A multisite, quasiexperimental trial of a college course to support student mental health during the COVID-19 pandemic.

OBJECTIVE: The purpose of this study was to investigate the acceptability, appropriateness, feasibility, and preliminary effectiveness of a three-credit college Wellness and Resilience Course (WRC) for improving student mental health and well-being outcomes in the context of the coronavirus disease 2019 (COVID-19) pandemic. METHOD: Undergraduate students aged 18-24 years old on five campuses in Western Pennsylvania or West Virginia who had either enrolled in the WRC (n = 81) or were attending university as usual (i.e., not enrolled in the WRC; n = 171) participated in surveys at baseline (beginning of semester), end of semester, and 3-month follow-up during the Spring and Fall 2020 semesters. RESULTS: Overall, students rated the WRC as acceptable, appropriate, and feasible. From baseline to the end of semester, students who received the WRC reported significant improvements in psychological flexibility (d = 0.30), mindfulness (d = 0.42), distress tolerance (d = 0.36), and use of dysfunctional and adaptive coping skills (d = 0.32), compared with students who did not receive the WRC. At follow-up, all gains remained statistically significant and students who received the WRC additionally reported significant improvements in stress (d = 0.44) and life satisfaction (d = 0.35) compared with students who did not receive the WRC. CONCLUSIONS: These findings offer preliminary evidence that college courses focused on mental wellness may be an important component of campus strategies to increase universal access to mental health support and skills. This study was registered on clinicaltrials.gov on April 8, 2020.

Experiences of Exploitation and Associations With Economic Abuse in Adolescent Dating Relationships: Findings From a U.S. Cross-Sectional Survey.

Adolescent relationship abuse (ARA) (i.e. physical, sexual, psychological, or economic abuse in the context of romantic relationships) is associated with adverse health outcomes, including anxiety, depression, suicidality, unintended pregnancy, and substance misuse. A related phenomenon, reproductive coercion involves interference with the reproductive decision making of a partner with the intention of promoting pregnancy or controlling outcomes of a pregnancy. Reproductive coercion is associated with unintended pregnancy, partner violence, and sexually transmitted infections. Little is known about the intersection between economic ARA, sexual exploitation, and reproductive coercion. This paper explores the intersections between reproductive coercion, transactional sex, and economic abuse victimization in adolescent dating relationships. In an online survey, 1,752 adolescents (ages 13-17) were asked about economic adolescent relationship abuse (educational, employment and financial interference), transactional sex, reproductive coercion, and contraceptive access within their relationships. We assessed associations with chi-square tests and logistic regression analysis. Youth who experienced economic ARA (70%, 1,232) reported financial dependence on their partner, contraceptive access, and reproductive coercion (74-83%; p-values<0.001) more often than their counterparts without economic ARA. Adolescents experiencing economic abuse were more likely to report transactional sex (aOR = 2.76, CI [2.12, 3.60], p < .001), depending on a partner to pay for contraception or birth control (aOR = 2.20, CI [1.71, 2.84], p < .001), and reproductive coercion (aOR = 3.20, CI [2.37, 4.32], p < .001). Youth-serving providers and agencies should be aware of intersections between economic ARA, transactional sex, financial dependence, and reproductive coercion, particularly for adolescents with health-related social needs.

Menopause in people with cystic fibrosis.

This study aimed to describe the menopause experience of people with cystic fibrosis (CF). We conducted a computer-based cross-sectional survey of women with CF 25 years or older at 10 US CF centers exploring a range of sexual and reproductive health concerns, including menopause. We used descriptive statistics to analyze results. Of 460 participants, 5 (3%) were perimenopausal and 34 (7%) were postmenopausal. Of participants perimenopausal or menopausal (n = 39), 97% reported the following menopausal symptoms occurring at least once a week: most commonly early wake-up (83%); stiffness/soreness in joints, neck, or shoulders (65%); and night sweats (65%). Among menopausal participants, the median self-reported age at menopause was 48.5 years (interquartile range, 5.5 y). Thirty percent experienced worsened CF symptoms during menopause, and 42% experienced worsening CF symptoms after menopause. Twenty-four percent of menopausal participants were on estrogen therapy and 15% on estrogen and progesterone therapy. Three-fourths of participants using hormone therapy reported no change in their CF symptoms. One percent of the 460 survey participants reported discussing menopause with their CF provider, despite 19% wanting to discuss this topic with their CF team. This is the first study to describe menopause symptoms of people with CF. People with CF experience a variety of menopausal symptoms and often report a worsening of their CF symptoms after menopause, suggesting an interplay between female sex hormones and CF. Larger studies are needed comparing the sexual and reproductive health experiences and care needs of people with CF in the menopause transition to the general population.

