Electronic health records: the next wave of complex disease genetics

Expanding scope of genetic studies in the era of biobanks

Biobanks have become pivotal in genetic research, particularly through genome-wide association studies (GWAS), driving transformative insights into the genetic basis of complex diseases and traits through the integration of genetic data with phenotypic, environmental, family history, and behavioral information. This review explores the distinct design and utility of different biobanks, highlighting their unique contributions to genetic research. We further discuss the utility and methodological advances in combining data from disease-specific study or consortia with that of biobanks, especially focusing on summary statistics based meta-analysis. Subsequently we review the spectrum of additional advantages offered by biobanks in genetic studies in representing population differences, calibration of polygenic scores, assessment of pleiotropy and improving post-GWAS in silico analyses. Advances in sequencing technologies, particularly whole-exome and whole-genome sequencing, have further enabled the discovery of rare variants at biobank scale. Among recent developments, the integration of large-scale multi-omics data especially proteomics and metabolomics, within biobanks provides deeper insights into disease mechanisms and regulatory pathways. Despite challenges like ascertainment strategies and phenotypic misclassification, biobanks continue to evolve, driving methodological innovation and enabling precision medicine. We highlight the contributions of biobanks to genetic research, their growing integration with multi-omics, and finally discuss their future potential for advancing healthcare and therapeutic development.

Inclusion bias affects common variant discovery and replication in a health-system linked biobank

Electronic Health Records (EHR) -linked biobanks have emerged as promising tools for precision medicine, enabling the integration of clinical and molecular data for individual risk assessment. Association studies performed in biobank studies can connect common genetic variation to clinical phenotypes, such as through the use of polygenic scores (PGS), which are starting to have utility in aiding clinician decision making. However, while biobanks aggregate large amounts of data effectively for such studies, most employ various opt-in consent protocols, and, as a result, are expected to be subject to participation and recruitment biases. The extent to which biases affect genetic analyses in biobanks remains unstudied. In this study, we quantify bias and evaluate its impact on genetic analyses, using the UCLA ATLAS Community Health Initiative as a case study. Our analyses reveal that a wide array of factors, particularly socio-demographic characteristics and healthcare utilization patterns, influence participation, effectively differentiating biobank participants from the broader patient population (AUROC = 0.85, AUPRC = 0.82). Through weighting the sample using inverse probability weights derived from probabilities of enrollment, we replicated 54% more known GWAS variants than models that did not take bias into account (e.g. associations between variants in the PPARG gene and type 2 diabetes). We further show that PGS-Phenome wide associations are affected by the weighting scheme, and suggest associations corroborated by weighted analyses to be more robust. Our results highlight that genetic analyses within biobanks should account for inclusion biases, and suggest inverse probability weighting as a potential approach.

Knowledge-Driven Online Multimodal Automated Phenotyping System

Though electronic health record (EHR) systems are a rich repository of clinical information with large potential, the use of EHR-based phenotyping algorithms is often hindered by inaccurate diagnostic records, the presence of many irrelevant features, and the requirement for a human-labeled training set. In this paper, we describe a knowledge-driven online multimodal automated phenotyping (KOMAP) system that i) generates a list of informative features by an online narrative and codified feature search engine (ONCE) and ii) enables the training of a multimodal phenotyping algorithm based on summary data. Powered by composite knowledge from multiple EHR sources, online article corpora, and a large language model, features selected by ONCE show high concordance with the state-of-the-art AI models (GPT4 and ChatGPT) and encourage large-scale phenotyping by providing a smaller but highly relevant feature set. Validation of the KOMAP system across four healthcare centers suggests that it can generate efficient phenotyping algorithms with robust performance. Compared to other methods requiring patient-level inputs and gold-standard labels, the fully online KOMAP provides a significant opportunity to enable multi-center collaboration.

Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment

Exposure to traumatic stress is common in the general population. Variation in the brain's molecular encoding of stress potentially contributes to the heterogeneous clinical outcomes in response to traumatic experiences. For instance, only a minority of those exposed to trauma will develop post-traumatic stress disorder (PTSD). Risk for PTSD is at least partially heritable, with a growing number of genetic factors identified through GWAS. A major limitation of genetic studies is that they capture only the genetic component of risk, whereas PTSD by definition requires an environmental traumatic exposure. Furthermore, the extent, timing, and type of trauma affects susceptibility. Here, we discuss the molecular mechanisms of PTSD risk together with gene × environment interactions, with a focus on how either might inform genetic screening for individuals at high risk for disease, reveal biological mechanisms that might one day yield novel therapeutics, and impact best clinical practices even today. To close, we discuss the interaction of trauma with sex, gender, and race, with a focus on the implications for treatment. Altogether, we suggest that predicting, preventing, and treating PTSD will require integrating both genotypic and environmental information.

