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

The evolution in our understanding of the genetics of rheumatoid arthritis and the impact on novel drug discovery

Introduction: Rheumatoid arthritis (RA) is an autoimmune disease that is characterized by chronic inflammation of the joints and affects 1% of the population. Polymorphisms of genes that encode proteins that primarily participate in inflammation may influence RA occurrence or become useful biomarkers for certain types of anti-rheumatic treatment.Areas covered: The authors summarize the recent progress in our understanding of the genetics of RA. In the last few years, multiple variants of genes that are associated with RA risk have been identified. The development of new technologies and the detection of new potential therapeutic targets that contribute to novel drug discovery are also described.Expert opinion: There is still the need to search for new genes which may be a potential target for RA therapy. The challenge is to develop appropriate strategies for achieving insight into the molecular pathways involved in RA pathogenesis. Understanding the genetics, immunogenetics, epigenetics and immunology of RA could help to identify new targets for RA therapy. The development of new technologies has enabled the detection of a number of new genes, particularly genes associated with proinflammatory cytokines and chemokines, B- and T-cell activation pathways, signal transducers and transcriptional activators, which might be potential therapeutic targets in RA.

Psychiatric Genetics, Epigenetics, and Cellular Models in Coming Years

Psychiatric genetic studies have uncovered hundreds of loci associated with various psychiatric disorders. We take the opportunity to review achievements in the past and provide our view of what is coming in the fields of molecular genetics, epigenetics, and cellular models. We expect that SNP-array and sequencing-based studies of genetic associations will continue to expand, covering more disorders, drug responses, phenotypes, and diverse populations. Epigenetic studies of psychiatric disorders will be another promising field with the growing recognition that environmental factors impact the risk for psychiatric disorders by modulating epigenetic factors. Functional studies of genetic findings will be needed in cellular models to provide important connections between genetic and epigenetic variants and biological phenotypes.

Body Shape and Alzheimer's Disease: A Mendelian Randomization Analysis

Obesity has been reported to be related to memory impairment and decline in cognitive function, possibly further leading to the development of Alzheimer’s disease (AD). However, observational studies revealed both negative and positive associations between body shape (BS) and AD, thereby making it difficult to confirm causality due to residual confounds and reverse causation. Thus, using genome-wide association study summary data, two-sample Mendelian randomization (MR) analyses were applied to identify whether there exists a causal association between BS and AD. BS was measured using anthropometric traits (ATs) in this study, including body mass index (BMI), waist-to-hip ratio (WHR), waist-to-hip ratio adjusted by body mass index (WHRadjBMI), and waist circumference (WC). The associations of single nucleotide polymorphisms (SNP) with each AT and AD were obtained separately from aggregated data from the Genetic Investigation of Anthropometric Traits (GIANT) consortium and International Genomics of Alzheimer’s Project (IGAP) summary data (17,008 cases with AD and 37,154 controls). An inverse-variance weighted method was applied to obtain the overall causal estimate for multiple instrumental SNPs. The odds ratio (OR) [95% confidence interval (CI)] for AD risk per 1-SD difference in BMI was 1.04 (0.88, 1.23), in WHR was 1.01 (0.77, 1.33), in WHRadjBMI was 1.12 (0.89, 1.41), and in WC was 1.02 (0.82, 1.27). Furthermore, simulation analyses of survivor bias indicated the overall causal effect of BMI on risk of AD was not biased. In conclusion, the evidence from MR analyses showed no casual effect of BS on AD risk, which is inconsistent with the results from previous observational studies. The biological mechanism underlying the findings warrants further study.

Use of Natural Language Processing Tools to Identify and Classify Periprosthetic Femur Fractures

BACKGROUND

Manual chart review is labor-intensive and requires specialized knowledge possessed by highly trained medical professionals. The cost and infrastructure challenges required to implement this is prohibitive for most hospitals. Natural language processing (NLP) tools are distinctive in their ability to extract critical information from unstructured text in the electronic health records. As a simple proof-of-concept for the potential application of NLP technology in total hip arthroplasty (THA), we examined its ability to identify periprosthetic femur fractures (PPFFx) followed by more complex Vancouver classification.

METHODS

PPFFx were identified among all THAs performed at a single academic institution between 1998 and 2016. A randomly selected training cohort (1538 THAs with 89 PPFFx cases) was used to develop the prototype NLP algorithm and an additional randomly selected cohort (2982 THAs with 84 PPFFx cases) was used to further validate the algorithm. Keywords to identify, and subsequently classify, Vancouver type PPFFx about THA were defined. The gold standard was confirmed by experienced orthopedic surgeons using chart and radiographic review. The algorithm was applied to consult and operative notes to evaluate language used by surgeons as a means to predict the correct pathology in the absence of a listed, precise diagnosis. Given the variability inherent to fracture descriptions by different surgeons, an iterative process was used to improve the algorithm during the training phase following error identification. Validation statistics were calculated using manual chart review as the gold standard.

RESULTS

In distinguishing PPFFx, the NLP algorithm demonstrated 100% sensitivity and 99.8% specificity. Among 84 PPFFx test cases, the algorithm demonstrated 78.6% sensitivity and 94.8% specificity in determining the correct Vancouver classification.

CONCLUSION

NLP-enabled algorithms are a promising alternative to manual chart review for identifying THA outcomes. NLP algorithms applied to surgeon notes demonstrated excellent accuracy in delineating PPFFx, but accuracy was low for Vancouver classification subtype. This proof-of-concept study supports the use of NLP technology to extract THA-specific data elements from the unstructured text in electronic health records in an expeditious and cost-effective manner.

LEVEL OF EVIDENCE

Level III.

