Sarah A. Pendergrass

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study

Unleashing the power of precision medicine Precision medicine promises the ability to identify risks and treat patients on the basis of pathogenic genetic variation. Two studies combined exome sequencing results for over 50,000 people with their electronic health records. Dewey et al. found that ∼3.5% of individuals in their cohort had clinically actionable genetic variants. Many of these variants affected blood lipid levels that could influence cardiovascular health. Abul-Husn et al. extended these findings to investigate the genetics and treatment of familial hypercholesterolemia, a risk factor for cardiovascular disease, within their patient pool. Genetic screening helped identify at-risk patients who could benefit from increased treatment. Science, this issue p. 10.1126/science.aaf6814, p. 10.1126/science.aaf7000 More than 50,000 exomes, coupled with electronic health records, inform on medically relevant genetic variants. INTRODUCTION Large-scale genetic studies of integrated health care populations, with phenotypic data captured natively in the documentation of clinical care, have the potential to unveil genetic associations that point the way to new biology and therapeutic targets. This setting also represents an ideal test bed for the implementation of genomics in routine clinical care in service of precision medicine. RATIONALE The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System aims to catalyze genomic discovery and precision medicine by coupling high-throughput exome sequencing to longitudinal electronic health records (EHRs) of participants in Geisinger’s MyCode Community Health Initiative. Here, we describe initial insights from whole-exome sequencing of 50,726 adult participants of predominantly European ancestry using clinical phenotypes derived from EHRs. RESULTS The median duration of EHR data associated with sequenced participants was 14 years, with a median of 87 clinical encounters, 687 laboratory tests, and seven procedures per participant. Forty-eight percent of sequenced individuals had one or more first- or second-degree relatives in the sample, and genome-wide autozygosity was similar to other outbred European populations. We found ~4.2 million single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in loss of gene function (LoF). The overwhelming majority of these genetic variants occurred at a minor allele frequency of ≤1%, and more than half were singletons. Each participant harbored a median of 21 rare predicted LoFs. At this sample size, ~92% of sequenced genes, including genes that encode existing drug targets or confer risk for highly penetrant genetic diseases, harbor rare heterozygous predicted LoF variants. About 7% of sequenced genes contained rare homozygous predicted LoF variants in at least one individual. Linking these data to EHR-derived laboratory phenotypes revealed consequences of partial or complete LoF in humans. Among these were previously unidentified associations between predicted LoFs in CSF2RB and basophil and eosinophil counts, and EGLN1-associated erythrocytosis segregating in genetically identified family networks. Using predicted LoFs as a model for drug target antagonism, we found associations supporting the majority of therapeutic targets for lipid lowering. To highlight the opportunity for genotype-phenotype association discovery, we performed exome-wide association analyses of EHR-derived lipid values, newly implicating rare predicted LoFs, and deleterious missense variants in G6PC in association with triglyceride levels. In a survey of 76 clinically actionable disease-associated genes, we estimated that 3.5% of individuals harbor pathogenic or likely pathogenic variants that meet criteria for clinical action. Review of the EHR uncovered findings associated with the monogenic condition in ~65% of pathogenic variant carriers’ medical records. CONCLUSION The findings reported here demonstrate the value of large-scale sequencing in an integrated health system population, add to the knowledge base regarding the phenotypic consequences of human genetic variation, and illustrate the challenges and promise of genomic medicine implementation. DiscovEHR provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic target discovery. Therapeutic target validation and genomic medicine in DiscovEHR. (A) Associations between predicted LoF variants in lipid drug target genes and lipid levels. Boxes correspond to effect size, given as the absolute value of effect, in SD units; whiskers denote 95% confidence intervals for effect. The size of the box is proportional to the logarithm (base 10) of predicted LoF carriers. (B and C) Prevalence and expressivity of clinically actionable genetic variants in 76 disease genes, according to EHR data. G76, Geisinger-76. The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.

Polygenic heritability estimates in pharmacogenetics: focus on asthma and related phenotypes.

Although accurate measures of heritability are required to understand the pharmacogenetic basis of drug treatment response, these are generally not available, as it is unfeasible to give medications to individuals for which treatment is not indicated. Using a polygenic linear mixed modeling approach, we estimated lower bounds on the heritability of asthma and the heritability of two related drug–response phenotypes, bronchodilator response and airway hyperreactivity, using genome-wide single nucleotide polymorphism (SNP) data from existing asthma cohorts. Our estimate of the heritability for bronchodilator response is 28.5% (SE 16%, P=0.043) and airway hyperresponsiveness is 51.1% (SE 34%, P=0.064), whereas we estimate asthma genetic liability at 61.5% (SE 16%, P<0.001). Our results agree with the previously published estimates of the heritability of these traits, suggesting that the linear mixed modeling method is useful for computing the heritability of other pharmacogenetic traits. Furthermore, our results indicate that multiple SNP main effects, including SNPs as yet unidentified by genome-wide association study methods, together explain a sizable portion of the heritability of these traits.

Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery

With the large volume of clinical and epidemiological data being collected, increasingly linked to extensive genotypic data, coupled with expanding high-performance computational resources, there are considerable opportunities for comprehensively exploring the networks of connections that exist between the phenome and the genome. These networks can be identified through Phenome-Wide Association Studies (PheWAS) where the association between a collection of genetic variants, or in some cases a particular clinical lab variable, and a wide and diverse range of phenotypes, diagnoses, traits, and/or outcomes are evaluated. This is a departure from the more familiar genome-wide association study approach, which has been used to identify single nucleotide polymorphisms associated with one outcome or a very limited phenotypic domain. In addition to highlighting novel connections between multiple phenotypes and elucidating more of the phenotype-genotype landscape, PheWAS can generate new hypotheses for further exploration, and can also be used to narrow the search space for research using comprehensive data collections. The complex results of PheWAS also have the potential for uncovering new mechanistic insights. We review here how the PheWAS approach has been used with data from epidemiological studies, clinical trials, and de-identified electronic health record data. We also review methodologies for the analyses underlying PheWAS, and emerging methods developed for evaluating the comprehensive results of PheWAS including genotype–phenotype networks. This review also highlights PheWAS as an important tool for identifying new biomarkers, elucidating the genetic architecture of complex traits, and uncovering pleiotropy. There are many directions and new methodologies for the future of PheWAS analyses, from the phenotypic data to the genetic data, and herein we also discuss some of these important future PheWAS developments.

Polygenic inheritance of paclitaxel-induced sensory peripheral neuropathy driven by axon outgrowth gene sets in CALGB 40101 (Alliance)

Peripheral neuropathy is a common dose-limiting toxicity for patients treated with paclitaxel. For most individuals, there are no known risk factors that predispose patients to the adverse event, and pathogenesis for paclitaxel-induced peripheral neuropathy is unknown. Determining whether there is a heritable component to paclitaxel-induced peripheral neuropathy would be valuable in guiding clinical decisions and may provide insight into treatment of and mechanisms for the toxicity. Using genotype and patient information from the paclitaxel arm of CALGB 40101 (Alliance), a phase III clinical trial evaluating adjuvant therapies for breast cancer in women, we estimated the variance in maximum grade and dose at first instance of sensory peripheral neuropathy. Our results suggest that paclitaxel-induced neuropathy has a heritable component, driven in part by genes involved in axon outgrowth. Disruption of axon outgrowth may be one of the mechanisms by which paclitaxel treatment results in sensory peripheral neuropathy in susceptible patients.

Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield Personalized Medicine Research Project Biobank.

Environment-wide association studies (EWAS) provide a way to uncover the environmental mechanisms involved in complex traits in a high-throughput manner. Genome-wide association studies have led to the discovery of genetic variants associated with many common diseases but do not take into account the environmental component of complex phenotypes. This EWAS assesses the comprehensive association between environmental variables and the outcome of type 2 diabetes (T2D) in the Marshfield Personalized Medicine Research Project Biobank (Marshfield PMRP). We sought replication in two National Health and Nutrition Examination Surveys (NHANES). The Marshfield PMRP currently uses four tools for measuring environmental exposures and outcome traits: 1) the PhenX Toolkit includes standardized exposure and phenotypic measures across several domains, 2) the Diet History Questionnaire (DHQ) is a food frequency questionnaire, 3) the Measurement of a Persons Habitual Physical Activity scores the level of an individuals physical activity, and 4) electronic health records (EHR) employs validated algorithms to establish T2D case-control status. Using PLATO software, 314 environmental variables were tested for association with T2D using logistic regression, adjusting for sex, age, and BMI in over 2,200 European Americans. When available, similar variables were tested with the same methods and adjustment in samples from NHANES III and NHANES 1999-2002. Twelve and 31 associations were identified in the Marshfield samples at p<0.01 and p<0.05, respectively. Seven and 13 measures replicated in at least one of the NHANES at p<0.01 and p<0.05, respectively, with the same direction of effect. The most significant environmental exposures associated with T2D status included decreased alcohol use as well as increased smoking exposure in childhood and adulthood. The results demonstrate the utility of the EWAS method and survey tools for identifying environmental components of complex diseases like type 2 diabetes. These high-throughput and comprehensive investigation methods can easily be applied to investigate the relation between environmental exposures and multiple phenotypes in future analyses.