Clinical associations with treatment resistance in depression: An electronic health record study

Treatment resistance is common in major depressive disorder (MDD), yet clinical risk factors are not well understood. Using a discovery-replication design, we conducted phenome-wide association studies (PheWASs) of MDD treatment resistance in two electronic health record (EHR)-linked biobanks. The PheWAS included participants with an MDD diagnosis in the EHR and at least one antidepressant (AD) prescription. Participant lifetime diagnoses were mapped to phecodes. PheWASs were conducted for three treatment resistance outcomes based on AD prescription data: number of unique ADs prescribed, ≥1 and ≥2 CE switches. Of the 180 phecodes significantly associated with these outcomes in the discovery cohort (n = 12,558), 71 replicated (n = 8,206). In addition to identifying known clinical factors for treatment resistance in MDD, the total unique AD prescriptions was associated with additional clinical variables including irritable bowel syndrome, gastroesophageal reflux disease, symptomatic menopause, and spondylosis. We calculated polygenic risk of specific-associated conditions and tested their association with AD outcomes revealing that genetic risk for many of these conditions is also associated with the total unique AD prescriptions. The number of unique ADs prescribed, which is easily assessed in EHRs, provides a more nuanced measure of treatment resistance, and may facilitate future research and clinical application in this area.

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

Clinical and genetic contributions to medical comorbidity in bipolar disorder: a study using electronic health records-linked biobank data

Bipolar disorder is a chronic and complex polygenic disease with high rates of comorbidity. However, the independent contribution of either diagnosis or genetic risk of bipolar disorder to the medical comorbidity profile of individuals with the disease remains unresolved. Here, we conducted a multi-step phenome-wide association study (PheWAS) of bipolar disorder using phenomes derived from the electronic health records of participants enrolled in the Mayo Clinic Biobank and the Mayo Clinic Bipolar Disorder Biobank. First, we explored the conditions associated with a diagnosis of bipolar disorder by conducting a phenotype-based PheWAS followed by LASSO-penalized regression to account for correlations within the phenome. Then, we explored the conditions associated with bipolar disorder polygenic risk score (BD-PRS) using a PRS-based PheWAS with a sequential exclusion approach to account for the possibility that diagnosis, instead of genetic risk, may drive such associations. 53,386 participants (58.7% women) with a mean age at analysis of 67.8 years (SD = 15.6) were included. A bipolar disorder diagnosis (n = 1479) was associated with higher rates of psychiatric conditions, injuries and poisonings, endocrine/metabolic and neurological conditions, viral hepatitis C, and asthma. BD-PRS was associated with psychiatric comorbidities but, in contrast, had no positive associations with general medical conditions. While our findings warrant confirmation with longitudinal-prospective studies, the limited associations between bipolar disorder genetics and medical conditions suggest that shared environmental effects or environmental consequences of diagnosis may have a greater impact on the general medical comorbidity profile of individuals with bipolar disorder than its genetic risk.

Genomic diversity improves disease discovery for all

Genomic data from different populations will improve understanding of complex diseases.

Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.

Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million Veteran Program

The expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.

Electronic health records (EHRs) in clinical research and platform trials: Application of the innovative EHR-based methods developed by EU-PEARL

OBJECTIVE

Electronic Health Record (EHR) systems are digital platforms in clinical practice used to collect patients' clinical information related to their health status and represents a useful storage of real-world data. EHRs have a potential role in research studies, in particular, in platform trials. Platform trials are innovative trial designs including multiple trial arms (conducted simultaneously and/or sequentially) on different treatments under a single master protocol. However, the use of EHRs in research comes with important challenges such as incompleteness of records and the need to translate trial eligibility criteria into interoperable queries. In this paper, we aim to review and to describe our proposed innovative methods to tackle some of the most important challenges identified. This work is part of the Innovative Medicines Initiative (IMI) EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project's work package 3 (WP3), whose objective is to deliver tools and guidance for EHR-based protocol feasibility assessment, clinical site selection, and patient pre-screening in platform trials, investing in the building of a data-driven clinical network framework that can execute these complex innovative designs for which feasibility assessments are critically important.