Druggable Transcriptional Networks in the Human Neurogenic Epigenome

Chromosome conformation capture methods have revealed the dynamics of genome architecture which is spatially organized into topologically associated domains, with gene regulation mediated by enhancer-promoter pairs in chromatin space. New evidence shows that endogenous hormones and several xenobiotics act within circumscribed topological domains of the spatial genome, impacting subsets of the chromatin contacts of enhancer-gene promoter pairs in cis and trans Results from the National Institutes of Health-funded PsychENCODE project and the study of chromatin remodeling complexes have converged to provide a clearer understanding of the organization of the neurogenic epigenome in humans. Neuropsychiatric diseases, including schizophrenia, bipolar spectrum disorder, autism spectrum disorder, attention deficit hyperactivity disorder, and other neuropsychiatric disorders are significantly associated with mutations in neurogenic transcriptional networks. In this review, we have reanalyzed the results from publications of the PsychENCODE Consortium using pharmacoinformatics network analysis to better understand druggable targets that control neurogenic transcriptional networks. We found that valproic acid and other psychotropic drugs directly alter these networks, including chromatin remodeling complexes, transcription factors, and other epigenetic modifiers. We envision a new generation of CNS therapeutics targeted at neurogenic transcriptional control networks, including druggable parts of chromatin remodeling complexes and master transcription factor-controlled pharmacogenomic networks. This may provide a route to the modification of interconnected gene pathways impacted by disease in patients with neuropsychiatric and neurodegenerative disorders. Direct and indirect therapeutic strategies to modify the master regulators of neurogenic transcriptional control networks may ultimately help extend the life span of CNS neurons impacted by disease.

Phenome-wide Mendelian-randomization study of genetically determined vitamin D on multiple health outcomes using the UK Biobank study

BACKGROUND

Vitamin D deficiency is highly prevalent across the globe. Existing studies suggest that a low vitamin D level is associated with more than 130 outcomes. Exploring the causal role of vitamin D in health outcomes could support or question vitamin D supplementation.

METHODS

We carried out a systematic literature review of previous Mendelian-randomization studies on vitamin D. We then implemented a Mendelian Randomization-Phenome Wide Association Study (MR-PheWAS) analysis on data from 339 256 individuals of White British origin from UK Biobank. We first ran a PheWAS analysis to test the associations between a 25(OH)D polygenic risk score and 920 disease outcomes, and then nine phenotypes (i.e. systolic blood pressure, diastolic blood pressure, risk of hypertension, T2D, ischaemic heart disease, body mass index, depression, non-vertebral fracture and all-cause mortality) that met the pre-defined inclusion criteria for further analysis were examined by multiple MR analytical approaches to explore causality.

RESULTS

The PheWAS analysis did not identify any health outcome associated with the 25(OH)D polygenic risk score. Although a selection of nine outcomes were reported in previous Mendelian-randomization studies or umbrella reviews to be associated with vitamin D, our MR analysis, with substantial study power (>80% power to detect an association with an odds ratio >1.2 for per standard deviation increase of log-transformed 25[OH]D), was unable to support an interpretation of causal association.

CONCLUSIONS

We investigated the putative causal effects of vitamin D on multiple health outcomes in a White population. We did not support a causal effect on any of the disease outcomes tested. However, we cannot exclude small causal effects or effects on outcomes that we did not have enough power to explore due to the small number of cases.

DNA methylation profiles are associated with complex regional pain syndrome after traumatic injury

Factors contributing to development of complex regional pain syndrome (CRPS) are not fully understood. This study examined possible epigenetic mechanisms that may contribute to CRPS after traumatic injury. DNA methylation profiles were compared between individuals developing CRPS (n = 9) and those developing non-CRPS neuropathic pain (n = 38) after undergoing amputation following military trauma. Linear Models for Microarray (LIMMA) analyses revealed 48 differentially methylated cytosine-phosphate-guanine dinucleotide (CpG) sites between groups (unadjusted P's < 0.005), with the top gene COL11A1 meeting Bonferroni-adjusted P < 0.05. The second largest differential methylation was observed for the HLA-DRB6 gene, an immune-related gene linked previously to CRPS in a small gene expression study. For all but 7 of the significant CpG sites, the CRPS group was hypomethylated. Numerous functional Gene Ontology-Biological Process categories were significantly enriched (false discovery rate-adjusted q value <0.15), including multiple immune-related categories (eg, activation of immune response, immune system development, regulation of immune system processes, and antigen processing and presentation). Differentially methylated genes were more highly connected in human protein-protein networks than expected by chance (P < 0.05), supporting the biological relevance of the findings. Results were validated in an independent sample linking a DNA biobank with electronic health records (n = 126 CRPS phenotype, n = 19,768 non-CRPS chronic pain phenotype). Analyses using PrediXcan methodology indicated differences in the genetically determined component of gene expression in 7 of 48 genes identified in methylation analyses (P's < 0.02). Results suggest that immune- and inflammatory-related factors might confer risk of developing CRPS after traumatic injury. Validation findings demonstrate the potential of using electronic health records linked to DNA for genomic studies of CRPS.

Contextual Deep Regression Network for Volume Estimation in Orbital CT

Diseases of the optic nerve cause structural changes observable through clinical computed tomography (CT) imaging. Previous work has shown that multi-atlas methods can be used to segment and extract volumetric measurements from the optic nerve, which are associated with visual disability and disease. In this work, we trained a weakly supervised convolutional neural network to learn optic nerve volumes directly, without segmentation. Furthermore, we explored the role of contextual electronic medical record (EMR) information, specifically ICD-9 codes, to improve optic nerve volume estimation. We constructed a merged network to combine data from imaging as well as EMR and demonstrated that context improved volume prediction, with a 15% increase in explained-variance ( R ²). Finally, we compared disease prediction models using volumes learned from multi-atlas, CNN, and contextual-CNN. We observed that the predicted optic nerve volume from merge-CNN had an AUC of 0.74 for classification of disease, as compared to an AUC of 0.54 using the multi-atlas metric. This is the first work to show that a contextually derived volume biomarker is more accurate than volume estimations through multi-atlas or weakly supervised image CNN. These results highlight the potential for image processing improvements by incorporating non-imaging data.

A regression framework to uncover pleiotropy in large-scale electronic health record data

OBJECTIVE

Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype-phenotype relationship. Although individual genotype-phenotype associations have been thoroughly explored, seemingly unrelated phenotypes can be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy have been challenged by both methodologic limitations and a lack of available suitable data sources.

