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

Learning Opportunities for Drug Repositioning via GWAS and PheWAS Findings

Drug repositioning for available medications can be preferred over traditional drug development, which requires substantially more effort to uncover new insights into medications and diseases. Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) are two complimentary methods for finding novel associations between genes and diseases. We hypothesize that discoveries from these studies could be leveraged to find new targets for existing drugs. Thus, we propose a framework to learn opportunities for inferring such relationships via overlapped genes between disease-associated genes (e.g. GWAS and PheWAS findings) and drugtargeted genes. We use drug indications found in Medication Indication Resource (MEDI) as a gold standard to evaluate if drug indications learned from GWAS and PheWAS findings have clinical indications. We examined 151,011 <drug, GWAS phenotype> pairs from 987 drugs across 153 diseases and 763 pairs were statistically significant. Out of these 763 pairs, 16 of them were found to have clinical indications.

Loss of CENP-F Results in Dilated Cardiomyopathy with Severe Disruption of Cardiac Myocyte Architecture

Centromere-binding protein F (CENP-F) is a very large and complex protein with many and varied binding partners including components of the microtubule network. Numerous CENP-F functions impacting diverse cellular behaviors have been identified. Importantly, emerging data have shown that CENP-F loss- or gain-of-function has critical effects on human development and disease. Still, it must be noted that data at the single cardiac myocyte level examining the impact of CENP-F loss-of-function on fundamental cellular behavior is missing. To address this gap in our knowledge, we analyzed basic cell structure and function in cardiac myocytes devoid of CENP-F. We found many diverse structural abnormalities including disruption of the microtubule network impacting critical characteristics of the cardiac myocyte. This is the first report linking microtubule network malfunction to cardiomyopathy. Importantly, we also present data demonstrating a direct link between a CENP-F single nucleotide polymorphism (snp) and human cardiac disease. In a proximate sense, these data examining CENP-F function explain the cellular basis underlying heart disease in this genetic model and, in a larger sense, they will hopefully provide a platform upon which the field can explore diverse cellular outcomes in wide-ranging areas of research on this critical protein.

Electronic health records: the next wave of complex disease genetics

The combination of electronic health records (EHRs) with genetic data has ushered in the next wave of complex disease genetics. Population-based biobanks and other large cohorts provide sufficient sample sizes to identify novel genetic associations across the hundreds to thousands of phenotypes gleaned from EHRs. In this review, we summarize the current state of these EHR-linked biobanks, explore ongoing methods development in the field and highlight recent discoveries of genetic associations. We enumerate the many existing biobanks with EHRs linked to genetic data, many of which are available to researchers via application and contain sample sizes >50 000. We also discuss the computational and statistical considerations for analysis of such large datasets including mixed models, phenotype curation and cloud computing. Finally, we demonstrate how genome-wide association studies and phenome-wide association studies have identified novel genetic findings for complex diseases, specifically cardiometabolic traits. As more researchers employ innovative hypotheses and analysis approaches to study EHR-linked biobanks, we anticipate a richer understanding of the genetic etiology of complex diseases.

Pharmacogenomics of blood lipid regulation

Blood lipids are important modifiable risk factors for coronary heart disease and various drugs have been developed to target lipid fractions. Considerable efforts have been made to identify genetic variants that modulate responses to drugs in the hope of optimizing their use. Pharmacogenomics and new biotechnologies now allow for meaningful integration of human genetic findings and therapeutic development for increased efficiency and precision of lipid-lowering drugs. Polygenic predictors of disease risk are also changing how patient populations can be stratified, enabling targeted therapeutic interventions to patients more likely to derive the highest benefit, marking a shift from single variant to genomic approaches in pharmacogenomics.

Defining Phenotypes from Clinical Data to Drive Genomic Research

The rise in available longitudinal patient information in electronic health records (EHRs) and their coupling to DNA biobanks has resulted in a dramatic increase in genomic research using EHR data for phenotypic information. EHRs have the benefit of providing a deep and broad data source of health-related phenotypes, including drug response traits, expanding the phenome available to researchers for discovery. The earliest efforts at repurposing EHR data for research involved manual chart review of limited numbers of patients but now typically involve applications of rule-based and machine learning algorithms operating on sometimes huge corpora for both genome-wide and phenome-wide approaches. We highlight here the current methods, impact, challenges, and opportunities for repurposing clinical data to define patient phenotypes for genomics discovery. Use of EHR data has proven a powerful method for elucidation of genomic influences on diseases, traits, and drug-response phenotypes and will continue to have increasing applications in large cohort studies.

Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study

Protein-truncating variants can have profound effects on gene function and are critical for clinical genome interpretation and generating therapeutic hypotheses, but their relevance to medical phenotypes has not been systematically assessed. Here, we characterize the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and find 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We perform phenome-wide analyses and directly measure the effect in homozygous carriers, commonly referred to as “human knockouts,” across medical phenotypes for genes implicated as being protective against disease or associated with at least one phenotype in our study. We find several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.

Protein-truncating variants (PTVs) are predicted to significantly affect a gene’s function and, thus, human traits. Here, DeBoever et al. systematically analyze PTVs in more than 300,000 individuals across 135 phenotypes and identify 27 associations between PTVs and medical conditions.

Integrative Approaches to Understanding the Pathogenic Role of Genetic Variation in Rheumatic Diseases

The use of high-throughput omics may help to understand the contribution of genetic variants to the pathogenesis of rheumatic diseases. We discuss the concept of missing heritability: that genetic variants do not explain the heritability of rheumatoid arthritis and related rheumatologic conditions. In addition to an overview of how integrative data analysis can lead to novel insights into mechanisms of rheumatic diseases, we describe statistical approaches to prioritizing genetic variants for future functional analyses. We illustrate how analyses of large datasets provide hope for improved approaches to the diagnosis, treatment, and prevention of rheumatic diseases.