Enabling high-throughput genotype-phenotype associations in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project as part of the Population Architecture using Genomics and Epidemiology (PAGE) study.

Genetic association studies have rapidly become a major tool for identifying the genetic basis of common human diseases. The advent of cost-effective genotyping coupled with large collections of samples linked to clinical outcomes and quantitative traits now make it possible to systematically characterize genotype-phenotype relationships in diverse populations and extensive datasets. To capitalize on these advancements, the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project, as part of the collaborative Population Architecture using Genomics and Epidemiology (PAGE) study, accesses two collections: the National Health and Nutrition Examination Surveys (NHANES) and BioVU, Vanderbilt Universitys biorepository linked to de-identified electronic medical records. We describe herein the workflows for accessing and using the epidemiologic (NHANES) and clinical (BioVU) collections, where each workflow has been customized to reflect the content and data access limitations of each respective source. We also describe the process by which these data are generated, standardized, and shared for meta-analysis among the PAGE study sites. As a specific example of the use of BioVU, we describe the data mining efforts to define cases and controls for genetic association studies of common cancers in PAGE. Collectively, the efforts described here are a generalized outline for many of the successful approaches that can be used in the era of high-throughput genotype-phenotype associations for moving biomedical discovery forward to new frontiers of data generation and analysis.

Genomic architecture of pharmacological efficacy and adverse events

The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.

Using BioBin to explore rare variant population stratification.

Rare variants (RVs) will likely explain additional heritability of many common complex diseases; however, the natural frequencies of rare variation across and between human populations are largely unknown. We have developed a powerful, flexible collapsing method called BioBin that utilizes prior biological knowledge using multiple publicly available database sources to direct analyses. Variants can be collapsed according to functional regions, evolutionary conserved regions, regulatory regions, genes, and/or pathways without the need for external files. We conducted an extensive comparison of rare variant burden differences (MAF < 0.03) between two ancestry groups from 1000 Genomes Project data, Yoruba (YRI) and European descent (CEU) individuals. We found that 56.86% of gene bins, 72.73% of intergenic bins, 69.45% of pathway bins, 32.36% of ORegAnno annotated bins, and 9.10% of evolutionary conserved regions (shared with primates) have statistically significant differences in RV burden. Ongoing efforts include examining additional regional characteristics using regulatory regions and protein binding domains. Our results show interesting variant differences between two ancestral populations and demonstrate that population stratification is a pervasive concern for sequence analyses.

Electronic health record phenotype in subjects with genetic variants associated with arrhythmogenic right ventricular cardiomyopathy: a study of 30,716 subjects with exome sequencing

PurposeArrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart disease. Clinical follow-up of incidental findings in ARVC-associated genes is recommended. We aimed to determine the prevalence of disease thus ascertained.MethodsIndividuals (nxa0=xa030,716) underwent exome sequencing. Variants in PKP2, DSG2, DSC2, DSP, JUP, TMEM43, or TGFβ3 that were database-listed as pathogenic or likely pathogenic were identified and evidence-reviewed. For subjects with putative loss-of-function (pLOF) variants or variants of uncertain significance (VUS), electronic health records (EHR) were reviewed for ARVC diagnosis, diagnostic criteria, and International Classification of Diseases (ICD-9) codes.ResultsEighteen subjects had pLOF variants; none of these had an EHR diagnosis of ARVC. Of 14 patients with an electrocardiogram, one had a minor diagnostic criterion; the rest were normal. A total of 184 subjects had VUS, none of whom had an ARVC diagnosis. The proportion of subjects with VUS with major (4%) or minor (13%) electrocardiogram diagnostic criteria did not differ from that of variant-negative controls. ICD-9 codes showed no difference in defibrillator use, electrophysiologic abnormalities or nonischemic cardiomyopathies in patients with pLOF or VUSs compared with controls.ConclusionpLOF variants in an unselected cohort were not associated with ARVC phenotypes based on EHR review. The negative predictive value of EHR review remains uncertain.

eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants

BackgroundWe explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy.ResultsIn this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations withxa0pu2009<u20090.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 “Disorder of Lipoid metabolism” (pdiscoveryu2009=u20092.59x10-6, preplicatingu2009=u20092.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 “Acquired Hypothyroidism” (pdiscoveryu2009=u20095.31x103, preplicatingu2009=u20091.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 “Complications peculiar to certain specified procedures” (pdiscoveryu2009=u20098.65x103, preplicatingu2009=u20094.16x10-3).xa0ConclusionIn conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.