MATERIALS AND METHODS

In this study, we propose to utilize a new regression framework, reduced rank regression, to simultaneously analyze multiple phenotypes and genotypes to detect pleiotropic effects. We used a large-scale biobank linked electronic health record data from the Penn Medicine BioBank to select 5 cardiovascular diseases (hypertension, cardiac dysrhythmias, ischemic heart disease, congestive heart failure, and heart valve disorders) and 5 mental disorders (mood disorders; anxiety, phobic and dissociative disorders; alcohol-related disorders; neurological disorders; and delirium dementia) to validate our framework.

RESULTS

Compared with existing methods, reduced rank regression showed a higher power to distinguish known associated single-nucleotide polymorphisms from random single-nucleotide polymorphisms. In addition, genome-wide gene-based investigation of pleiotropy showed that reduced rank regression was able to identify candidate genetic variants with novel pleiotropic effects compared to existing methods.

CONCLUSION

The proposed regression framework offers a new approach to account for the phenotype and genotype correlations when identifying pleiotropic effects. By jointly modeling multiple phenotypes and genotypes together, the method has the potential to distinguish confounding from causal genotype and phenotype associations.

Probing the Virtual Proteome to Identify Novel Disease Biomarkers

BACKGROUND

Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals.

METHODS

We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651).

RESULTS

In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β.

CONCLUSIONS

We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

Target discovery using biobanks and human genetics

Large-scale biobanks can yield unprecedented insights into our health and provide discoveries of new and potentially targetable biomarkers. Several protective loss-of-function alleles have been identified, including variants that protect against cardiovascular disease, obesity, type 2 diabetes, and asthma and allergic diseases. These alleles serve as indicators of efficacy, mimicking the effects of drugs and suggesting that inhibiting these genes could provide therapeutic benefit, as has been observed for PCSK9. We provide a context for these findings through a multifaceted review covering the use of genetics in drug discovery efforts through genome-wide and phenome-wide association studies, linking deep mutation scanning data to molecular function and highlighting some additional tools that might help in the interpretation of newly discovered variants.

Evaluating statistical approaches to leverage large clinical datasets for uncovering therapeutic and adverse medication effects

Motivation

Phenome-wide association studies (PheWAS) have been used to discover many genotype-phenotype relationships and have the potential to identify therapeutic and adverse drug outcomes using longitudinal data within electronic health records (EHRs). However, the statistical methods for PheWAS applied to longitudinal EHR medication data have not been established.

Results

In this study, we developed methods to address two challenges faced with reuse of EHR for this purpose: confounding by indication, and low exposure and event rates. We used Monte Carlo simulation to assess propensity score (PS) methods, focusing on two of the most commonly used methods, PS matching and PS adjustment, to address confounding by indication. We also compared two logistic regression approaches (the default of Wald versus Firth's penalized maximum likelihood, PML) to address complete separation due to sparse data with low exposure and event rates. PS adjustment resulted in greater power than PS matching, while controlling Type I error at 0.05. The PML method provided reasonable P-values, even in cases with complete separation, with well controlled Type I error rates. Using PS adjustment and the PML method, we identify novel latent drug effects in pediatric patients exposed to two common antibiotic drugs, ampicillin and gentamicin.

Availability and implementation

R packages PheWAS and EHR are available at https://github.com/PheWAS/PheWAS and at CRAN (https://www.r-project.org/), respectively. The R script for data processing and the main analysis is available at https://github.com/choileena/EHR.

Supplementary information

Supplementary data are available at Bioinformatics online.

Motivation for Launching a Cancer Metastasis Inhibition (CMI) Program

Metastatic cancers impose significant burdens on patients, affecting quality of life, morbidity, and mortality. Even during remission, microscopic metastases can lurk, but few therapies directly target tumor cell metastasis. Agents that interfere with this process would represent a new paradigm in cancer management, changing the 'waiting game' into a time of active prevention. These therapies could take multiple forms based on the pathways involved in the metastatic process. For example, a phenome-wide association study showed that a single nucleotide polymorphism in the gene TBXA2R is associated with increased metastasis in multiple primary cancers (P = 0.003), suggesting clinical applicability of TBXA2R antagonists. Emerging data related to the role of platelets in metastasis are concordant with our sense that these pathways present significant opportunities for therapeutic development. However, before real progress can be made toward clinical targeting of the metastatic process, foundational work is needed to define informative measures of critical elements such as circulating tumor cells and tumor DNA, and circulatory vs. lymphatic spread. These challenges require an expansion of team science and composition to obtain competitive funding. At our academic medical center, we have implemented a Cancer Metastasis Inhibition (CMI) program investigating this approach across multiple cancers.

Using phenome-wide association studies to examine the effect of environmental exposures on human health

The field of environmental epidemiology has been using "-omics" technologies, including the exposome, metabolome, and methylome, to understand the potential effects and biological pathways of a number of environmental pollutants. However, the majority of studies have focused on a single disease or phenotype, and have not systematically considered patterns of multimorbidity and whether environmental pollutants have pleiotropic effects. These questions could be addressed by examining the relation between environmental exposures and the phenome - the patterns and profiles of human health that individuals experience from birth to death. By conducting Phenome Wide Association Studies (PheWAS), we can generate new hypotheses about new or poorly understood exposures, identify novel associations for established toxicants, and better understand biological pathways affected by environmental pollutants. In this article, we provide a conceptual framework for conducting PheWAS in environmental epidemiology and summarize some of the advantages and challenges to using the PheWAS to study environmental pollutant exposures. Ultimately, by adding the PheWAS to our "-omics" toolbox, we could substantially improve our understanding of the potential health effects of environmental pollutants.

Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior

Twin and family studies have shown that same-sex sexual behavior is partly genetically influenced, but previous searches for specific genes involved have been underpowered. We performed a genome-wide association study (GWAS) on 477,522 individuals, revealing five loci significantly associated with same-sex sexual behavior. In aggregate, all tested genetic variants accounted for 8 to 25% of variation in same-sex sexual behavior, only partially overlapped between males and females, and do not allow meaningful prediction of an individual's sexual behavior. Comparing these GWAS results with those for the proportion of same-sex to total number of sexual partners among nonheterosexuals suggests that there is no single continuum from opposite-sex to same-sex sexual behavior. Overall, our findings provide insights into the genetics underlying same-sex sexual behavior and underscore the complexity of sexuality.

Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program

Chronic kidney disease (CKD), defined by low estimated glomerular filtration rate (eGFR), contributes to global morbidity and mortality. Here we conduct a transethnic Genome-Wide Association Study of eGFR in 280,722 participants of the Million Veteran Program (MVP), with replication in 765,289 participants from the Chronic Kidney Disease Genetics (CKDGen) Consortium. We identify 82 previously unreported variants, confirm 54 loci, and report interesting findings including association of the sickle cell allele of betaglobin among non-Hispanic blacks. Our transcriptome-wide association study of kidney function in healthy kidney tissue identifies 36 previously unreported and nine known genes, and maps gene expression to renal cell types. In a Phenome-Wide Association Study in 192,868 MVP participants using a weighted genetic score we detect associations with CKD stages and complications and kidney stones. This investigation reinterprets the genetic architecture of kidney function to identify the gene, tissue, and anatomical context of renal homeostasis and the clinical consequences of dysregulation.

Precision oncology: lessons learned and challenges for the future

The decreasing cost of and increasing capacity of DNA sequencing has led to vastly increased opportunities for population-level genomic studies to discover novel genomic alterations associated with both Mendelian and complex phenotypes. To translate genomic findings clinically, a number of health care institutions have worked collaboratively or individually to initiate precision medicine programs. These precision medicine programs involve designing patient enrollment systems, tracking electronic health records, building biobank repositories, and returning results with actionable matched therapies. As cancer is a paradigm for genetic diseases and new therapies are increasingly tailored to attack genetic susceptibilities in tumors, these precision medicine programs are largely driven by the urgent need to perform genetic profiling on cancer patients in real time. Here, we review the current landscape of precision oncology and highlight challenges to be overcome and examples of benefits to patients. Furthermore, we make suggestions to optimize future precision oncology programs based upon the lessons learned from these "first generation" early adopters.

Interdisciplinary Models for Research and Clinical Endeavors in Genomic Medicine: A Scientific Statement From the American Heart Association

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition.

Family History-Wide Association Study to Identify Clinical and Environmental Risk Factors for Common Chronic Diseases

Family history is a strong risk factor for many common chronic diseases and summarizes shared environmental and genetic risk, but how this increased risk is mediated is unknown. We developed a "family history-wide association study" (FamWAS) to systematically and comprehensively test clinical and environmental quantitative traits (CEQTs) for their association with family history of disease. We implemented our method on 457 CEQTs for association with family history of diabetes, asthma, and coronary heart disease (CHD) in 42,940 adults spanning 8 waves of the 1999-2014 US National Health and Nutrition Examination Survey. We conducted pooled analyses of the 8 survey waves and analyzed trait associations using survey-weighted logistic regression. We identified 172 (37.6% of total), 32 (7.0%), and 78 (17.1%) CEQTs associated with family history of diabetes, asthma, and CHD, respectively, in subcohorts of individuals without the respective disease. Twenty associated CEQTs were shared across family history of diabetes, asthma, and CHD, far more than expected by chance. FamWAS can examine traits not previously studied in association with family history and uncover trait overlap, highlighting a putative shared mechanism by which family history influences disease risk.

Data-driven phenotype discovery of FMR1 premutation carriers in a population-based sample

The impact of the FMR1 premutation on human health is the subject of considerable controversy. A fundamental unanswered question is whether carrying the premutation allele is directly correlated with clinical phenotypes. A challenging problem in past genotype-phenotype studies of the FMR1 premutation is ascertainment bias, which could lead to invalid research conclusions and negatively affect clinical practice. Here, we created the first population-based FMR1-informed biobank to find the pattern of health characteristics in premutation carriers. Our extensive phenotyping shows that premutation carriers experience a clinical profile that is significantly different from controls and is evident throughout adulthood. Comprehensive understanding of the clinical risk associated with this genetic variant is critical for premutation carriers, their families, and clinicians and has important implications for public health.

Challenges and opportunities in stroke genetics

Stroke, ischaemic stroke and subtypes of ischaemic stroke display substantial heritability. When compared with related vascular conditions, the number of established risk loci reaching genome-wide significance for association with stroke is still in the lower range, particularly for aetiological stroke subtypes such as large artery atherosclerotic stroke or small vessel stroke. Nevertheless, for individual loci substantial progress has been made in determining the specific mechanisms mediating stroke risk. In this review, we present a roadmap for functional follow-up of common risk variants associated with stroke. First, we discuss in silico strategies for characterizing signals in non-coding regions and highlight databases providing information on quantitative trait loci for mRNA and protein expression, as well as methylation, focussing on those with presumed relevance for stroke. Next, we discuss experimental strategies for following up on non-coding risk variants and regions such as massively parallel reporter assays, proteome-wide association studies, and chromatin conformation capture (3C) assays. These and other approaches are relevant for gaining insight into the specific variants and mechanisms mediating genetic stroke risk. Finally, we discuss how genetic findings could influence clinical practice by adding to diagnostic algorithms and eventually improve treatment options for stroke.

Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data

The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.

GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across eMERGE Network

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition.

METHODS

First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI).

RESULTS

Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p = 1.70 × 10- 20). This effect was consistent in both pediatric (p = 9.92 × 10- 6) and adult (p = 9.73 × 10- 15) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) (p = 3.94 × 10- 8, beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout (p = 1.09 × 10- 4). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p = 3.80 × 10- 8), and another near ZFP90-CDH1 for fibrosis (rs698718, p = 2.74 × 10- 11). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses.

CONCLUSIONS

In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings.

Phenome-Wide Association Studies Uncover a Novel Association of Increased Atrial Fibrillation in Male Patients With Systemic Lupus Erythematosus

OBJECTIVE

Phenome-wide association studies (PheWAS) scan across billing codes in the electronic health record (EHR) and re-purpose clinical EHR data for research. In this study, we examined whether PheWAS could function as an EHR-based discovery tool for systemic lupus erythematosus (SLE) and identified novel clinical associations in male versus female patients with SLE.