Sexual and reproductive health experiences and care of adult women with cystic fibrosis.

BACKGROUND: As survival and health improve in people with cystic fibrosis (CF), more women with CF (wwCF) are considering their sexual and reproductive health (SRH). This study compared SRH experiences, behaviors, and care utilization of wwCF to the general population and defined CF-impacted considerations and care preferences. METHODS: We surveyed wwCF aged ≥25 years regarding SRH and compared results to the US National Survey of Family Growth (NSFG;n = 4357) and friend controls(n = 123). We used descriptive statistics and chi-squared/Fisher's exact testing and linear regression for comparisons. RESULTS: A total of 460 wwCF (mean age 36.1 years) completed the survey. WwCF were less likely to report current contraceptive use (43%vs76% NSFG, p<0.001;60% friends, p = 0.005). Nearly 25% of wwCF reported worsened CF symptoms during their menstrual cycles, 50% experienced urinary incontinence, and 80% vulvovaginal candidiasis. WwCF were significantly less likely to be parents (46%vs62% friends, p = 0.015) and to have experienced pregnancy (37%vs78% NSFG, p<0.001;58% friends, p = 0.002). More wwCF required medical assistance to conceive (29%vs12% NSFG, p<0.001 and 5% friends, p<0.001). Eighty-four percent of wwCF view their CF doctor as their main physician and 41% report no primary care provider (vs19% friends; p<0.001). WwCF report suboptimal rates of contraceptive and preconception counseling/care and are less likely to have received HPV vaccination (42%vs55%friends, p = 0.02). Despite desiring SRH conversations with their CF team, <50% report discussing SRH topics. CONCLUSION: WwCF have significantly different SRH experiences than non-CF peers. They report suboptimal SRH care compared to their preferences highlighting an urgent need to encourage SRH counseling/care in the CF model.

Assessing feasibility of an adolescent relationship abuse prevention program for girls.

INTRODUCTION: Sexual violence and relationship abuse are prevalent among adolescents and programs promoting gender equity, reproductive justice, and healthy relationships are key strategies for prevention. While such "gender transformative" approaches appear promising for boys, they have not been evaluated among girls. This study assessed the feasibility of this community-based program, called Sisterhood 2.0, among girls in socially disadvantaged urban neighborhoods in Pittsburgh, Pennsylvania. METHODS: This quasi-experimental trial examined feasibility of Sisterhood 2.0 (n = 246), delivered through 8 weekly sessions, assessed through attendance, retention and satisfaction. Participants completed surveys at baseline and end of program assessing other relevant measures. Generalized linear mixed models estimated changes from baseline to follow up comparing intervention to control participants. RESULTS: Eleven neighborhoods were assigned to Sisterhood 2.0 (n = 5 neighborhoods) or job-readiness training (n = 6 neighborhoods). Girls were between the ages of 13 and 19, 8-10th graders (59%), and self-identified as Black (69%). Participants most often attended because they thought the program would be interesting (74%) and returned because of the women teaching the program (71%). Girls reported experiences with physical adolescent relationship abuse (ARA) (30% in both arms), emotional ARA (66% intervention; 56% control), or sexual ARA (11% intervention; 12% control). Physical ARA perpetration was high in both arms (intervention: 47%; control: 46%). Significant intervention effects were observed in recognition of abuse (ß = 0.41, 95% confidence interval 0.03-0.78). No other significant intervention effects were observed. CONCLUSIONS: Community-based gender-transformative programming for girls is feasible and may be a promising approach for addressing interpersonal violence and promoting sexual health.