METHODS

ISO standards and relevant references informed a readiness survey, producing 354 criteria with corresponding questions selected and harmonised through a 7-round scoring process (0-1) in stakeholder meetings, with 85% of consensus being the threshold of acceptance for a criterium/question. ATLAS cohort definition and Cohort Diagnostics were mainly used to create the trial feasibility eligibility (I/E) criteria as executable interoperable queries.

RESULTS

The WP3/EU-PEARL group developed a readiness survey (eSurvey) for an efficient selection of clinical sites with suitable EHRs, consisting of yes-or-no questions, and a set-up of interoperable proxy queries using physicians' defined trial criteria. Both actions facilitate recruiting trial participants and alignment between study costs/timelines and data-driven recruitment potential.

CONCLUSION

The eSurvey will help create an archive of clinical sites with mature EHR systems suitable to participate in clinical trials/platform trials, and the interoperable proxy queries of trial eligibility criteria will help identify the number of potential participants. Ultimately, these tools will contribute to the production of EHR-based protocol design.

Precision Medicine: Disease Subtyping and Tailored Treatment

The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program

Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P < 4.6 × 10 - 11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.

The genetic architecture of changes in adiposity during adulthood

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.

Glaucoma Genetic Risk Scores in the Million Veteran Program

PURPOSE

Primary open-angle glaucoma (POAG) is a degenerative eye disease for which early treatment is critical to mitigate visual impairment and irreversible blindness. POAG-associated loci individually confer incremental risk. Genetic risk score(s) (GRS) could enable POAG risk stratification. Despite significantly higher POAG burden among individuals of African ancestry (AFR), GRS are limited in this population. A recent large-scale, multi-ancestry meta-analysis identified 127 POAG-associated loci and calculated cross-ancestry and ancestry-specific effect estimates, including in European ancestry (EUR) and AFR individuals. We assessed the utility of the 127-variant GRS for POAG risk stratification in EUR and AFR Veterans in the Million Veteran Program (MVP). We also explored the association between GRS and documented invasive glaucoma surgery (IGS).

DESIGN

Cross-sectional study.

PARTICIPANTS

MVP Veterans with imputed genetic data, including 5830 POAG cases (445 with IGS documented in the electronic health record) and 64 476 controls.

METHODS

We tested unweighted and weighted GRS of 127 published risk variants in EUR (3382 cases and 58 811 controls) and AFR (2448 cases and 5665 controls) Veterans in the MVP. Weighted GRS were calculated using effect estimates from the most recently published report of cross-ancestry and ancestry-specific meta-analyses. We also evaluated GRS in POAG cases with documented IGS.

MAIN OUTCOME MEASURES

Performance of 127-variant GRS in EUR and AFR Veterans for POAG risk stratification and association with documented IGS.

RESULTS

GRS were significantly associated with POAG (P < 5 × 10-5) in both groups; a higher proportion of EUR compared with AFR were consistently categorized in the top GRS decile (21.9%-23.6% and 12.9%-14.5%, respectively). Only GRS weighted by ancestry-specific effect estimates were associated with IGS documentation in AFR cases; all GRS types were associated with IGS in EUR cases.

CONCLUSIONS

Varied performance of the GRS for POAG risk stratification and documented IGS association in EUR and AFR Veterans highlights (1) the complex risk architecture of POAG, (2) the importance of diverse representation in genomics studies that inform GRS construction and evaluation, and (3) the necessity of expanding diverse POAG-related genomic data so that GRS can equitably aid in screening individuals at high risk of POAG and who may require more aggressive treatment.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources

OBJECTIVE

The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings.

MATERIALS AND METHODS

Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy.

RESULTS

Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address.