Circulating Troponin I Level in Patients with Acute Ischemic Stroke

PURPOSE OF REVIEW

Cardiac troponin levels in the blood are an important biomarker of acute coronary events, but may also be elevated in the context of acute ischemic stroke without an obvious concurrent myocardial insult. The objective of this study and systematic review is to determine how high the circulating troponin I level can rise due to ischemic stroke.

RECENT FINDINGS

Anonymized medical records from Vanderbilt University Medical Center were reviewed identifying 151,972 unique acute ischemic stroke events, of which 1226 met criteria for inclusion in this study. Included patients had at least one measurement of troponin I level documented during the hospital visit when an acute ischemic stroke was diagnosed and were free of known cardiac/coronary disease, renal impairment, sepsis, or other confounders. In this group, 20.6% had a circulating troponin I level elevated over the reference range, but 99% were below 2.13 ng/mL. This is significantly lower than the distribution observed in a cohort of 89,423 unique cases of acute coronary syndrome (p < 2.2^-16). A systematic review of published literature further supported the conclusion that troponin I level may increase due to an acute ischemic stroke, but rarely rises above 2 ng/mL. Because of the shared risk factors between stroke and coronary artery disease, clinicians caring for patients with acute ischemic stroke should always have a high index of suspicion for comorbid cardiac and cardiovascular disease. In general, troponin I levels greater than 2 ng/mL should not be attributed to an acute ischemic stroke, but should prompt a thorough evaluation for coronary artery disease.

Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis

In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We hypothesize that patients' phenotypic information available in electronic medical records (EMR) can be leveraged to accelerate disease diagnosis based on the intuition that providers need to document associated phenotypic information to support the diagnosis decision, especially for rare diseases. In this study, we proposed a collaborative filtering system enriched with natural language processing and semantic techniques to assist rare disease diagnosis based on phenotypic characterization. Specifically, we leveraged four similarity measurements with two neighborhood algorithms on 2010-2015 Mayo Clinic unstructured large patient cohort and evaluated different approaches. Preliminary results demonstrated that the use of collaborative filtering with phenotypic information is able to stratify patients with relatively similar rare diseases.

Phenome-wide association study identifies marked increased in burden of comorbidities in African Americans with systemic lupus erythematosus

Background

African Americans with systemic lupus erythematosus (SLE) have increased renal disease compared to Caucasians, but differences in other comorbidities have not been well-described. We used an electronic health record (EHR) technique to test for differences in comorbidities in African Americans compared to Caucasians with SLE.

Methods

We used a de-identified EHR with 2.8 million subjects to identify SLE cases using a validated algorithm. We performed phenome-wide association studies (PheWAS) comparing African American to Caucasian SLE cases and African American SLE cases to matched non-SLE controls. Controls were age, sex, and race matched to SLE cases. For multiple testing, a false discovery rate (FDR) p value of 0.05 was used.

Results

We identified 270 African Americans and 715 Caucasians with SLE and 1425 matched African American controls. Compared to Caucasians with SLE adjusting for age and sex, African Americans with SLE had more comorbidities in every organ system. The most striking included hypertension odds ratio (OR) = 4.25, FDR p = 5.49 × 10^{− 15}; renal dialysis OR = 10.90, FDR p = 8.75 × 10^{− 14}; and pneumonia OR = 3.57, FDR p = 2.32 × 10^{− 8}. Compared to the African American matched controls without SLE, African Americans with SLE were more likely to have comorbidities in every organ system. The most significant codes were renal and cardiac, and included renal failure (OR = 9.55, FDR p = 2.26 × 10^{− 40}) and hypertensive heart and renal disease (OR = 8.08, FDR p = 1.78 × 10^{− 22}). Adjusting for race, age, and sex in a model including both African American and Caucasian SLE cases and controls, SLE was independently associated with renal, cardiovascular, and infectious diseases (all p < 0.01).

Conclusions

African Americans with SLE have an increased comorbidity burden compared to Caucasians with SLE and matched controls. This increase in comorbidities in African Americans with SLE highlights the need to monitor for cardiovascular and infectious complications.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1561-8) contains supplementary material, which is available to authorized users.

A Systems Approach to Refine Disease Taxonomy by Integrating Phenotypic and Molecular Networks

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy.

•

The International Classification of Diseases (ICD) lags behind the current molecular characteristics of disease.

•

We quantified the limitations (specificity and blurred boundary) of ICD with integrated phenotypic and molecular profiles.

•

An integrative disease network integrating phenotypic and genotypic profiles proposes a refined disease category framework.

Disease taxonomy is one of the foundations of medical science and healthcare solutions. The most widely used disease taxonomy in clinical settings is the International Classification of Diseases (ICD), a system established >100 years ago and maintained by the World Health Organization to track disease incidence. It is well recognized that ICD, which is based on clinical observations, largely lags behind the molecular achievements of this medical big data era. We quantified the limitations of ICD using integrated phenotypic and molecular profiles and proposed a refined disease taxonomy with possible applications for precision medicine.

Recent Genetics and Epigenetics Approaches to PTSD

PURPOSE OF REVIEW

Following a life-threatening traumatic exposure, about 10% of those exposed are at considerable risk for developing posttraumatic stress disorder (PTSD), a severe and disabling syndrome characterized by uncontrollable intrusive memories, nightmares, avoidance behaviors, and hyperarousal in addition to impaired cognition and negative emotion symptoms. This review will explore recent genetic and epigenetic approaches to PTSD that explain some of the differential risk following trauma exposure.

RECENT FINDINGS

A substantial portion of the variance explaining differential risk responses to trauma exposure may be explained by differential inherited and acquired genetic and epigenetic risk. This biological risk is complemented by alterations in the functional regulation of genes via environmentally induced epigenetic changes, including prior childhood and adult trauma exposure. This review will cover recent findings from large-scale genome-wide association studies as well as newer epigenome-wide studies. We will also discuss future "phenome-wide" studies utilizing electronic medical records as well as targeted genetic studies focusing on mechanistic ways in which specific genetic or epigenetic alterations regulate the biological risk for PTSD.

PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger

Most phenome-wide association studies (PheWASs) to date have used a small to moderate number of SNPs for association with phenotypic data. We performed a large-scale single-cohort PheWAS, using electronic health record (EHR)-derived case-control status for 541 diagnoses using International Classification of Disease version 9 (ICD-9) codes and 25 median clinical laboratory measures. We calculated associations between these diagnoses and traits with ∼630,000 common frequency SNPs with minor allele frequency > 0.01 for 38,662 individuals. In this landscape PheWAS, we explored results within diseases and traits, comparing results to those previously reported in genome-wide association studies (GWASs), as well as previously published PheWASs. We further leveraged the context of functional impact from protein-coding to regulatory regions, providing a deeper interpretation of these associations. The comprehensive nature of this PheWAS allows for novel hypothesis generation, the identification of phenotypes for further study for future phenotypic algorithm development, and identification of cross-phenotype associations.

A simulation study investigating power estimates in phenome-wide association studies

Background

Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance.

Results

We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants.

Conclusions

This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2135-0) contains supplementary material, which is available to authorized users.

Psychiatric and Medical Conditions in Transition-Aged Individuals With ASD

BACKGROUND AND OBJECTIVES

Children with autism spectrum disorder (ASD) have a variety of medical and psychiatric conditions and an increased use of health care services. There is limited information about the prevalence of psychiatric and medical conditions in adolescents and young adults with ASD. Our objective was to describe the frequency of medical and psychiatric conditions in a large population of diverse, insured transition-aged individuals with ASD.

METHODS

Participants included Kaiser Permanente Northern California members who were enrolled from 2013 to 2015 and who were 14 to 25 years old. Individuals with ASD (n = 4123) were compared with peers with attention-deficit/hyperactivity disorder (n = 20 615), diabetes mellitus (n = 2156), and typical controls with neither condition (n = 20 615).

RESULTS

Over one-third (34%) of individuals with ASD had a co-occurring psychiatric condition; the most commonly reported medical conditions included infections (42%), obesity (25%), neurologic conditions (18%), allergy and/or immunologic conditions (16%), musculoskeletal conditions (15%), and gastrointestinal (11%) conditions. After controlling for sex, age, race, and duration of Kaiser Permanente Northern California membership, most psychiatric conditions were significantly more common in the ASD group than in each comparison group, and most medical conditions were significantly more common in the ASD group than in the attention-deficit/hyperactivity disorder and typical control groups but were similar to or significantly less common than the diabetes mellitus group.

CONCLUSIONS

Although more research is needed to identify factors contributing to this excess burden of disease, there is a pressing need for all clinicians to approach ASD as a chronic health condition requiring regular follow-up and routine screening and treatment of medical and psychiatric issues.

A Deep Phenotype Association Study Reveals Specific Phenotype Associations with Genetic Variants in Age-related Macular Degeneration: Age-Related Eye Disease Study 2 (AREDS2) Report No. 14

PURPOSE

Age-related macular degeneration (AMD), a multifactorial disease with variable phenotypic presentation, was associated with 52 single nucleotide polymorphisms (SNPs) at 34 loci in a genome-wide association study (GWAS). These genetic variants could modulate different biological pathways involved in AMD, contributing to phenotypic variability. To better understand the effects of these SNPs, we performed a deep phenotype association study (DeePAS) in the Age-Related Eye Disease Study 2 (AREDS2), followed by replication using AREDS participants, to identify genotype associations with AMD and non-AMD ocular and systemic phenotypes.

DESIGN

Cohort study.

PARTICIPANTS

AREDS and AREDS2 participants.

METHODS

AREDS2 participants (discovery cohort) had detailed phenotyping for AMD; other eye conditions; cardiovascular, neurologic, gastrointestinal, and endocrine disease; cognitive function; serum nutrient levels; and others (total of 139 AMD and non-AMD phenotypes). Genotypes of the 52 GWAS SNPs were obtained. The DeePAS was performed by correlating the 52 SNPs to all phenotypes using logistic and linear regression models. Associations that reached Bonferroni-corrected statistical significance were replicated in AREDS.

MAIN OUTCOME MEASURES

Genotype-phenotype associations.

RESULTS

A total of 1776 AREDS2 participants had 5 years follow-up; 1435 AREDS participants had 10 years. The DeePAS revealed a significant association of the rs3750846 SNP at the ARMS2/HTRA1 locus with subretinal/sub-retinal pigment epithelial (RPE) hemorrhage related to neovascular AMD (odds ratio 1.55 [95% confidence interval 1.31-1.84], P = 2.67 × 10-7). This novel association remained significant after conditioning on participants with neovascular AMD (P = 2.42 × 10-4). Carriers of rs3750846 had poorer visual acuity during follow-up (P = 6.82 × 10-7) and were more likely to have a first-degree relative with AMD (P = 5.38 × 10-6). Two SNPs at the CFH locus, rs10922109 and rs570618, were associated with the drusen area in the Early Treatment Diabetic Retinopathy Study Report (ETDRS) grid (P = 2.29 × 10-11 and P = 3.20 × 10-9, respectively) and the center subfield (P = 1.24 × 10-9 and P = 6.68 × 10-8, respectively). SNP rs570618 was additionally associated with the presence of calcified drusen (P = 5.38 × 10-6). Except for positive family history of AMD with rs3750846, all genotype-phenotype associations were significantly replicated in AREDS. No pleiotropic associations were identified.

CONCLUSIONS

The association of the SNP at the ARMS2/HTRA1 locus with subretinal/sub-RPE hemorrhage and poorer visual acuity and of SNPs at the CFH locus with drusen area may provide new insights in pathophysiological pathways underlying different stages of AMD.

Clinical Natural Language Processing in languages other than English: opportunities and challenges

Background

Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area.