Rapid-Cycle Community Assessment of Health-Related Social Needs of Children and Families During Coronavirus Disease 2019.

OBJECTIVE: To identify unmet health and social resource needs during a county-wide coronavirus disease 19 (COVID-19) stay-at-home order and phased re-opening in Western Pennsylvania. METHODS: With public health, social service, and community partners connected through an ongoing academic-community collaborative, we developed and fielded a weekly repeated cross-sectional electronic survey assessing usage of and unmet need for health and social service resources. Using 10 weeks of surveys (April 3-June 11, 2020) by Allegheny County residents, we examined variation in responses by week and by sociodemographic characteristics using chi-square tests. We shared written reports weekly and discussed emerging trends with community partners. RESULTS: Participants ranged from 229 to 1001 per week. Unmet need for at least 1 health or health-related social need resource varied by week, ranging from 55% (95% confidence interval [CI] 50%-59%) of participants in week 2 to 43% (95% CI 37%-49%) of participants in week 9 (P = .006). Increased use of at least 1 resource ranged from 53% (95% CI 47%-58%) of participants in week 3 to 36% (95% CI 31%-42%) in week 9 (P < .001). Unmet need for food and financial assistance peaked early during the stay-at-home order, while unmet need for mental health care rose later. Unmet need for food assistance varied significantly by race and ethnicity and by household prepandemic income. CONCLUSIONS: Over half of families with children reported unmet health or social service needs during the first month of a county-wide COVID-19 stay-at-home order. Unmet needs varied with race, ethnicity, and income and with duration of the stay-at-home order.

Correction to: Systematic characterization of germline variants from the DiscovEHR study endometrial carcinoma population.

Following publication of the original article [1], the authors reported that Fig. 1 was not correctly processed during the production process. The correct Fig. 1 is given below.

Systematic characterization of germline variants from the DiscovEHR study endometrial carcinoma population.

BACKGROUND: Endometrial cancer (EMCA) is the fifth most common cancer among women in the world. Identification of potentially pathogenic germline variants from individuals with EMCA will help characterize genetic features that underlie the disease and potentially predispose individuals to its pathogenesis. METHODS: The Geisinger Health System's (GHS) DiscovEHR cohort includes exome sequencing on over 50,000 consenting patients, 297 of whom have evidence of an EMCA diagnosis in their electronic health record. Here, rare variants were annotated as potentially pathogenic. RESULTS: Eight genes were identified as having increased burden in the EMCA cohort relative to the non-cancer control cohort. None of the eight genes had an increased burden in the other hormone related cancer cohort from GHS, suggesting they can help characterize the underlying genetic variation that gives rise to EMCA. Comparing GHS to the cancer genome atlas (TCGA) EMCA germline data illustrated 34 genes with potentially pathogenic variation and eight unique potentially pathogenic variants that were present in both studies. Thus, similar germline variation among genes can be observed in unique EMCA cohorts and could help prioritize genes to investigate for future work. CONCLUSION: In summary, this systematic characterization of potentially pathogenic germline variants describes the genetic underpinnings of EMCA through the use of data from a single hospital system.

Author Correction: Rare variants in drug target genes contributing to complex diseases, phenome-wide.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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.

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.

Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis.

BACKGROUND: Missing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these methods appropriately. Here, we provide detailed procedures for when and how to conduct imputation of EHR laboratory results. OBJECTIVE: The objective of this study was to demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered. METHODS: We analyzed clinical laboratory measures from 602,366 patients in the EHR of Geisinger Health System in Pennsylvania, USA. Using these data, we constructed a representative set of complete cases and assessed the performance of 12 different imputation methods for missing data that was simulated based on 4 mechanisms of missingness (missing completely at random, missing not at random, missing at random, and real data modelling). RESULTS: Our results showed that several methods, including variations of Multivariate Imputation by Chained Equations (MICE) and softImpute, consistently imputed missing values with low error; however, only a subset of the MICE methods was suitable for multiple imputation. CONCLUSIONS: The analyses we describe provide an outline of considerations for dealing with missing EHR data, steps that researchers can perform to characterize missingness within their own data, and an evaluation of methods that can be applied to impute clinical data. While the performance of methods may vary between datasets, the process we describe can be generalized to the majority of structured data types that exist in EHRs, and all of our methods and code are publicly available.