DISCUSSION

Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis

A timely diagnosis is a key challenge for many rare diseases. As an expanding group of rare and severe monogenic disorders with a broad spectrum of clinical manifestations, ciliopathies, notably renal ciliopathies, suffer from important underdiagnosis issues. Our objective is to develop an approach for screening large-scale clinical data warehouses and detecting patients with similar clinical manifestations to those from diagnosed ciliopathy patients. We expect that the top-ranked similar patients will benefit from genetic testing for an early diagnosis. The dependence and relatedness between phenotypes were taken into account in our similarity model through medical concept embedding. The relevance of each phenotype to each patient was also considered by adjusted aggregation of phenotype similarity into patient similarity. A ranking model based on the best-subtype-average similarity was proposed to address the phenotypic overlapping and heterogeneity of ciliopathies. Our results showed that using less than one-tenth of learning sources, our language and center specific embedding provided comparable or better performances than other existing medical concept embeddings. Combined with the best-subtype-average ranking model, our patient-patient similarity-based screening approach was demonstrated effective in two large scale unbalanced datasets containing approximately 10,000 and 60,000 controls with kidney manifestations in the clinical data warehouse (about 2 and 0.4% of prevalence, respectively). Our approach will offer the opportunity to identify candidate patients who could go through genetic testing for ciliopathy. Earlier diagnosis, before irreversible end-stage kidney disease, will enable these patients to benefit from appropriate follow-up and novel treatments that could alleviate kidney dysfunction.

Integration of Biobanks in National eHealth Ecosystems Facilitating Long-Term Longitudinal Clinical-Omics Studies and Citizens' Engagement in Research Through eHealthBioR

Biobanks have long existed to support research activities with BBMRI-ERIC formed as a European research infrastructure supporting the coordination for biobanking with 20 country members and one international organization. Although the benefits of biobanks to the research community are well-established, the direct benefit to citizens is limited to the generic benefit of promoting future research. Furthermore, the advent of General Data Protection Regulation (GDPR) legislation raised a series of challenges for scientific research especially related to biobanking associate activities and longitudinal research studies. Electronic health record (EHR) registries have long existed in healthcare providers. In some countries, even at the national level, these record the state of the health of citizens through time for the purposes of healthcare and data portability between different providers. The potential of EHRs in research is great and has been demonstrated in many projects that have transformed EHR data into retrospective medical history information on participating subjects directly from their physician's collected records; many key challenges, however, remain. In this paper, we present a citizen-centric framework called eHealthBioR, which would enable biobanks to link to EHR systems, thus enabling not just retrospective but also lifelong prospective longitudinal studies of participating citizens. It will also ensure strict adherence to legal and ethical requirements, enabling greater control that encourages participation. Citizens would benefit from the real and direct control of their data and samples, utilizing technology, to empower them to make informed decisions about providing consent and practicing their rights related to the use of their data, as well as by having access to knowledge and data generated from samples they provided to biobanks. This is expected to motivate patient engagement in future research and even leads to participatory design methodologies with citizen/patient-centric designed studies. The development of platforms based on the eHealthBioR framework would need to overcome significant challenges. However, it would shift the burden of addressing these to experts in the field while providing solutions enabling in the long term the lower monetary and time cost of longitudinal studies coupled with the option of lifelong monitoring through EHRs.

Exploring the Use of Genomic and Routinely Collected Data: Narrative Literature Review and Interview Study

Background

Advancing the use of genomic data with routinely collected health data holds great promise for health care and research. Increasing the use of these data is a high priority to understand and address the causes of disease.

Objective

This study aims to provide an outline of the use of genomic data alongside routinely collected data in health research to date. As this field prepares to move forward, it is important to take stock of the current state of play in order to highlight new avenues for development, identify challenges, and ensure that adequate data governance models are in place for safe and socially acceptable progress.

Methods

We conducted a literature review to draw information from past studies that have used genomic and routinely collected data and conducted interviews with individuals who use these data for health research. We collected data on the following: the rationale of using genomic data in conjunction with routinely collected data, types of genomic and routinely collected data used, data sources, project approvals, governance and access models, and challenges encountered.

Results

The main purpose of using genomic and routinely collected data was to conduct genome-wide and phenome-wide association studies. Routine data sources included electronic health records, disease and death registries, health insurance systems, and deprivation indices. The types of genomic data included polygenic risk scores, single nucleotide polymorphisms, and measures of genetic activity, and biobanks generally provided these data. Although the literature search showed that biobanks released data to researchers, the case studies revealed a growing tendency for use within a data safe haven. Challenges of working with these data revolved around data collection, data storage, technical, and data privacy issues.

Conclusions

Using genomic and routinely collected data holds great promise for progressing health research. Several challenges are involved, particularly in terms of privacy. Overcoming these barriers will ensure that the use of these data to progress health research can be exploited to its full potential.