Main Body

We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application.

Conclusion

We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.

Rare variants in drug target genes contributing to complex diseases, phenome-wide

The DrugBank database consists of ~800 genes that are well characterized drug targets. This list of genes is a useful resource for association testing. For example, loss of function (LOF) genetic variation has the potential to mimic the effect of drugs, and high impact variation in these genes can impact downstream traits. Identifying novel associations between genetic variation in these genes and a range of diseases can also uncover new uses for the drugs that target these genes. Phenome Wide Association Studies (PheWAS) have been successful in identifying genetic associations across hundreds of thousands of diseases. We have conducted a novel gene based PheWAS to test the effect of rare variants in DrugBank genes, evaluating associations between these genes and more than 500 quantitative and dichotomous phenotypes. We used whole exome sequencing data from 38,568 samples in Geisinger MyCode Community Health Initiative. We evaluated the results of this study when binning rare variants using various filters based on potential functional impact. We identified multiple novel associations, and the majority of the significant associations were driven by functionally annotated variation. Overall, this study provides a sweeping exploration of rare variant associations within functionally relevant genes across a wide range of diagnoses.

An Epidemiological Human Disease Network Derived from Disease Co-occurrence in Taiwan

In “classic” biomedical research, diseases have usually been studied individually. The pioneering human disease network (HDN) studies jointly consider a large number of diseases, analyse their interconnections, and provide a more comprehensive description of diseases. However, most of the existing HDN studies are based on molecular information and can only partially describe disease interconnections. Building on the unique Taiwan National Health Insurance Research Database (NHIRD), in this study, we construct the epidemiological HDN (eHDN), where two diseases are concluded as interconnected if their observed probability of co-occurrence deviating that expected under independence. Advancing from the existing HDN, the eHDN can also accommodate non-molecular connections and have more important practical implications. Building on the network construction, we examine important network properties such as connectivity, module, hub, and others and describe their temporal patterns. This study is among the first to systematically construct the eHDN and can have important implications for human disease research and health care and management.

Large-Scale Genomic Biobanks and Cardiovascular Disease

PURPOSE OF REVIEW

Cardiovascular disease is a leading cause of morbidity and mortality worldwide and is the focus of extensive biomedical research. Large genetic consortia combining data from many traditional prospective cohort and ascertained case-control study designs have facilitated the discovery of genetic associations for a variety of cardiovascular diseases including diabetes, coronary artery disease, and hypertension. Biobank-based genetic studies offer an alternative whereby large populations are genotyped and linked to electronic health records.

RECENT FINDINGS

Biobank sample sizes worldwide have surpassed even the largest genetic consortia and have yielded key insights into the genetic determinants of both common and rare cardiovascular phenotypes. Herein, we provide an overview of the largest genomic biobanks and discuss the relevant advantages and challenges inherent to the biobank model of cohort generation and genomic study design.

Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining

There are more than 3.7 million published articles on the biological functions or disease implications of proteins, constituting an important resource of proteomics knowledge. However, it is difficult to summarize the millions of proteomics findings in the literature manually and quantify their relevance to the biology and diseases of interest. We developed a fully automated bioinformatics framework to identify and prioritize proteins associated with any biological entity. We used the 22 targeted areas of the Biology/Disease-driven (B/D)-Human Proteome Project (HPP) as examples, prioritized the relevant proteins through their Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores, validated the relevance of the score by comparing the protein prioritization results with a curated database, computed the scores of proteins across the topics of B/D-HPP, and characterized the top proteins in the common model organisms. We further extended the bioinformatics workflow to identify the relevant proteins in all organ systems and human diseases and deployed a cloud-based tool to prioritize proteins related to any custom search terms in real time. Our tool can facilitate the prioritization of proteins for any organ system or disease of interest and can contribute to the development of targeted proteomic studies for precision medicine.

The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development

Drug development continues to be costly and slow, with medications failing due to lack of efficacy or presence of toxicity. The promise of pharmacogenomic discovery includes tailoring therapeutics based on an individual's genetic makeup, rational drug development, and repurposing medications. Rapid growth of large research cohorts, linked to electronic health record (EHR) data, fuels discovery of new genetic variants predicting drug action, supports Mendelian randomization experiments to show drug efficacy, and suggests new indications for existing medications. New biomedical informatics and machine-learning approaches advance the ability to interpret clinical information, enabling identification of complex phenotypes and subpopulations of patients. We review the recent history of use of "big data" from EHR-based cohorts and biobanks supporting these activities. Future studies using EHR data, other information sources, and new methods will promote a foundation for discovery to more rapidly advance precision medicine.

Calcium channel blockers as drug repurposing candidates for gestational diabetes: Mining large scale genomic and electronic health records data to repurpose medications

New therapeutic approaches are needed for gestational diabetes mellitus (GDM), but must show safety and efficacy in a historically understudied population. We studied associations between electronic medical record (EMR) phenotypes and genetic variants to uncover drugs currently considered safe in pregnancy that could treat or prevent GDM. We identified 129 systemically active drugs considered safe in pregnancy targeting the proteins produced from 196 genes. We tested for associations between GDM and/or type 2 diabetes (DM2) and 306 SNPs in 130 genes represented on the Illumina Infinium Human Exome Bead Chip (DM2 was included due to shared pathophysiological features with GDM). In parallel, we tested the association between drugs and glucose tolerance during pregnancy as measured by the glucose recorded during a routine 50-g glucose tolerance test (GTT). We found an association between GDM/DM2 and the genes targeted by 11 drug classes. In the EMR analysis, 6 drug classes were associated with changes in GTT. Two classes were identified in both analyses. L-type calcium channel blocking antihypertensives (CCBs), were associated with a 3.18 mg/dL (95% CI -6.18 to -0.18) decrease in glucose during GTT, and serotonin receptor type 3 (5HT-3) antagonist antinausea medications were associated with a 3.54 mg/dL (95% CI 1.86-5.23) increase in glucose during GTT. CCBs were identified as a class of drugs considered safe in pregnancy could have efficacy in treating or preventing GDM. 5HT-3 antagonists may be associated with worse glucose tolerance.

