resources and the original PheMAP. We correlated top-ranked concepts from each resource to features used in established Phenotype KnowledgeBase (PheKB) algorithms for hypothyroidism, type II diabetes mellitus (T2DM), and dementias. Using resources separately, we calculated multiple phenotype risk scores for individuals from Vanderbilt University Medical Center's BioVU DNA Biobank and compared phenotyping performance against rule-based eMERGE algorithms. Lastly, we implemented an ensemble strategy which classified patient case/control status based upon PheMAP resource agreement.

RESULTS

Jaccard similarity matrices indicate that the similarity of CUIs comprising single resource-based PheMAPs varies. Single resource-based PheMAPs generated from MedlinePlus and MedicineNet outperformed others but only encompass 81.6% of overall disease phenotypes. We propose the PheMAP-Ensemble which provides higher average accuracy and precision than the combined average accuracy and precision of single resource-based PheMAPs. While offering complete phenotype coverage, PheMAP-Ensemble significantly increases phenotyping recall compared to the original iteration.

CONCLUSIONS

Resources comprising the PheMAP produce different phenotyping performance when implemented individually. The ensemble method significantly improves the quality of PheMAP by fully utilizing dissimilar resources to capture accurate phenotyping data from EHRs.

Analysis of genetically determined gene expression suggests role of inflammatory processes in exfoliation syndrome

BACKGROUND

Exfoliation syndrome (XFS) is an age-related systemic disorder characterized by excessive production and progressive accumulation of abnormal extracellular material, with pathognomonic ocular manifestations. It is the most common cause of secondary glaucoma, resulting in widespread global blindness. The largest global meta-analysis of XFS in 123,457 multi-ethnic individuals from 24 countries identified seven loci with the strongest association signal in chr15q22-25 region near LOXL1. Expression analysis have so far correlated coding and a few non-coding variants in the region with LOXL1 expression levels, but functional effects of these variants is unclear. We hypothesize that analysis of the contribution of the genetically determined component of gene expression to XFS risk can provide a powerful method to elucidate potential roles of additional genes and clarify biology that underlie XFS.

RESULTS

Transcriptomic Wide Association Studies (TWAS) using PrediXcan models trained in 48 GTEx tissues leveraging on results from the multi-ethnic and European ancestry GWAS were performed. To eliminate the possibility of false-positive results due to Linkage Disequilibrium (LD) contamination, we i) performed PrediXcan analysis in reduced models removing variants in LD with LOXL1 missense variants associated with XFS, and variants in LOXL1 models in both multiethnic and European ancestry individuals, ii) conducted conditional analysis of the significant signals in European ancestry individuals, and iii) filtered signals based on correlated gene expression, LD and shared eQTLs, iv) conducted expression validation analysis in human iris tissues. We observed twenty-eight genes in chr15q22-25 region that showed statistically significant associations, which were whittled down to ten genes after statistical validations. In experimental analysis, mRNA transcript levels for ARID3B, CD276, LOXL1, NEO1, SCAMP2, and UBL7 were significantly decreased in iris tissues from XFS patients compared to control samples. TWAS genes for XFS were significantly enriched for genes associated with inflammatory conditions. We also observed a higher incidence of XFS comorbidity with inflammatory and connective tissue diseases.

CONCLUSION

Our results implicate a role for connective tissues and inflammation pathways in the etiology of XFS. Targeting the inflammatory pathway may be a potential therapeutic option to reduce progression in XFS.

LAVAA: a lightweight association viewer across ailments

Motivation

Biobank scale genetic associations results over thousands of traits can be difficult to visualize and navigate.

Results

We have created LAVAA, a visualization web-application to generate genetic volcano plots for simultaneously considering the P-value, effect size, case counts, trait class and fine-mapping posterior probability at a single-nucleotide polymorphism (SNP) across a range of traits from a large set of genome-wide association study. We find that user interaction with association results in LAVAA can enrich and enhance the biological interpretation of individual loci.

Availability and implementation

LAVAA is available as a stand-alone web service (https://geneviz.aalto.fi/LAVAA/) and will be available in future releases of the finngen.fi website starting with release 10 in late 2023.

Phenome-wide Investigation of Behavioral, Environmental, and Neural Associations with Cross-Disorder Genetic Liability in Youth of European Ancestry

Etiologic insights into psychopathology may be gained by using hypothesis-free methods to identify associations between genetic risk for broad psychopathology and phenotypes measured during adolescence, including both markers of child psychopathology and intermediate phenotypes such as neural structure that may link genetic risk with outcomes. We conducted a phenome-wide association study (phenotype n=1,269-1,694) of polygenic risk scores (PRS) for broad spectrum psychopathology (i.e., Compulsive, Psychotic, Neurodevelopmental, and Internalizing) in youth of PCA-selected European ancestry (n=5,556; ages 9-13) who completed the baseline and/or two-year follow-up of the ongoing Adolescent Brain Cognitive Development℠ (ABCD) Study. We found that Neurodevelopmental and Internalizing PRS were significantly associated with a host of proximal as well as distal phenotypes (Neurodevelopmental: 187 and 211; Internalizing: 122 and 173 phenotypes at baseline and two-year follow-up, respectively), whereas Compulsive and Psychotic PRS showed zero and one significant associations, respectively, after Bonferroni correction. Neurodevelopmental PRS were further associated with brain structure metrics (e.g., total volume, mean right hemisphere cortical thickness), with only cortical volume indirectly linking Neurodevelopmental PRS to grades in school. Genetic variation influencing risk to psychopathology manifests broadly as behaviors, psychopathology symptoms, and related risk factors in middle childhood and early adolescence.

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Biobanks of linked clinical patient histories and biological samples are an efficient strategy to generate large cohorts for modern genetics research. Biobank recruitment varies by factors such as geographic catchment and sampling strategy, which affect biobank demographics and research utility. Here, we describe the Michigan Genomics Initiative (MGI), a single-health-system biobank currently consisting of >91,000 participants recruited primarily during surgical encounters at Michigan Medicine. The surgical enrollment results in a biobank enriched for many diseases and ideally suited for a disease genetics cohort. Compared with the much larger population-based UK Biobank, MGI has higher prevalence for nearly all diagnosis-code-based phenotypes and larger absolute case counts for many phenotypes. Genome-wide association study (GWAS) results replicate known findings, thereby validating the genetic and clinical data. Our results illustrate that opportunistic biobank sampling within single health systems provides a unique and complementary resource for exploring the genetics of complex diseases.

Mendelian randomization as a tool to inform drug development using human genetics

Drug development is essential to the advancement of human health, however, the process is slow, costly, and at high risk of failure at all stages. A promising strategy for expediting and improving the probability of success in the drug development process is the use of naturally randomized human genetic variation for drug target identification and validation. These data can be harnessed using the Mendelian randomization (MR) analytic paradigm to proxy the lifelong consequences of genetic perturbations of drug targets. In this review, we discuss the myriad applications of the MR paradigm for human drug target identification and validation. We review the methodology and applications of MR, key limitations of MR, and potential future opportunities for research. Throughout the review, we refer to illustrative examples of MR analyses investigating the consequences of genetic inhibition of interleukin 6 signaling which, in some cases, have anticipated results from randomized controlled trials. As human genetic data become more widely available, we predict that MR will serve as a key pillar of support for drug development efforts.

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery

A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.