Exome Sequencing-Based Screening for BRCA1/2 Expected Pathogenic Variants Among Adult Biobank Participants.

Importance: Detection of disease-associated variants in the BRCA1 and BRCA2 (BRCA1/2) genes allows for cancer prevention and early diagnosis in high-risk individuals. Objectives: To identify pathogenic and likely pathogenic (P/LP) BRCA1/2 variants in an unselected research cohort, and to characterize the features associated with P/LP variants. Design, Setting, and Participants: This is a cross-sectional study of adult volunteers (n = 50 726) who underwent exome sequencing at a single health care system (Geisinger Health System, Danville, Pennsylvania) from January 1, 2014, to March 1, 2016. Participants are part of the DiscovEHR cohort and were identified through the Geisinger MyCode Community Health Initiative. They consented to a research protocol that included sequencing and return of actionable test results. Clinical data from electronic health records and clinical visits were correlated with variants. Comparisons were made between those with (cases) and those without (controls) P/LP variants in BRCA1/2. Main Outcomes: Prevalence of P/LP BRCA1/2 variants in cohort, proportion of variant carriers not previously ascertained through clinical testing, and personal and family history of relevant cancers among BRCA1/2 variant carriers and noncarriers. Results: Of the 50 726 health system patients who underwent exome sequencing, 50 459 (99.5%) had no expected pathogenic BRCA1/2 variants and 267 (0.5%) were BRCA1/2 carriers. Of the 267 cases (148 [55.4%] were women and 119 [44.6%] were men with a mean [range] age of 58.9 [23-90] years), 183 (68.5%) received clinically confirmed results in their electronic health record. Among the 267 participants with P/LP BRCA1/2 variants, 219 (82.0%) had no prior clinical testing, 95 (35.6%) had BRCA1 variants, and 172 (64.4%) had BRCA2 variants. Syndromic cancer diagnoses were present in 11 (47.8%) of the 23 deceased BRCA1/2 carriers and in 56 (20.9%) of all 267 BRCA1/2 carriers. Among women, 31 (20.9%) of 148 variant carriers had a personal history of breast cancer, compared with 1554 (5.2%) of 29 880 noncarriers (odds ratio [OR], 5.95; 95% CI, 3.88-9.13; P < .001). Ovarian cancer history was present in 15 (10.1%) of 148 variant carriers and in 195 (0.6%) of 29 880 variant noncarriers (OR, 18.30; 95% CI, 10.48-31.4; P < .001). Among 89 BRCA1/2 carriers without prior testing but with comprehensive personal and family history data, 44 (49.4%) did not meet published guidelines for clinical testing. Conclusions and Relevance: This study found that compared with previous clinical care, exome sequencing-based screening identified 5 times as many individuals with P/LP BRCA1/2 variants. These findings suggest that genomic screening may identify BRCA1/2-associated cancer risk that might otherwise remain undetected within health care systems and may provide opportunities to reduce morbidity and mortality in patients.

Protein-Truncating Variants at the Cholesteryl Ester Transfer Protein Gene and Risk for Coronary Heart Disease.