An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies

BACKGROUND

Genome-phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.

METHODS

In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.

RESULTS

We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer's disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).

CONCLUSIONS

This study offers powerful tools for exploring the functional consequences of variants generated from genome-phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.

Another Round of "Clue" to Uncover the Mystery of Complex Traits

A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.

Surrogate-assisted feature extraction for high-throughput phenotyping

Objective

Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy.

Methods

The proposed Surrogate-Assisted Feature Extraction (SAFE) method selects candidate features from a pool of comprehensive medical concepts found in publicly available knowledge sources. The target phenotype's International Classification of Diseases, Ninth Revision and natural language processing counts, acting as noisy surrogates to the gold-standard labels, are used to create silver-standard labels. Candidate features highly predictive of the silver-standard labels are selected as the final features.

Results

Algorithms were trained to identify patients with coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis using various numbers of labels to compare the performance of features selected by SAFE, a previously published automated feature extraction for phenotyping procedure, and domain experts. The out-of-sample area under the receiver operating characteristic curve and F -score from SAFE algorithms were remarkably higher than those from the other two, especially at small label sizes.

Conclusion

SAFE advances high-throughput phenotyping methods by automatically selecting a succinct set of informative features for algorithm training, which in turn reduces overfitting and the needed number of gold-standard labels. SAFE also potentially identifies important features missed by automated feature extraction for phenotyping or experts.

Learning bundled care opportunities from electronic medical records

OBJECTIVE

The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-driven approach can automatically learn potential bundles.

METHODS

We designed a framework to infer health condition collections (HCCs) based on the similarity of their clinical workflows, according to electronic medical record (EMR) utilization. We evaluated the framework with data from over 16,500 inpatient stays from Northwestern Memorial Hospital in Chicago, Illinois. The plausibility of the inferred HCCs for bundled care was assessed through an online survey of a panel of five experts, whose responses were analyzed via an analysis of variance (ANOVA) at a 95% confidence level. We further assessed the face validity of the HCCs using evidence in the published literature.

RESULTS

The framework inferred four HCCs, indicative of (1) fetal abnormalities, (2) late pregnancies, (3) prostate problems, and (4) chronic diseases, with congestive heart failure featuring prominently. Each HCC was substantiated with evidence in the literature and was deemed plausible for bundled care by the experts at a statistically significant level.

CONCLUSIONS

The findings suggest that an automated EMR data-driven framework conducted can provide a basis for discovering bundled care opportunities. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.

ANCO-GeneDB: annotations and comprehensive analysis of candidate genes for alcohol, nicotine, cocaine and opioid dependence

Studies have shown that genetic factors play an important role in the risk to substance addiction and abuse. So far, various genetic and genomic studies have reported the related evidence. These rich, but highly heterogeneous, data provide us an unprecedented opportunity to systematically collect, curate and assess the genetic and genomic signals from published studies and to perform a comprehensive analysis of their features, functional roles and druggability. Such genetic data resources have been made available for other disease or phenotypes but not for major substance dependence yet. Here, we report comprehensive data collection and secondary analyses of four phenotypes of dependence: alcohol dependence, nicotine dependence, cocaine dependence and opioid dependence, collectively named as Alcohol, Nicotine, Cocaine and Opioid (ANCO) dependence. We built the ANCO-GeneDB, an ANCO-dependence-associated gene resource database. ANCO-GeneDB includes resources from genome-wide association studies and candidate gene-based studies, transcriptomic studies, methylation studies, literature mining and drug-target data, as well as the derived data such as spatial-temporal gene expression, promoters, enhancers and expression quantitative trait loci. All associated genes and genetic variants are well annotated by using the collected evidence. Based on the collected data, we performed integrative, secondary analyses to prioritize genes, pathways, eQTLs and tissues that are significantly enriched in ANCO-related phenotypes.

Heterogeneity of asthma and the risk of celiac disease in children

BACKGROUND

Although human leukocyte antigen (HLA)-DR and HLA-DQ genes and gluten play crucial roles in developing celiac disease (CD), most patients with these risk factors still do not develop CD, which indicates additional unrecognized risk factors.

OBJECTIVE

To determine the association between asthma and the risk of CD in children.

METHODS

We conducted a population-based retrospective case-control study in children who resided in Olmsted County, Minnesota. We identified children with CD (cases) between January 1, 1997, and December 31, 2014, and compared these with children without CD (controls) (1:2 matching). Asthma status was ascertained by using the predetermined asthma criteria (PAC) and the asthma predictive index (API). Data analysis included conditional logistic regression models and an unsupervised network analysis by using an independent phenome-wide association scan (PheWAS) data set.

RESULTS

Although asthma status as determined by using PAC was not associated with the risk of CD (odds ratio [OR] 1.4 [95% confidence interval {CI}, 0.8-2.5]; p = 0.2), asthma status by using the API was significantly associated (OR 2.8 [95% CI, 1.3-6.0]; p = 0.008). A subgroup analysis indicated that children with both asthma as determined by using PAC and a family history of asthma had an increased risk of CD compared with those without asthma (OR 2.28 [95% CI, 1.11-4.67]; p = 0.024). PheWAS data showed a cluster of asthma single nucleotide polymorphisms and patients with CD.

CONCLUSION

A subgroup of children with asthma who also had a family history of asthma seemed to be at an increased risk of CD, and, thus, the third factor that underlies the risk of CD might be related to genetic factors for asthma. Heterogeneity of asthma plays a role in determining the risk of asthma-related comorbidity.

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Clinical information extraction applications: A literature review

BACKGROUND

With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text.

OBJECTIVES

In this literature review, we present a review of recent published research on clinical information extraction (IE) applications.