Microinjection quality control in zebrafish model for genetic manipulations

Microinjection technique is one of the essential methodologies that are used widely in zebrafish research. Microinjection is utilized to perform genetic manipulations within the developing zebrafish model. Further, this technique is used to study a wide range of genetic diseases and gene of interest role in early developmental processes. Thus, quality control for microinjection is an essential factor to ensure experimental reproducibility and consistency. In this technical note, in vitro transcribed synthetic mRNA encoding green fluorescence protein (eGFP), and red fluorescent protein (m-cherry) as well as fluorescein and rhodamine fluorescent dyes were injected into a single-cell zebrafish embryo for volume quality control. Given the importance of having quality control system and methodology to yield similar genetic manipulation within the zebrafish embryo:•

We aimed to establish the unified delivery of injected material into zebrafish one cell stage embryo.

•

We aimed to establish consistency of the injected volume into mineral oil droplets that will serve as a quality control parameter to conforms a quality control practice to ensure the reproducibility of the microinjection technique.

•

The calibration of microinjection droplet size resulted in the visualization of fluorescent protein and dyes in the zebrafish embryo with precise volumes of delivered materials under the control of needle opening, injection pressure and time.

Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies

Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.

Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data

With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. To date, several phenome-wide association studies (PheWAS) have been performed on biobank data, which provides comprehensive insights into many aspects of human genetics and biology. Although inspiring, PheWAS on large-scale biobank data encounters new challenges including computational burden, unbalanced phenotypic distribution, and genetic relationship. In this paper, we first discuss these new challenges and their potential impact on data analysis. Then, we summarize approaches that are scalable and robust in GWAS and PheWAS. This review can serve as a practical guide for geneticists, epidemiologists, and other medical researchers to identify genetic variations associated with health-related phenotypes in large-scale biobank data analysis. Meanwhile, it can also help statisticians to gain a comprehensive and up-to-date understanding of the current technical tool development.

Integration of genetic and clinical information to improve imputation of data missing from electronic health records

OBJECTIVE

Clinical data of patients' measurements and treatment history stored in electronic health record (EHR) systems are starting to be mined for better treatment options and disease associations. A primary challenge associated with utilizing EHR data is the considerable amount of missing data. Failure to address this issue can introduce significant bias in EHR-based research. Currently, imputation methods rely on correlations among the structured phenotype variables in the EHR. However, genetic studies have shown that many EHR-based phenotypes have a heritable component, suggesting that measured genetic variants might be useful for imputing missing data. In this article, we developed a computational model that incorporates patients' genetic information to perform EHR data imputation.

MATERIALS AND METHODS

We used the individual single nucleotide polymorphism's association with phenotype variables in the EHR as input to construct a genetic risk score that quantifies the genetic contribution to the phenotype. Multiple approaches to constructing the genetic risk score were evaluated for optimal performance. The genetic score, along with phenotype correlation, is then used as a predictor to impute the missing values.

RESULTS

To demonstrate the method performance, we applied our model to impute missing cardiovascular related measurements including low-density lipoprotein, heart failure, and aortic aneurysm disease in the electronic Medical Records and Genomics data. The integration method improved imputation's area-under-the-curve for binary phenotypes and decreased root-mean-square error for continuous phenotypes.

CONCLUSION

Compared with standard imputation approaches, incorporating genetic information offers a novel approach that can utilize more of the EHR data for better performance in missing data imputation.

"Just tell me what's going on": The views of parents of children with genetic conditions regarding the research use of their child's electronic health record

OBJECTIVE

The purpose of this study was to understand the ethical, legal, and social issues described by parents of children with known or suspected genetic conditions that cause intellectual and developmental disabilities regarding research use of their child's electronic health record (EHR).

MATERIALS AND METHODS

We conducted 4 focus groups with parents of children with a known (n = 12) or suspected (n = 11) genetic condition, as well as 2 comparison groups with parents who had a child with no known genetic condition (n = 15). Focus group transcripts were coded and analyzed using directed content analysis.

RESULTS

After weighing the risks and benefits, parents of children with known or suspected genetic conditions were willing to share their child's EHR for research studies under certain conditions. Preferences were for studies conducted by universities or nonprofits that might benefit their child or others with the same condition. Parents also valued return of research results.

DISCUSSION

Trust, transparency, altruism, and concerns about privacy emerged as factors that affect parents' willingness to allow research use of their child's EHR.

CONCLUSION

Researchers should consider how to build trust with parents by increasing transparency of the research process and explaining specifically how they will ensure the confidentiality of EHR data.

Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data

The 'discovery' stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur 'outside' the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias.

