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Abstract Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. Mechanisms leading to preeclampsia are unclear; however, there is evidence of high heritability. We evaluated the association of polygenic scores (PGS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Non-Hispanic Black and White reproductive age females with pregnancy indications and genotypes were obtained from Vanderbilt University’s BioVU, Electronic Medical Records and Genomics network, and Penn Medicine Biobank. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PGS were acquired from Giri et al. Associations between preeclampsia and each PGS were evaluated separately by race and data source before subsequent meta-analysis. Ten-fold cross validation was used for prediction modeling. In 3504 Black and 5009 White included individuals, the rate of preeclampsia was 15.49%. In cross-ancestry meta-analysis, all PGSs were associated with preeclampsia (ORDBP = 1.10, 95% CI 1.02–1.17, p = 7.68 × 10−3; ORSBP = 1.16, 95% CI 1.09–1.23, p = 2.23 × 10−6; ORPP = 1.14, 95% CI 1.07–1.27, p = 9.86 × 10−5). Addition of PGSs to clinical prediction models did not improve predictive performance. Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.

Abstract Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.

Abstract Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30−0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69−0.88], p = 4.0 × 10−5) or immunosuppressant (n = 354, HR = 0.61 [0.38–0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44−0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Abstract Objective Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups. Results Precise SNP tracking showed no sample swap errors within the clinical testing laboratories. In contrast, when comparing predicted sex-by-genotype to the provided sex on the test requisition, we identified 110 inconsistencies from 25,015 clinical samples (0.44%), that had occurred during sample collection or accessioning. The genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density genotyping arrays. It was determined that discrepancies resulted from clerical errors (49.09%), samples from transgender participants (3.64%) and stem cell or bone marrow transplant patients (7.27%) along with undetermined sample mix-ups (40%) for which sample swaps occurred prior to arrival at genome centers, however the exact cause of the events at the sampling sites resulting in the mix-ups were not able to be determined.

Abstract Clostridioides difficile (C. diff.) infection (CDI) is a leading cause of hospital acquired diarrhea in North America and Europe and a major cause of morbidity and mortality. Known risk factors do not fully explain CDI susceptibility, and genetic susceptibility is suggested by the fact that some patients with colons that are colonized with C. diff. do not develop any infection while others develop severe or recurrent infections. To identify common genetic variants associated with CDI, we performed a genome-wide association analysis in 19,861 participants (1349 cases; 18,512 controls) from the Electronic Medical Records and Genomics (eMERGE) Network. Using logistic regression, we found strong evidence for genetic variation in the DRB locus of the MHC (HLA) II region that predisposes individuals to CDI (P > 1.0 × 10–14; OR 1.56). Altered transcriptional regulation in the HLA region may play a role in conferring susceptibility to this opportunistic enteric pathogen.

Abstract Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values

Abstract Background Evolving ARDS epidemiology and management during COVID-19 have prompted calls to reexamine the construct validity of Berlin criteria, which have been rarely evaluated in real-world data. We developed a Berlin ARDS definition (EHR-Berlin) computable in electronic health records (EHR) to (1) assess its construct validity, and (2) assess how expanding its criteria affected validity. Methods We performed a retrospective cohort study at two tertiary care hospitals with one EHR, among adults hospitalized with COVID-19 February 2020-March 2021. We assessed five candidate definitions for ARDS: the EHR-Berlin definition modeled on Berlin criteria, and four alternatives informed by recent proposals to expand criteria and include patients on high-flow oxygen (EHR-Alternative 1), relax imaging criteria (EHR-Alternatives 2–3), and extend timing windows (EHR-Alternative 4). We evaluated two aspects of construct validity for the EHR-Berlin definition: (1) criterion validity: agreement with manual ARDS classification by experts, available in 175 patients; (2) predictive validity: relationships with hospital mortality, assessed by Pearson r and by area under the receiver operating curve (AUROC). We assessed predictive validity and timing of identification of EHR-Berlin definition compared to alternative definitions. Results Among 765 patients, mean (SD) age was 57 (18) years and 471 (62%) were male. The EHR-Berlin definition classified 171 (22%) patients as ARDS, which had high agreement with manual classification (kappa 0.85), and was associated with mortality (Pearson r = 0.39; AUROC 0.72, 95% CI 0.68, 0.77). In comparison, EHR-Alternative 1 classified 219 (29%) patients as ARDS, maintained similar relationships to mortality (r = 0.40; AUROC 0.74, 95% CI 0.70, 0.79, Delong test P = 0.14), and identified patients earlier in their hospitalization (median 13 vs. 15 h from admission, Wilcoxon signed-rank test P

Abstract SLC1A4 is a trimeric neutral amino acid transporter essential for shuttling L‐serine from astrocytes into neurons. Individuals with biallelic variants in SLC1A4 are known to have spastic tetraplegia, thin corpus callosum, and progressive microcephaly (SPATCCM) syndrome, but individuals with heterozygous variants are not thought to have disease. We identify an 8‐year‐old patient with global developmental delay, spasticity, epilepsy, and microcephaly who has a de novo heterozygous three amino acid duplication in SLC1A4 (L86_M88dup). We demonstrate that L86_M88dup causes a dominant‐negative N‐glycosylation defect of SLC1A4, which in turn reduces the plasma membrane localization of SLC1A4 and the transport rate of SLC1A4 for L‐serine.