METHODS

A literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library.

RESULTS

A total of 1917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications in the areas of disease- and drug-related studies, and clinical workflow optimizations.

CONCLUSIONS

Clinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.

Generating Clinical Notes for Electronic Health Record Systems

Clinical notes summarize interactions that occur between patients and healthcare providers. With adoption of electronic health record (EHR) and computer-based documentation (CBD) systems, there is a growing emphasis on structuring clinical notes to support reusing data for subsequent tasks. However, clinical documentation remains one of the most challenging areas for EHR system development and adoption. The current manuscript describes the Vanderbilt experience with implementing clinical documentation with an EHR system. Based on their experience rolling out an EHR system that supports multiple methods for clinical documentation, the authors recommend that documentation method selection be made on the basis of clinical workflow, note content standards and usability considerations, rather than on a theoretical need for structured data.

Enhancing the Promise of Drug Repositioning through Genetics

The development of new drugs has become challenging as the necessary investments in time and money have increased while drug approval rates have decreased. A potential solution to this problem is drug repositioning which aims to use existing drugs to treat conditions for which they were not originally intended. One approach that may enhance the likelihood of success is to reposition drugs against a target that has a genetic basis. The multitude of genome-wide association studies (GWASs) conducted in recent years represents a large potential pool of novel targets for drug repositioning. Although trait-associated variants identified from GWAS still need to be causally linked to a target gene, recently developed functional genomic techniques, databases, and workflows are helping to remove this bottleneck. The pre-clinical validation of repositioning against these targets also needs to be carefully performed to ensure that findings are not confounded by off-target effects or limitations of the techniques used. Nevertheless, the approaches described in this review have the potential to provide a faster, cheaper and more certain route to clinical approval.

PIE: A prior knowledge guided integrated likelihood estimation method for bias reduction in association studies using electronic health records data

Objectives

This study proposes a novelPrior knowledge guidedIntegrated likelihoodEstimation (PIE) method to correct bias in estimations of associations due to misclassification of electronic health record (EHR)-derived binary phenotypes, and evaluates the performance of the proposed method by comparing it to 2 methods in common practice.

Methods

We conducted simulation studies and data analysis of real EHR-derived data on diabetes from Kaiser Permanente Washington to compare the estimation bias of associations using the proposed method, the method ignoring phenotyping errors, the maximum likelihood method with misspecified sensitivity and specificity, and the maximum likelihood method with correctly specified sensitivity and specificity (gold standard). The proposed method effectively leverages available information on phenotyping accuracy to construct a prior distribution for sensitivity and specificity, and incorporates this prior information through the integrated likelihood for bias reduction.

Results

Our simulation studies and real data application demonstrated that the proposed method effectively reduces the estimation bias compared to the 2 current methods. It performed almost as well as the gold standard method when the prior had highest density around true sensitivity and specificity. The analysis of EHR data from Kaiser Permanente Washington showed that the estimated associations from PIE were very close to the estimates from the gold standard method and reduced bias by 60%–100% compared to the 2 commonly used methods in current practice for EHR data.

Conclusions

This study demonstrates that the proposed method can effectively reduce estimation bias caused by imperfect phenotyping in EHR-derived data by incorporating prior information through integrated likelihood.

When Enough Is Enough: Decision Criteria for Moving a Known Drug into Clinical Testing for a New Indication in the Absence of Preclinical Efficacy Data

Many animal models of disease are suboptimal in their representation of human diseases and lack of predictive power in the success of pivotal human trials. In the context of repurposing drugs with known human safety, it is sometimes appropriate to conduct the "last experiment first," that is, progressing directly to human investigations. However, there are not accepted criteria for when to proceed straight to humans to test a new indication. We propose a specific set of criteria to guide the decision-making around when to initiate human proof of principle without preclinical efficacy studies in animal models. This approach could accelerate the transition of novel therapeutic approaches to human applications.

Current Scope and Challenges in Phenome-Wide Association Studies

PURPOSE OF REVIEW

Over many decades, researchers have been designing studies to investigate the relationship between genotypes and phenotypes to gain an understanding about the effect of genetics on disease. Recently, a high-throughput approach called phenome-wide associations studies (PheWAS) have been extensively used to identify associations between genetic variants and many diseases and traits simultaneously. In this review, we describe the value of PheWAS along with methodological issues and challenges in interpretation for current applications of PheWAS.

RECENT FINDINGS

PheWAS have uncovered a paradigm to identify new associations for genetic loci across many diseases. The application of PheWAS have been effective with phenotype data from electronic health records, epidemiological studies, and clinical trials data.

SUMMARY

The key strength of a PheWAS is to identify the association of one or more genetic variants with multiple phenotypes, which can showcase interconnections among the phenotypes due to shared genetic associations. While the PheWAS approach appears promising, there are a number of challenges that need to be addressed to provide additional robustness to PheWAS findings.

Association between borderline dysnatremia and mortality insight into a new data mining approach

BACKGROUND

Even small variations of serum sodium concentration may be associated with mortality. Our objective was to confirm the impact of borderline dysnatremia for patients admitted to hospital on in-hospital mortality using real life care data from our electronic health record (EHR) and a phenome-wide association analysis (PheWAS).

METHODS

Retrospective observational study based on patient data admitted to Hôpital Européen George Pompidou, between 01/01/2008 and 31/06/2014; including 45,834 patients with serum sodium determinations on admission. We analyzed the association between dysnatremia and in-hospital mortality, using a multivariate logistic regression model to adjust for classical potential confounders. We performed a PheWAS to identify new potential confounders.