RATIONALE: Therapies that inhibit CETP (cholesteryl ester transfer protein) have failed to demonstrate a reduction in risk for coronary heart disease (CHD). Human DNA sequence variants that truncate the CETP gene may provide insight into the efficacy of CETP inhibition. OBJECTIVE: To test whether protein-truncating variants (PTVs) at the CETP gene were associated with plasma lipid levels and CHD. METHODS AND RESULTS: We sequenced the exons of the CETP gene in 58 469 participants from 12 case-control studies (18 817 CHD cases, 39 652 CHD-free controls). We defined PTV as those that lead to a premature stop, disrupt canonical splice sites, or lead to insertions/deletions that shift frame. We also genotyped 1 Japanese-specific PTV in 27561 participants from 3 case-control studies (14 286 CHD cases, 13 275 CHD-free controls). We tested association of CETP PTV carrier status with both plasma lipids and CHD. Among 58 469 participants with CETP gene-sequencing data available, average age was 51.5 years and 43% were women; 1 in 975 participants carried a PTV at the CETP gene. Compared with noncarriers, carriers of PTV at CETP had higher high-density lipoprotein cholesterol (effect size, 22.6 mg/dL; 95% confidence interval, 18-27; P<1.0×10-4), lower low-density lipoprotein cholesterol (-12.2 mg/dL; 95% confidence interval, -23 to -0.98; P=0.033), and lower triglycerides (-6.3%; 95% confidence interval, -12 to -0.22; P=0.043). CETP PTV carrier status was associated with reduced risk for CHD (summary odds ratio, 0.70; 95% confidence interval, 0.54-0.90; P=5.1×10-3). CONCLUSIONS: Compared with noncarriers, carriers of PTV at CETP displayed higher high-density lipoprotein cholesterol, lower low-density lipoprotein cholesterol, lower triglycerides, and lower risk for CHD.

Association of Rare and Common Variation in the Lipoprotein Lipase Gene With Coronary Artery Disease.

Importance: The activity of lipoprotein lipase (LPL) is the rate-determining step in clearing triglyceride-rich lipoproteins from the circulation. Mutations that damage the LPL gene (LPL) lead to lifelong deficiency in enzymatic activity and can provide insight into the relationship of LPL to human disease. Objective: To determine whether rare and/or common variants in LPL are associated with early-onset coronary artery disease (CAD). Design, Setting, and Participants: In a cross-sectional study, LPL was sequenced in 10 CAD case-control cohorts of the multinational Myocardial Infarction Genetics Consortium and a nested CAD case-control cohort of the Geisinger Health System DiscovEHR cohort between 2010 and 2015. Common variants were genotyped in up to 305 699 individuals of the Global Lipids Genetics Consortium and up to 120 600 individuals of the CARDIoGRAM Exome Consortium between 2012 and 2014. Study-specific estimates were pooled via meta-analysis. Exposures: Rare damaging mutations in LPL included loss-of-function variants and missense variants annotated as pathogenic in a human genetics database or predicted to be damaging by computer prediction algorithms trained to identify mutations that impair protein function. Common variants in the LPL gene region included those independently associated with circulating triglyceride levels. Main Outcomes and Measures: Circulating lipid levels and CAD. Results: Among 46 891 individuals with LPL gene sequencing data available, the mean (SD) age was 50 (12.6) years and 51% were female. A total of 188 participants (0.40%; 95% CI, 0.35%-0.46%) carried a damaging mutation in LPL, including 105 of 32 646 control participants (0.32%) and 83 of 14 245 participants with early-onset CAD (0.58%). Compared with 46 703 noncarriers, the 188 heterozygous carriers of an LPL damaging mutation displayed higher plasma triglyceride levels (19.6 mg/dL; 95% CI, 4.6-34.6 mg/dL) and higher odds of CAD (odds ratio = 1.84; 95% CI, 1.35-2.51; P < .001). An analysis of 6 common LPL variants resulted in an odds ratio for CAD of 1.51 (95% CI, 1.39-1.64; P = 1.1 × 10-22) per 1-SD increase in triglycerides. Conclusions and Relevance: The presence of rare damaging mutations in LPL was significantly associated with higher triglyceride levels and presence of coronary artery disease. However, further research is needed to assess whether there are causal mechanisms by which heterozygous lipoprotein lipase deficiency could lead to coronary artery disease.

IDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHR.