Heritability of the Fibromyalgia Phenotype Varies by Age

OBJECTIVE

Many studies suggest a strong familial component to fibromyalgia (FM). However, those studies have nearly all been confined to individuals with primary FM, i.e., FM without any other accompanying disorder. The current 2011 and 2016 criteria for diagnosing FM construct a score using a combination of the number of painful body sites and the severity of somatic symptoms (FM score). This study was undertaken to estimate the genetic heritability of the FM score across sex and age groups to identify subgroups of individuals with greater heritability, which may help in the design of future genetic studies.

METHODS

We collected data on 26,749 individuals of European ancestry undergoing elective surgery at the University of Michigan (Michigan Genomics Initiative study). We estimated the single-nucleotide polymorphism-based heritability of FM score by age and sex categories using genome-wide association study data and a linear mixed-effects model.

RESULTS

Overall, the FM score had an estimated heritability of 13.9% (SE 2.9%) (P = 1.6 × 10^-7 ). Estimated FM score heritability was highest in individuals ≤50 years of age (23.5%; SE 7.9%) (P = 3.0 ×10^-4 ) and lowest in individuals >60 years of age (7.5%; SE 8.1%) (P = 0.41). These patterns remained the same when we analyzed FM as a case-control phenotype. Even though women had an ~30% higher average FM score than men across age categories, FM score heritability did not differ significantly by sex.

CONCLUSION

Younger individuals appear to have a much stronger genetic component to the FM score than older individuals. Older individuals may be more likely to have what was previously called "secondary FM." Regardless of the cause, these results have implications for future genetic studies of FM and associated conditions.

An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records

Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well-characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease-gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease-gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

Axes of a revolution: challenges and promises of big data in healthcare

Health data are increasingly being generated at a massive scale, at various levels of phenotyping and from different types of resources. Concurrent with recent technological advances in both data-generation infrastructure and data-analysis methodologies, there have been many claims that these events will revolutionize healthcare, but such claims are still a matter of debate. Addressing the potential and challenges of big data in healthcare requires an understanding of the characteristics of the data. Here we characterize various properties of medical data, which we refer to as 'axes' of data, describe the considerations and tradeoffs taken when such data are generated, and the types of analyses that may achieve the tasks at hand. We then broadly describe the potential and challenges of using big data in healthcare resources, aiming to contribute to the ongoing discussion of the potential of big data resources to advance the understanding of health and disease.

A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank

The etiology of most complex diseases involves genetic variants, environmental factors, and gene-environment interaction (G × E) effects. Compared with marginal genetic association studies, G × E analysis requires more samples and detailed measure of environmental exposures, and this limits the possible discoveries. Large-scale population-based biobanks with detailed phenotypic and environmental information, such as UK-Biobank, can be ideal resources for identifying G × E effects. However, due to the large computation cost and the presence of case-control imbalance, existing methods often fail. Here we propose a scalable and accurate method, SPAGE (SaddlePoint Approximation implementation of G × E analysis), that is applicable for genome-wide scale phenome-wide G × E studies. SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis in order to reduce computation cost, and SPAGE uses a saddlepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanced case-control ratios. Simulation studies show that SPAGE is 33-79 times faster than the Wald test and 72-439 times faster than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level even when case-control ratios are extremely unbalanced. Through the analysis of UK-Biobank data of 344,341 white British European-ancestry samples, we show that SPAGE can efficiently analyze large samples while controlling for unbalanced case-control ratios.

Genetics of Atopic Dermatitis: From DNA Sequence to Clinical Relevance

Atopic dermatitis (AD) is a complex disease that is thought to be triggered by environmental factors in genetically susceptible individuals. Twin studies have estimated the heritability of AD to be approximately 75%, with the null (loss-of-function) mutations of the gene encoding filaggrin (FLG) (chromosome 1q21.3) as the strongest known genetic risk factor. The discovery of the filaggrin gene was important in the emerging model for AD pathogenesis, combining skin barrier function with adaptive and innate immunity. Assisted by the recent development of large-scale high-throughput genomics, more than 30 genetic loci have been linked to AD across different populations. Identification of these loci, together with functional studies, has already provided new insights into disease biology and identified novel drug targets. Further, these susceptibility loci are laying the groundwork for phenome-wide association studies to test their multiple phenotype relationships and application of Mendelian randomization to investigate causal relationships. Despite many known genes, a majority of the genetic risk for AD is yet unexplored. Therefore, studies investigating refined phenotype groups, low-frequency and rare genetic variation, gene-gene and/or gene-environment interactions, epigenetic mechanisms and data from multi-omics technologies are warranted. In this review, we describe genetic discoveries for AD, including results from candidate gene studies, studies of AD-like genetic diseases, genome-wide association studies and genetic sequencing studies. We explain how some of these genetic discoveries have unraveled new mechanistic insights into the pathogenesis of AD and exemplify how personal genetic data could be used for preventive strategies and a tailored treatment regimen (i.e., precision medicine).