METHODS

We used a de-identified version of the Vanderbilt University Medical Center EHR, which includes more than 2.8 million subjects. We performed EHR-based PheWAS to compare SLE patients with age-, sex-, and race-matched control subjects and to compare male SLE patients with female SLE patients, controlling for multiple testing using a false discovery rate (FDR) P value of 0.05.

RESULTS

We identified 1,097 patients with SLE and 5,735 matched control subjects. In a comparison of patients with SLE and matched controls, SLE patients were shown to be more likely to have International Classification of Diseases, Ninth Revision codes related to the SLE disease criteria. In the PheWAS of male versus female SLE patients, with adjustment for age and race, male patients were shown to be more likely to have atrial fibrillation (odds ratio 4.50, false discovery rate P = 3.23 × 10^-3 ). Chart review confirmed atrial fibrillation, with the majority of patients developing atrial fibrillation after the SLE diagnosis and having multiple risk factors for atrial fibrillation. After adjustment for age, sex, race, and coronary artery disease, SLE disease status was shown to be significantly associated with atrial fibrillation (P = 0.002).

CONCLUSION

Using PheWAS to compare male and female patients with SLE, we identified a novel association of an increased incidence of atrial fibrillation in male patients. SLE disease status was shown to be independently associated with atrial fibrillation, even after adjustment for age, sex, race, and coronary artery disease. These results demonstrate the utility of PheWAS as an EHR-based discovery tool for SLE.

Disease associations depend on visit type: results from a visit-wide association study

INTRODUCTION

Widespread adoption of Electronic Health Records (EHR) increased the number of reported disease association studies, or Phenome-Wide Association Studies (PheWAS). Traditional PheWAS studies ignore visit type (i.e., department/service conducting the visit). In this study, we investigate the role of visit type on disease association results in the first Visit-Wide Association Study or 'VisitWAS'.

RESULTS

We studied this visit type effect on association results using EHR data from the University of Pennsylvania. Penn EHR data comes from 1,048 different departments and clinics. We analyzed differences between cancer and obstetrics/gynecologist (Ob/Gyn) visits. Some findings were expected (i.e., increase of neoplasm diagnoses among cancer visits), but others were surprising, including an increase in infectious disease conditions among those visiting the Ob/Gyn.

CONCLUSION

We conclude that assessing visit type is important for EHR studies because different medical centers have different visit type distributions. To increase reproducibility among EHR data mining algorithms, we recommend that researchers report visit type in studies.

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings

In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine.

Genome-wide association study of peripheral artery disease in the Million Veteran Program

Peripheral artery disease (PAD) is a leading cause of cardiovascular morbidity and mortality¹; however, the extent to which genetic factors increase risk for PAD is largely unknown. Using electronic health record data, we performed a genome-wide association study in the Million Veteran Program testing ~32 million DNA sequence variants with PAD (31,307 cases and 211,753 controls) across veterans of European, African, and Hispanic ancestry. The results were replicated in an independent sample of 5,117 PAD cases and 389,291 controls from UK Biobank. We identified 19 PAD loci, 18 of which have not been previously reported. 11 of the 19 loci were associated with disease in three vascular beds (coronary, cerebral, peripheral), including LDLR, LPL, and LPA, suggesting that therapeutic modulation of LDL cholesterol, the LPL pathway or circulating lipoprotein(a) may be efficacious for multiple atherosclerotic disease phenotypes. Conversely, 4 of the variants appeared to be specific for PAD, including F5 p.R506Q, highlighting the pathogenic role of thrombosis in the peripheral vascular bed and providing genetic support for Factor Xa inhibition as a therapeutic strategy for PAD. Our results highlight mechanistic similarities and differences among coronary, cerebral, and peripheral atherosclerosis and provide therapeutic insights.

Genomics-First Evaluation of Heart Disease Associated With Titin-Truncating Variants

BACKGROUND

Truncating variants in the Titin gene (TTNtvs) are common in individuals with idiopathic dilated cardiomyopathy (DCM). However, a comprehensive genomics-first evaluation of the impact of TTNtvs in different clinical contexts, and the evaluation of modifiers such as genetic ancestry, has not been performed.

METHODS

We reviewed whole exome sequence data for >71 000 individuals (61 040 from the Geisinger MyCode Community Health Initiative (2007 to present) and 10 273 from the PennMedicine BioBank (2013 to present) to identify anyone with TTNtvs. We further selected individuals with TTNtvs in exons highly expressed in the heart (proportion spliced in [PSI] >0.9). Using linked electronic health records, we evaluated associations of TTNtvs with diagnoses and quantitative echocardiographic measures, including subanalyses for individuals with and without DCM diagnoses. We also reviewed data from the Jackson Heart Study to validate specific analyses for individuals of African ancestry.

RESULTS

Identified with a TTNtv in a highly expressed exon (hiPSI) were 1.2% individuals in PennMedicine BioBank and 0.6% at Geisinger. The presence of a hiPSI TTNtv was associated with increased odds of DCM in individuals of European ancestry (odds ratio [95% CI]: 18.7 [9.1-39.4] {PennMedicine BioBank} and 10.8 [7.0-16.0] {Geisinger}). hiPSI TTNtvs were not associated with DCM in individuals of African ancestry, despite a high DCM prevalence (odds ratio, 1.8 [0.2-13.7]; P=0.57). Among 244 individuals of European ancestry with DCM in PennMedicine BioBank, hiPSI TTNtv carriers had lower left ventricular ejection fraction (β=-12%, P=3×10-7), and increased left ventricular diameter (β=0.65 cm, P=9×10-3). In the Geisinger cohort, hiPSI TTNtv carriers without a cardiomyopathy diagnosis had more atrial fibrillation (odds ratio, 2.4 [1.6-3.6]) and heart failure (odds ratio, 3.8 [2.4-6.0]), and lower left ventricular ejection fraction (β=-3.4%, P=1×10-7).

CONCLUSIONS

Individuals of European ancestry with hiPSI TTNtv have an abnormal cardiac phenotype characterized by lower left ventricular ejection fraction, irrespective of the clinical manifestation of cardiomyopathy. Associations with arrhythmias, including atrial fibrillation, were observed even when controlling for cardiomyopathy diagnosis. In contrast, no association between hiPSI TTNtvs and DCM was discerned among individuals of African ancestry. Given these findings, clinical identification of hiPSI TTNtv carriers may alter clinical management strategies.