RESULTS

Hyponatremia and hypernatremia were recorded for 12.0% and 1.0% of hospital stays, respectively. Adjusted odds ratios (ORa) for severe, moderate and borderline hyponatremia were 3.44 (95% CI, 2.41-4.86), 2.48 (95% CI, 1.96-3.13) and 1.98 (95% CI, 1.73-2.28), respectively. ORa for severe, moderate and borderline hypernatremia were 4.07 (95% CI, 2.92-5.62), 4.42 (95% CI, 2.04-9.20) and 3.72 (95% CI, 1.53-8.45), respectively. Borderline hyponatremia (ORa = 1.57 95% CI, 1.35-1.81) and borderline hypernatremia (ORa = 3.47 95% CI, 2.43-4.90) were still associated with in-hospital mortality after adjustment for classical and new confounding factors identified through the PheWAS analysis.

CONCLUSION

Borderline dysnatremia on admission are independently associated with a higher risk of in-hospital mortality. By using medical data automatically collected in EHR and a new data mining approach, we identified new potential confounding factors that were highly associated with both mortality and dysnatremia.

Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine

Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships between genetic loci and across traits. As we move toward making precision medicine a reality, whereby we make predictions about disease risk based on genomic profiles, we need to identify improved predictive models of the relationship between genome and phenome. Methods that embrace pleiotropy (the effect of one locus on more than one trait), and gene-environment (G×E) and gene-gene (G×G) interactions, will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on the genotype and environment of an individual, the whole premise of precision medicine.

Human genetics contributes to the understanding of disease pathophysiology and drug discovery

BACKGROUND

Today, sequencing technology has markedly reduced the cost and time needed to read the human genome than ever before. Genome-wide association studies have successfully identified a number of disease risk genes.

CONTRIBUTION TO UNDERSTANDING OF DISEASE PATHOPHYSIOLOGY

Recent advancements in genomic technology have substantially furthered our understanding of the pathophysiology of many diseases, such as rheumatoid arthritis.

TOWARD DRUG DISCOVERY AND FUTURE DIRECTION

Accumulating genomic information is now expected to accelerate the discovery of novel drugs. Rapidly growing multi-dimensional information in life sciences would make human genetics significantly important in the near future.

Relationship of SULT1A1 copy number variation with estrogen metabolism and human health

Human cytosolic sulfotransferase 1A1 (SULT1A1) is considered to be one of the most important SULT isoforms for metabolism, detoxification, and carcinogenesis. This theory is driven by observations that SULT1A1 is widely expressed in multiple tissues and acts on a wide range of phenolic substrates. SULT1A1 is subject to functional common copy number variation (CNV) including deletions or duplications. However, it is less clear how SULT1A1 CNV impacts health and disease. To better understand the biological role of SULT1A1 in human health, we genotyped CNV in 14,275 Marshfield Clinic patients linked to an extensive electronic health record. Since SULT1A1 is linked to steroid metabolism, select serum steroid hormones were measured in 100 individuals with a wide spectrum of SULT1A1 CNV genotypes. Furthermore, comprehensive phenome-wide association studies (PheWAS) were conducted using diagnostic codes and clinical text data. For the first time, individuals homozygous null for SULT1A1 were identified in a human population. Thirty-six percent of the population carried >2 copies of SULT1A1 whereas 4% had ≤1 copy. Results indicate SULT1A1 CNV was negatively correlated with estrone-sulfate to estrone ratio predominantly in males (E1S/E1; p=0.03, r=-0.21) and may be associated with increased risk for common allergies. The effect of SULT1A1 CNV on circulating estrogen metabolites was opposite to the predicted CNV-metabolite trend based on enzymatic function. This finding, and the potential association with common allergies reported herein, warrants future studies.

PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene–environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits.

Centralized infrastructure to support analyses involving complexity beyond genome-wide association studies is broadly needed. Here, Ritchie and colleagues develop PLATO, a software tool to process and integrate various methods for this task.

The Gain-of-Function Integrin β3 Pro33 Variant Alters the Serotonin System in the Mouse Brain

Engagement of integrins by the extracellular matrix initiates signaling cascades that drive a variety of cellular functions, including neuronal migration and axonal pathfinding in the brain. Multiple lines of evidence link the ITGB3 gene encoding the integrin β3 subunit with the serotonin (5-HT) system, likely via its modulation of the 5-HT transporter (SERT). The ITGB3 coding polymorphism Leu33Pro (rs5918, Pl^A2) produces hyperactive αvβ3 receptors that influence whole-blood 5-HT levels and may influence the risk for autism spectrum disorder (ASD). Using a phenome-wide scan of psychiatric diagnoses, we found significant, male-specific associations between the Pro33 allele and attention-deficit hyperactivity disorder and ASDs. Here, we used knock-in (KI) mice expressing an Itgb3 variant that phenocopies the human Pro33 variant to elucidate the consequences of constitutively enhanced αvβ3 signaling to the 5-HT system in the brain. KI mice displayed deficits in multiple behaviors, including anxiety, repetitive, and social behaviors. Anatomical studies revealed a significant decrease in 5-HT synapses in the midbrain, accompanied by decreases in SERT activity and reduced localization of SERTs to integrin adhesion complexes in synapses of KI mice. Inhibition of focal adhesion kinase (FAK) rescued SERT function in synapses of KI mice, demonstrating that constitutive active FAK signaling downstream of the Pro32Pro33 integrin αvβ3 suppresses SERT activity. Our studies identify a complex regulation of 5-HT homeostasis and behaviors by integrin αvβ3, revealing an important role for integrins in modulating risk for neuropsychiatric disorders.SIGNIFICANCE STATEMENT The integrin β3 Leu33Pro coding polymorphism has been associated with autism spectrum disorders (ASDs) within a subgroup of patients with elevated blood 5-HT levels, linking integrin β3, 5-HT, and ASD risk. We capitalized on these interactions to demonstrate that the Pro33 coding variation in the murine integrin β3 recapitulates the sex-dependent neurochemical and behavioral attributes of ASD. Using state-of-the-art techniques, we show that presynaptic 5-HT function is altered in these mice, and that the localization of 5-HT transporters to specific compartments within the synapse, disrupted by the integrin β3 Pro33 mutation, is critical for appropriate reuptake of 5-HT. Our studies provide fundamental insight into the genetic network regulating 5-HT neurotransmission in the CNS that is also associated with ASD risk.