A wide range of patient health data is recorded in Electronic Health Records (EHR). This data includes diagnosis, surgical procedures, clinical laboratory measurements, and medication information. Together this information reflects the patient's medical history. Many studies have efficiently used this data from the EHR to find associations that are clinically relevant, either by utilizing International Classification of Diseases, version 9 (ICD-9) codes or laboratory measurements, or by designing phenotype algorithms to extract case and control status with accuracy from the EHR. Here we developed a strategy to utilize longitudinal quantitative trait data from the EHR at Geisinger Health System focusing on outpatient metabolic and complete blood panel data as a starting point. Comprehensive Metabolic Panel (CMP) as well as Complete Blood Counts (CBC) are parts of routine care and provide a comprehensive picture from high level screening of patients' overall health and disease. We randomly split our data into two datasets to allow for discovery and replication. We first conducted a genome-wide association study (GWAS) with median values of 25 different clinical laboratory measurements to identify variants from Human Omni Express Exome beadchip data that are associated with these measurements. We identified 687 variants that associated and replicated with the tested clinical measurements at p<5×10-08. Since longitudinal data from the EHR provides a record of a patient's medical history, we utilized this information to further investigate the ICD-9 codes that might be associated with differences in variability of the measurements in the longitudinal dataset. We identified low and high variance patients by looking at changes within their individual longitudinal EHR laboratory results for each of the 25 clinical lab values (thus creating 50 groups - a high variance and a low variance for each lab variable). We then performed a PheWAS analysis with ICD-9 diagnosis codes, separately in the high variance group and the low variance group for each lab variable. We found 717 PheWAS associations that replicated at a p-value less than 0.001. Next, we evaluated the results of this study by comparing the association results between the high and low variance groups. For example, we found 39 SNPs (in multiple genes) associated with ICD-9 250.01 (Type-I diabetes) in patients with high variance of plasma glucose levels, but not in patients with low variance in plasma glucose levels. Another example is the association of 4 SNPs in UMOD with chronic kidney disease in patients with high variance for aspartate aminotransferase (discovery p-value: 8.71×10-09 and replication p-value: 2.03×10-06). In general, we see a pattern of many more statistically significant associations from patients with high variance in the quantitative lab variables, in comparison with the low variance group across all of the 25 laboratory measurements. This study is one of the first of its kind to utilize quantitative trait variance from longitudinal laboratory data to find associations among genetic variants and clinical phenotypes obtained from an EHR, integrating laboratory values and diagnosis codes to understand the genetic complexities of common diseases.

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

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.

INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES.

Electronic health records (EHR) provide a comprehensive resource for discovery, allowing unprecedented exploration of the impact of genetic architecture on health and disease. The data of EHRs also allow for exploration of the complex interactions between health measures across health and disease. The discoveries arising from EHR based research provide important information for the identification of genetic variation for clinical decision-making. Due to the breadth of information collected within the EHR, a challenge for discovery using EHR based data is the development of high-throughput tools that expose important areas of further research, from genetic variants to phenotypes. Phenome-Wide Association studies (PheWAS) provide a way to explore the association between genetic variants and comprehensive phenotypic measurements, generating new hypotheses and also exposing the complex relationships between genetic architecture and outcomes, including pleiotropy. EHR based PheWAS have mainly evaluated associations with case/control status from International Classification of Disease, Ninth Edition (ICD-9) codes. While these studies have highlighted discovery through PheWAS, the rich resource of clinical lab measures collected within the EHR can be better utilized for high-throughput PheWAS analyses and discovery. To better use these resources and enrich PheWAS association results we have developed a sound methodology for extracting a wide range of clinical lab measures from EHR data. We have extracted a first set of 21 clinical lab measures from the de-identified EHR of participants of the Geisinger MyCodeTM biorepository, and calculated the median of these lab measures for 12,039 subjects. Next we evaluated the association between these 21 clinical lab median values and 635,525 genetic variants, performing a genome-wide association study (GWAS) for each of 21 clinical lab measures. We then calculated the association between SNPs from these GWAS passing our Bonferroni defined p-value cutoff and 165 ICD-9 codes. Through the GWAS we found a series of results replicating known associations, and also some potentially novel associations with less studied clinical lab measures. We found the majority of the PheWAS ICD-9 diagnoses highly related to the clinical lab measures associated with same SNPs. Moving forward, we will be evaluating further phenotypes and expanding the methodology for successful extraction of clinical lab measurements for research and PheWAS use. These developments are important for expanding the PheWAS approach for improved EHR based discovery.