A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition

Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

Unbiased Phenome-Wide Association Studies of Red Cell Distribution Width Identifies Key Associations with Pulmonary Hypertension

Rationale: Red cell distribution width (RDW) is a prognostic factor in many diseases; however, its clinical utility remains limited because the relative value of RDW as a biomarker across disease states has not been established. Objectives: To establish an unbiased RDW disease hierarchy to guide the clinical use of RDW and to assess its relationship to cardiovascular hemodynamic and structural parameters. Methods: We performed phenome-wide association studies for RDW in discovery and replication cohorts derived from a deidentified electronic health record in nonanemic individuals. RDW values obtained within 30 days of echocardiogram or right heart catheterization were tested for association with structural and hemodynamic variables. Results: RDW was associated with 263 phenotypes in both men and women in the discovery cohort (n = 121,530), 48 of which replicated in an independent cohort (n = 2,039). The strongest associations were observed with pulmonary arterial hypertension (odds ratio [OR], 2.1; 95% confidence interval [CI], 1.9-2.3), chronic pulmonary heart disease (OR, 2.0; 95% CI, 1.9-2.2), and congestive heart failure (OR, 1.9; 95% CI, 1.8-2.0); P < 1 × 10-74 for all. By echocardiography, RDW was higher in the setting of right ventricular dysfunction than left ventricular dysfunction (P < 0.001). Measured invasively, mean pulmonary arterial pressure was associated with RDW (21 vs. 33 mm Hg at 25th vs. 75th percentile RDW; P < 1 × 10-7) and remained strongly significant even when controlling for mean pulmonary capillary wedge pressure (21 vs. 29 mm Hg at 25th vs. 75th percentile RDW; P < 1 × 10-7). Conclusions: Among 1,364 coded medical conditions, increased RDW was strongly associated with pulmonary hypertension and heart failure. Hemodynamic and echocardiographic phenotyping confirmed these associations and underscored that the most clinically relevant phenotype associated with RDW was pulmonary hypertension. These hypothesis-generating findings highlight the potential shared pathophysiology of pulmonary hypertension and elevated RDW. Elevated RDW in the absence of anemia should alert clinicians to the potential for underlying cardiopulmonary disease.

Evaluation of the association of bariatric surgery with subsequent depression

BACKGROUND/OBJECTIVES

Bariatric surgery is helpful in enabling sustained weight loss, but effects on depression are unclear. Reductions in depression-related symptoms and increases in suicide rate have both been observed after bariatric surgery, but these observations are confounded by the presence of pre-existing depression. The goal of this study is to evaluate the effect of bariatric surgery on subsequent depression diagnosis.

SUBJECTS/METHODS

In this observational study, a prospective cohort study was simulated by evaluating depression risk based on diagnostic codes. An administrative database was utilized for this study, containing records and observations between 1 January 2008 through 29 February 2016 of enrolled patients in the United States. Individuals considered in this analysis were enrolled in a commercial health insurance program, observed for at least 6 months prior to surgery, and met the eligibility criteria for bariatric surgery. In all, 777,140 individuals were considered in total.

RESULTS

Bariatric surgery was found to be significantly associated with subsequent depression relative to both non-surgery controls (HR = 1.31, 95% CI, 1.27-1.34, P < 2e-32) and non-bariatric abdominal surgery controls (HR = 2.15, 95% CI, 2.09-2.22, P < 2e-32). Patients with pre-surgical psychiatric screening had a reduced depression hazard ratio with respect to patients without (HR = 0.85, 95% CI, 0.81-0.89, P = 3.208e-12). Men were found to be more susceptible to post-bariatric surgery depression compared with women. Pre-surgical psychiatric evaluations reduced the magnitude of this effect. Relative to bariatric surgeries as a whole, vertical sleeve gastrectomy had a lower incidence of depression, while Roux-en Y Gastric Bypass and revision/removal surgeries had higher rates.

CONCLUSIONS

In individuals without a history of depression, bariatric surgery is associated with subsequent diagnosis of depression. This study provides guidance for patients considering bariatric surgery and their clinicians in terms of evaluating potential risks and benefits of surgery.

A Phenome-Wide Association Study Uncovers a Pathological Role of Coagulation Factor X during Acinetobacter baumannii Infection

Coagulation and inflammation are interconnected, suggesting that coagulation plays a key role in the inflammatory response to pathogens. A phenome-wide association study (PheWAS) was used to identify clinical phenotypes of patients with a polymorphism in coagulation factor X. Patients with this single nucleotide polymorphism (SNP) were more likely to be hospitalized with hemostatic and infection-related disorders, suggesting that factor X contributes to the immune response to infection. To investigate this, we modeled infections by human pathogens in a mouse model of factor X deficiency. Factor X-deficient mice were protected from systemic Acinetobacter baumannii infection, suggesting that factor X plays a role in the immune response to A. baumannii Factor X deficiency was associated with reduced cytokine and chemokine production and alterations in immune cell population during infection: factor X-deficient mice demonstrated increased abundance of neutrophils, macrophages, and effector T cells. Together, these results suggest that factor X activity is associated with an inefficient immune response and contributes to the pathology of A. baumannii infection.

Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports

Background

Silent brain infarction (SBI) is defined as the presence of 1 or more brain lesions, presumed to be because of vascular occlusion, found by neuroimaging (magnetic resonance imaging or computed tomography) in patients without clinical manifestations of stroke. It is more common than stroke and can be detected in 20% of healthy elderly people. Early detection of SBI may mitigate the risk of stroke by offering preventative treatment plans. Natural language processing (NLP) techniques offer an opportunity to systematically identify SBI cases from electronic health records (EHRs) by extracting, normalizing, and classifying SBI-related incidental findings interpreted by radiologists from neuroimaging reports.

Objective

This study aimed to develop NLP systems to determine individuals with incidentally discovered SBIs from neuroimaging reports at 2 sites: Mayo Clinic and Tufts Medical Center.