African American ancestry contribution to asthma and atopic dermatitis

OBJECTIVE

Asthma and atopic dermatitis (AD) are complex diseases with striking disparities across racial and ethnic groups, which may be partly attributable to genetic factors. Here we summarize current knowledge from asthma and AD genome-wide association studies (GWAS) and pharmacogenetic studies in African ancestry populations.

DATA SOURCES

GWAS catalog; PUBMed.

STUDY SELECTIONS

GWAS catalog studies with trait annotations "asthma" and "atopic eczema" and African ancestry individuals in the discovery dataset; the recent CAAPA asthma GWAS; reports on pharmacogenetic studies in asthma and AD.

RESULTS

Although GWASs have revolutionized gene discovery for multiple complex traits, African Americans continue to be severely underrepresented in sufficiently powered genetics studies. Indeed, of the 16 asthma and 21 AD loci that reached genomewide significance in Europeans, very few have replicated in African ancestry populations. Challenges in comparing results from European vs African ancestry cohorts include modest sample size, differences in risk allele frequency, effect size, correlation between genetic variants, and environmental exposure in evolutionary history. African Americans also constitute a small percentage of dermatological and respiratory-focused clinical trials. Pharmacogenetic studies have similarly been focused largely on non-Hispanic whites, despite compelling evidence that genetic variation from different ancestral backgrounds may alter therapeutic efficacy of asthma and AD drugs.

CONCLUSION

Large-scale genetic studies of asthma and AD in African Americans are essential to reduce research and health disparities and empower scientific discoveries.

Personalized Medicine and the Power of Electronic Health Records

Personalized medicine has largely been enabled by the integration of genomic and other data with electronic health records (EHRs) in the United States and elsewhere. Increased EHR adoption across various clinical settings and the establishment of EHR-linked population-based biobanks provide unprecedented opportunities for the types of translational and implementation research that drive personalized medicine. We review advances in the digitization of health information and the proliferation of genomic research in health systems and provide insights into emerging paths for the widespread implementation of personalized medicine.

GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish

Although the use of model systems for studying the mechanism of mutations that have a large effect is common, we highlight here the ways that zebrafish-model-system studies of a gene, GRIK5, that contributes to the polygenic liability to develop eye diseases have helped to illuminate a mechanism that implicates vascular biology in eye disease. A gene-expression prediction derived from a reference transcriptome panel applied to BioVU, a large electronic health record (EHR)-linked biobank at Vanderbilt University Medical Center, implicated reduced GRIK5 expression in diverse eye diseases. We tested the function of GRIK5 by depletion of its ortholog in zebrafish, and we observed reduced blood vessel numbers and integrity in the eye and increased vascular permeability. Analyses of EHRs in >2.6 million Vanderbilt subjects revealed significant comorbidity of eye and vascular diseases (relative risks 2-15); this comorbidity was confirmed in 150 million individuals from a large insurance claims dataset. Subsequent studies in >60,000 genotyped BioVU participants confirmed the association of reduced genetically predicted expression of GRIK5 with comorbid vascular and eye diseases. Our studies pioneer an approach that allows a rapid iteration of the discovery of gene-phenotype relationships to the primary genetic mechanism contributing to the pathophysiology of human disease. Our findings also add dimension to the understanding of the biology driven by glutamate receptors such as GRIK5 (also referred to as GLUK5 in protein form) and to mechanisms contributing to human eye diseases.

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations.

Targeted Searches of the Electronic Health Record and Genomics Identify an Etiology in Three Patients with Short Stature and High IGF-I Levels

INTRODUCTION

Short stature is one of the most common reasons for referral to a pediatric endocrinologist and can result from many etiologies. However, many patients with short stature do not receive a definitive diagnosis.

OBJECTIVE

To ascertain whether integrating targeted bioinformatics searches of electronic health records (EHRs) combined with genomic studies could identify patients with previously undiagnosed rare genetic etiologies of short stature. We focused on a specific rare phenotypic subgroup: patients with short stature and elevated IGF-I levels.