Phenotype validation in electronic health records based genetic association studies

The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR-derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome-wide association studies (PheWAS). It is essential to characterize discovered associations using gold standard phenotype data by chart review. In this work, we propose a genotype stratified case-control sampling strategy to select subjects for phenotype validation. We develop a closed-form maximum-likelihood estimator for the odds ratio parameters and a score statistic for testing genetic association using the combined validated and error-prone EHR-derived phenotype data, and assess the extent of power improvement provided by this approach. Compared with case-control sampling based only on EHR-derived phenotype data, our genotype stratified strategy maintains nominal type I error rates, and result in higher power for detecting associations. It also corrects the bias in the odds ratio parameter estimates, and reduces the corresponding variance especially when the minor allele frequency is small.

Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank

Motivation

Epidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank.

General features

PHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to determine how to appropriately test each trait, then performs the analyses and produces plots and summary tables.

Implementation

The PHESANT phenome scan is implemented in R. PHESANT includes a novel Javascript D3.js visualization and accompanying Java code that converts the phenome scan results to the required JavaScript Object Notation (JSON) format.

Availability

PHESANT is available on GitHub at [https://github.com/MRCIEU/PHESANT]. Git tag v0.5 corresponds to the version presented here.

Clinical and Genome-Wide Analysis of Cisplatin-Induced Peripheral Neuropathy in Survivors of Adult-Onset Cancer

Purpose: Our purpose was to characterize the clinical influences, genetic risk factors, and gene mechanisms contributing to persistent cisplatin-induced peripheral neuropathy (CisIPN) in testicular cancer survivors (TCSs).Experimental Design: TCS given cisplatin-based therapy completed the validated EORTC QLQ-CIPN20 questionnaire. An ordinal CisIPN phenotype was derived, and associations with age, smoking, excess drinking, hypertension, body mass index, diabetes, hypercholesterolemia, cumulative cisplatin dose, and self-reported health were examined for 680 TCS. Genotyping was performed on the Illumina HumanOmniExpressExome chip. Following quality control and imputation, 5.1 million SNPs in 680 genetically European TCS formed the input set. GWAS and PrediXcan were used to identify genetic variation and genetically determined gene expression traits, respectively, contributing to CisIPN. We evaluated two independent datasets for replication: Vanderbilt's electronic health database (BioVU) and the CALGB 90401 trial.Results: Eight sensory items formed a subscale with good internal consistency (Cronbach α = 0.88). Variables significantly associated with CisIPN included age at diagnosis (OR per year, 1.06; P = 2 × 10-9), smoking (OR, 1.54; P = 0.004), excess drinking (OR, 1.83; P = 0.007), and hypertension (OR, 1.61; P = 0.03). CisIPN was correlated with lower self-reported health (OR, 0.56; P = 2.6 × 10-9) and weight gain adjusted for years since treatment (OR per Δkg/m2, 1.05; P = 0.004). PrediXcan identified lower expressions of MIDN and RPRD1B, and higher THEM5 expression as associated with CisIPN (P value for each < 5 × 10-6) with replication of RPRD1B meeting significance criteria (Fisher combined P = 0.0089).Conclusions: CisIPN is associated with age, modifiable risk factors, and genetically determined expression level of RPRD1B Further study of implicated genes could elucidate the pathophysiologic underpinnings of CisIPN. Clin Cancer Res; 23(19); 5757-68. ©2017 AACR.

Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic BioMe biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, COL27A1, with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.

Diseases often run in families. These disease are frequently linked to changes in DNA that are passed down through generations. Close family members may share these disease-causing mutations; so may distant relatives who inherited the same mutation from a common ancestor long ago. Geneticists use a method called linkage mapping to trace a disease found in multiple members of a family over generations to genetic changes in a shared ancestor. This allows scientists to pinpoint the exact place in the genome the disease-causing mutation occurred. Using computer algorithms, scientists can apply the same technique to identify mutations that distant relatives inherited from a common ancestor.

Belbin et al. used this computational technique to identify a mutation that may cause unusually short stature or bone and joint problems in up to 2% of people of Puerto Rican descent. In the experiments, the genomes of about 32,000 New Yorkers who have volunteered to participate in the BioMe Biobank and their health records were used to search for genetic changes linked to extremely short stature. The search revealed that people who inherited two copies of this mutation from their parents were likely to be extremely short or to have bone and joint problems. People who inherited one copy had an increased likelihood of joint or bone problems.

This mutation affects a gene responsible for making a form of protein called collagen that is important for bone growth. The analysis suggests the mutation first arose in a Native American ancestor living in Puerto Rico around the time that European colonization began. The mutation had previously been linked to a disorder called Steel syndrome that was thought to be rare. Belbin et al. showed this condition is actually fairly common in people whose ancestors recently came from Puerto Rico, but may often go undiagnosed by their physicians. The experiments emphasize the importance of including diverse populations in genetic studies, as studies of people of predominantly European descent would likely have missed the link between this disease and mutation.

Classification of common human diseases derived from shared genetic and environmental determinants

In this study, we used insurance claims for over one-third of the entire US population to create a subset of 128,989 families (481,657 unique individuals). We then used these data to (i) estimate the heritability and familial environmental patterns of 149 diseases and (ii) infer the genetic and environmental correlations for disease pairs from a set of 29 complex diseases. The majority (52 of 65) of our study's heritability estimates matched earlier reports, and 84 of our estimates appear to have been obtained for the first time. We used correlation matrices to compute environmental and genetic disease classifications and corresponding reliability measures. Among unexpected observations, we found that migraine, typically classified as a disease of the central nervous system, appeared to be most genetically similar to irritable bowel syndrome and most environmentally similar to cystitis and urethritis, all of which are inflammatory diseases.