Methods

Both rule-based and machine learning approaches were adopted in developing the NLP system. The rule-based system was implemented using the open source NLP pipeline MedTagger, developed by Mayo Clinic. Features for rule-based systems, including significant words and patterns related to SBI, were generated using pointwise mutual information. The machine learning models adopted convolutional neural network (CNN), random forest, support vector machine, and logistic regression. The performance of the NLP algorithm was compared with a manually created gold standard. The gold standard dataset includes 1000 radiology reports randomly retrieved from the 2 study sites (Mayo and Tufts) corresponding to patients with no prior or current diagnosis of stroke or dementia. 400 out of the 1000 reports were randomly sampled and double read to determine interannotator agreements. The gold standard dataset was equally split to 3 subsets for training, developing, and testing.

Results

Among the 400 reports selected to determine interannotator agreement, 5 reports were removed due to invalid scan types. The interannotator agreements across Mayo and Tufts neuroimaging reports were 0.87 and 0.91, respectively. The rule-based system yielded the best performance of predicting SBI with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.991, 0.925, 1.000, 1.000, and 0.990, respectively. The CNN achieved the best score on predicting white matter disease (WMD) with an accuracy, sensitivity, specificity, PPV, and NPV of 0.994, 0.994, 0.994, 0.994, and 0.994, respectively.

Conclusions

We adopted a standardized data abstraction and modeling process to developed NLP techniques (rule-based and machine learning) to detect incidental SBIs and WMDs from annotated neuroimaging reports. Validation statistics suggested a high feasibility of detecting SBIs and WMDs from EHRs using NLP.

An exploratory phenome wide association study linking asthma and liver disease genetic variants to electronic health records from the Estonian Biobank

The Estonian Biobank, governed by the Institute of Genomics at the University of Tartu (Biobank), has stored genetic material/DNA and continuously collected data since 2002 on a total of 52,274 individuals representing ~5% of the Estonian adult population and is increasing. To explore the utility of data available in the Biobank, we conducted a phenome-wide association study (PheWAS) in two areas of interest to healthcare researchers; asthma and liver disease. We used 11 asthma and 13 liver disease-associated single nucleotide polymorphisms (SNPs), identified from published genome-wide association studies, to test our ability to detect established associations. We confirmed 2 asthma and 5 liver disease associated variants at nominal significance and directionally consistent with published results. We found 2 associations that were opposite to what was published before (rs4374383:AA increases risk of NASH/NAFLD, rs11597086 increases ALT level). Three SNP-diagnosis pairs passed the phenome-wide significance threshold: rs9273349 and E06 (thyroiditis, p = 5.50x10-8); rs9273349 and E10 (type-1 diabetes, p = 2.60x10-7); and rs2281135 and K76 (non-alcoholic liver diseases, including NAFLD, p = 4.10x10-7). We have validated our approach and confirmed the quality of the data for these conditions. Importantly, we demonstrate that the extensive amount of genetic and medical information from the Estonian Biobank can be successfully utilized for scientific research.

Semi-supervised estimation of covariance with application to phenome-wide association studies with electronic medical records data

Electronic medical records data are valuable resources for discovery research. They contain detailed phenotypic information on individual patients, opening opportunities for simultaneously studying multiple phenotypes. A useful tool for such simultaneous assessment is the phenome-wide association study, which relates a genomic or biological marker of interest to a wide spectrum of disease phenotypes, typically defined by the diagnostic billing codes. One challenge arises when the biomarker of interest is expensive to measure on the entire electronic medical record cohort. Performing phenome-wide association study based on supervised estimation using only subjects who have marker measurements may yield limited power. In this paper, we focus on the setting where the marker is measured on a small fraction of the patients while a few surrogate markers such as historical measurements of the biomarker are available on a large number of patients. We propose an efficient semi-supervised estimation procedure to estimate the covariance between the biomarker and the billing code, leveraging the surrogate marker information. We employ surrogate marker values to impute the missing outcome via a two-step semi-non-parametric approach and demonstrate that our proposed estimator is always more efficient than the supervised counterpart without requiring the imputation model to be correct. We illustrate the proposed procedure by assessing the association between the C-reactive protein and some inflammatory diseases with an electronic medical record study of inflammatory bowel disease performed with the Partners HealthCare electronic medical record database where C-reactive protein was only measured for a small fraction of the patients due to budget constraints.

Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects

Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict safety. Characterizing the effect of natural variation in the genes encoding drug targets should present a powerful approach to predict side effects arising from drugging particular proteins. In this retrospective analysis, we report a correlation between the organ systems affected by genetic variation in drug targets and the organ systems in which side effects are observed. Across 1819 drugs and 21 phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0 vs 19.2%; OR = 1.80). This result suggests that human genetic data should be used to predict safety issues associated with drug targets.

Safety issues including side effects are one of the major factors causing failure of clinical trials in drug development. Here, the authors leverage information about phenotypes associated with variation in genes encoding drug targets to predict drug-treatment-related side effects.

Advanced Genetic Approaches in Discovery and Characterization of Genes Involved With Osteoporosis in Mouse and Human

Osteoporosis is a complex condition with contributions from, and interactions between, multiple genetic loci and environmental factors. This review summarizes key advances in the application of genetic approaches for the identification of osteoporosis susceptibility genes. Genome-wide linkage analysis (GWLA) is the classical approach for identification of genes that cause monogenic diseases; however, it has shown limited success for complex diseases like osteoporosis. In contrast, genome-wide association studies (GWAS) have successfully identified over 200 osteoporosis susceptibility loci with genome-wide significance, and have provided most of the candidate genes identified to date. Phenome-wide association studies (PheWAS) apply a phenotype-to-genotype approach which can be used to complement GWAS. PheWAS is capable of characterizing the association between osteoporosis and uncommon and rare genetic variants. Another alternative approach, whole genome sequencing (WGS), will enable the discovery of uncommon and rare genetic variants in osteoporosis. Meta-analysis with increasing statistical power can offer greater confidence in gene searching through the analysis of combined results across genetic studies. Recently, new approaches to gene discovery include animal phenotype based models such as the Collaborative Cross and ENU mutagenesis. Site-directed mutagenesis and genome editing tools such as CRISPR/Cas9, TALENs and ZNFs have been used in functional analysis of candidate genes in vitro and in vivo. These resources are revolutionizing the identification of osteoporosis susceptibility genes through the use of genetically defined inbred mouse libraries, which are screened for bone phenotypes that are then correlated with known genetic variation. Identification of osteoporosis-related susceptibility genes by genetic approaches enables further characterization of gene function in animal models, with the ultimate aim being the identification of novel therapeutic targets for osteoporosis.

Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

Bayesian meta-analysis across genome-wide association studies of diverse phenotypes

Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared with standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta-analysis of 20 diverse GWAS which were part of the Wellcome Trust Case Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for a range of possible true patterns of association across studies in a computationally efficient framework.

Diagnostic Algorithms to Study Post-Concussion Syndrome Using Electronic Health Records: Validating a Method to Capture an Important Patient Population

Post-concussion syndrome (PCS) is characterized by persistent cognitive, somatic, and emotional symptoms after a mild traumatic brain injury (mTBI). Genetic and other biological variables may contribute to PCS etiology, and the emergence of biobanks linked to electronic health records (EHRs) offers new opportunities for research on PCS. We sought to validate the EHR data of PCS patients by comparing two diagnostic algorithms deployed in the Vanderbilt University Medical Center de-identified database of 2.8 million patient EHRs. The algorithms identified individuals with PCS by: 1) natural language processing (NLP) of narrative text in the EHR combined with structured demographic, diagnostic, and encounter data; or 2) coded billing and procedure data. The predictive value of each algorithm was assessed, and cases and controls identified by each approach were compared on demographic and medical characteristics. The NLP algorithm identified 507 cases and 10,857 controls. The negative predictive value in controls was 78% and the positive predictive value (PPV) in cases was 82%. Conversely, the coded algorithm identified 1142 patients with two or more PCS billing codes and had a PPV of 76%. Comparisons of PCS controls to both case groups recovered known epidemiology of PCS: cases were more likely than controls to be female and to have pre-morbid diagnoses of anxiety, migraine, and post-traumatic stress disorder. In contrast, controls and cases were equally likely to have attention deficit hyperactive disorder and learning disabilities, in accordance with the findings of recent systematic reviews of PCS risk factors. We conclude that EHRs are a valuable research tool for PCS. Ascertainment based on coded data alone had a predictive value comparable to an NLP algorithm, recovered known PCS risk factors, and maximized the number of included patients.

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.

Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model

The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent "omnigenic" or "core genes" model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision 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.

Feature extraction for phenotyping from semantic and knowledge resources

OBJECTIVE

Phenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. To further accelerate the development of phenotyping algorithms, we propose a fully automated and robust unsupervised feature selection method that leverages only publicly available medical knowledge sources, instead of EHR data.

METHODS

SEmantics-Driven Feature Extraction (SEDFE) collects medical concepts from online knowledge sources as candidate features and gives them vector-form distributional semantic representations derived with neural word embedding and the Unified Medical Language System Metathesaurus. A number of features that are semantically closest and that sufficiently characterize the target phenotype are determined by a linear decomposition criterion and are selected for the final classification algorithm.

RESULTS

SEDFE was compared with the EHR-based SAFE algorithm and domain experts on feature selection for the classification of five phenotypes including coronary artery disease, rheumatoid arthritis, Crohn's disease, ulcerative colitis, and pediatric pulmonary arterial hypertension using both supervised and unsupervised approaches. Algorithms yielded by SEDFE achieved comparable accuracy to those yielded by SAFE and expert-curated features. SEDFE is also robust to the input semantic vectors.

CONCLUSION

SEDFE attains satisfying performance in unsupervised feature selection for EHR phenotyping. Both fully automated and EHR-independent, this method promises efficiency and accuracy in developing algorithms for high-throughput phenotyping.

PheWAS-Based Systems Genetics Methods for Anti-Breast Cancer Drug Discovery

Breast cancer is a high-risk disease worldwide. For such complex diseases that are induced by multiple pathogenic genes, determining how to establish an effective drug discovery strategy is a challenge. In recent years, a large amount of genetic data has accumulated, particularly in the genome-wide identification of disorder genes. However, understanding how to use these data efficiently for pathogenesis elucidation and drug discovery is still a problem because the gene–disease links that are identified by high-throughput techniques such as phenome-wide association studies (PheWASs) are usually too weak to have biological significance. Systems genetics is a thriving area of study that aims to understand genetic interactions on a genome-wide scale. In this study, we aimed to establish two effective strategies for identifying breast cancer genes based on the systems genetics algorithm. As a result, we found that the GeneRank-based strategy, which combines the prognostic phenotype-based gene-dependent network with the phenotypic-related PheWAS data, can promote the identification of breast cancer genes and the discovery of anti-breast cancer drugs.

Using topic modeling via non-negative matrix factorization to identify relationships between genetic variants and disease phenotypes: A case study of Lipoprotein(a) (LPA)

Genome-wide and phenome-wide association studies are commonly used to identify important relationships between genetic variants and phenotypes. Most studies have treated diseases as independent variables and suffered from the burden of multiple adjustment due to the large number of genetic variants and disease phenotypes. In this study, we used topic modeling via non-negative matrix factorization (NMF) for identifying associations between disease phenotypes and genetic variants. Topic modeling is an unsupervised machine learning approach that can be used to learn patterns from electronic health record data. We chose the single nucleotide polymorphism (SNP) rs10455872 in LPA as the predictor since it has been shown to be associated with increased risk of hyperlipidemia and cardiovascular diseases (CVD). Using data of 12,759 individuals with electronic health records (EHR) and linked DNA samples at Vanderbilt University Medical Center, we trained a topic model using NMF from 1,853 distinct phenotypes and identified six topics. We tested their associations with rs10455872 in LPA. Topics enriched for CVD and hyperlipidemia had positive correlations with rs10455872 (P < 0.001), replicating a previous finding. We also identified a negative correlation between LPA and a topic enriched for lung cancer (P < 0.001) which was not previously identified via phenome-wide scanning. We were able to replicate the top finding in a separate dataset. Our results demonstrate the applicability of topic modeling in exploring the relationship between genetic variants and clinical diseases.