METHODS

We performed a cross-sectional cohort study at three large academic pediatric healthcare networks. Eligible subjects included children with heights below -2 SD, IGF-I levels >90th percentile, and no known etiology for short stature. We performed a search of the EHRs to identify eligible patients. Patients were then recruited for phenotyping followed by exome sequencing and in vitro assays of IGF1R function.

RESULTS

A total of 234 patients were identified by the bioinformatics algorithm with 39 deemed eligible after manual review (17%). Of those, 9 were successfully recruited. A genetic etiology was identified in 3 of the 9 patients including 2 novel variants in IGF1R and a de novo variant in CHD2. In vitro studies supported the pathogenicity of the IGF1R variants.

CONCLUSIONS

This study provides proof of principle that patients with rare phenotypic subgroups can be identified based on discrete data elements in the EHRs. Although limitations exist to fully automating this approach, these searches may help find patients with previously unidentified rare genetic disorders.

Fishing forward and reverse: Advances in zebrafish phenomics

Understanding how the genome instructs the phenotypic characteristics of an organism is one of the major scientific endeavors of our time. Advances in genetics have progressively deciphered the inheritance, identity and biological relevance of genetically encoded information, contributing to the rise of several, complementary omic disciplines. One of them is phenomics, an emergent area of biology dedicated to the systematic multi-scale analysis of phenotypic traits. This discipline provides valuable gene function information to the rapidly evolving field of genetics. Current molecular tools enable genome-wide analyses that link gene sequence to function in multi-cellular organisms, illuminating the genome-phenome relationship. Among vertebrates, zebrafish has emerged as an outstanding model organism for high-throughput phenotyping and modeling of human disorders. Advances in both systematic mutagenesis and phenotypic analyses of embryonic and post-embryonic stages in zebrafish have revealed the function of a valuable collection of genes and the general structure of several complex traits. In this review, we summarize multiple large-scale genetic efforts addressing parental, embryonic, and adult phenotyping in the zebrafish. The genetic and quantitative tools available in the zebrafish model, coupled with the broad spectrum of phenotypes that can be assayed, make it a powerful model for phenomics, well suited for the dissection of genotype-phenotype associations in development, physiology, health and disease.

Aging is an adaptation that selects in animals against disruption of homeostasis

During evolution, Muller's ratchet permanently generates deleterious germline mutations that eventually must be defused by selection. It seems widely held that cancer and aging-related diseases (ARDs) cannot contribute to this germline gene selection because they tail reproduction and thus occur too late, at the end of the life cycle. Here we posit however that by lessening the offspring's survival by proxy through diminishing parental care, they can still contribute to the selection. The hypothesis in detail: The widespread occurrence of aging in animals suggests that it is an adaptation. But to what benefit? Aging seems to have only drawbacks. In humans, ARDs cause today almost all mortality; they include heart disease, cerebrovascular disease, Alzheimer's disease, kidney disease and cancer. Compensation seems unthinkable. For cancer, the author proposed in a previous study a benefit to the species: purifying selection against deleterious germline genes that when expressed enhance intracellular energy dissipation. This multicausal energy dissipation, posited as the universal origin of cancer initiation, relates to cellular heat generation, disrupted metabolism, and inflammation. The organism reproduces during cancer's dormancy, and when approaching its end of life, the onset of cancer is accelerated in proportion to the cancer-initiating signal. Through cancer, the organism, now a parent, implements the self-actuated programmed death of Skulachev's phenoptosis. This "first death" enhances by proxy the offspring's chance of "second death" (or "double death") through diminished parental care. Repetition over generations realizes a purifying selection against genes causing energy dissipation. The removal of the deleterious germline gene mutations permanently generated by Muller's ratchet gives a benefit. We generalize, motivated by the parallels between cancer and aging, the purifying selection posited for cancer to aging. An ARD would be initiated in the organ by multicausal disruption of homeostasis, and be followed by dormancy and senescence until its onset near the end of the life cycle. Just as for cancer, the ARD eventually enhances double death, and the realized permanent selection gives a benefit to the species through the selection against germ line genes that disrupt homeostasis. Given their similarities, cancer and aging are combined in the posited Unified Cancer-Aging Adaptation (UCAA) model, which may be confirmed by next-generation sequencing data. Also because of the emerging important role of cellular senescence, the hypothesis may guide the development of therapies against both cancer and aging.