Biallelic pathogenic variants in UQCRFS1 underlie a rare form of isolated mitochondrial complex III deficiency associated with lactic acidosis and a distinctive scalp alopecia previously described in two unrelated probands. Here, we describe a participant in the Undiagnosed Diseases Network (UDN) with a dual diagnosis of two autosomal recessive disorders revealed by genome sequencing: UQCRFS1-related mitochondrial complex III deficiency and GJA8-related cataracts. Both pathogenic variants have been reported before: UQCRFS1 (NM_006003.3:c.215–1 G>C, p.Val72_Thr81del10) in a case with mitochondrial complex III deficiency and GJA8 (NM_005267.5:c.736 G>T, p.Glu246*) as a somatic change in aged cornea leading to decreased junctional coupling. A multi-modal approach combining enzyme assays and cellular proteomics analysis provided clear evidence of complex III respiratory chain dysfunction and low abundance of the Rieske iron-sulfur protein, validating the pathogenic effect of the UQCRFS1 variant. This report extends the genotypic and phenotypic spectrum for these two rare disorders and highlights the utility of deep phenotyping and genomics data to achieve diagnosis and insights into rare disease.

OBJECTIVES/GOALS: We aimed to conduct an updated genome-wide meta-analysis of keloids in expanded populations, including those most afflicted by keloids. Our overall objective was to improve understanding of keloid development though the identification and further characterization of keloid-associated genes with genetically predicted gene expression (GPGE). METHODS/STUDY POPULATION: We used publicly available summary statistics from several large-scale DNA biobanks, including the UK Biobank, FinnGen, and Biobank Japan. We also leveraged data from the Million Veterans Program and performed genome-wide association studies of keloids in BioVU and eMERGE. For each of these datasets, cases were determined from ICD-9/ICD-10 codes and phecodes. With these data we conducted fixed effects meta-analysis, both across ancestries and stratified by broad ancestry groups. This approach allowed us to consider cumulative evidence for genetic risk factors for keloids and explore potential ancestry-specific components of risk. We used FUMA for functional annotation of results and LDSC to estimate ancestry-specific heritability. We performed GPGE analysis using S-PrediXcan with GTEx v8 tissues. RESULTS/ANTICIPATED RESULTS: We detected 30 (23 novel) genomic risk loci in the cross-ancestry analysis. Major risk loci were broadly consistent between ancestries, with variable effects. Keloid heritability estimates from LDSC were 6%, 21%, and 34% for European, East Asian, and African ancestry, respectively. The top hit (P = 1.7e-77) in the cross-ancestry analysis was at a replicated variant (rs10863683) located downstream of LINC01705. GPGE analysis identified an association between decreased risk of keloids and increased expression of LINC01705 in fibroblasts (P = 3.6e10-20), which are important in wound healing. The top hit in the African-ancestry analysis (P = 5.5e-31) was a novel variant (rs34647667) in a conserved region downstream of ITGA11. ITGA11 encodes a collagen receptor and was previously associated with uterine fibroids. DISCUSSION/SIGNIFICANCE: This work significantly increases the yield of discoveries from keloid genetic association studies, describing both common and ancestry-specific effects. Stark differences in heritability support a potential adaptive origin for keloid disparities. Further work will continue to examine keloids in the broader context of other fibrotic diseases.

Background: Globally, respiratory infections contribute to significant morbidity and mortality. However, genetic determinants of respiratory infections are understudied and remain poorly understood. Methods: We conducted a genome-wide association study in 19,459 hospitalised respiratory infection cases and 101,438 controls from UK Biobank (Stage 1). We followed-up well-imputed top signals from our Stage 1 analysis in 50,912 respiratory infection cases and 150,442 controls from 11 cohorts (Stage 2). We aggregated effect estimates across studies using inverse variance-weighted meta-analyses. Additionally, we investigated the function of the top signals in order to gain understanding of the underlying biological mechanisms. Results: From our Stage 1 analysis, we report 56 signals at P

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Abstract Background Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. Methods We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5–4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. Conclusions By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Circulatory system diseases and nervous system disorders often co-occur in patients. Here the authors use eMERGE and UK BioBank data to identify genomic regions associated with both phenotypes, providing insight into the relationship between these conditions.

Background Cardiometabolic diseases are highly comorbid, but their relationship with female‐specific or overwhelmingly female‐predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross‐trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross‐trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score–based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross‐trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female‐specific health conditions.

IMPORTANCE:. Vascular dysfunction and capillary leak are common in critically ill COVID-19 patients, but identification of endothelial pathways involved in COVID-19 pathogenesis has been limited. Angiopoietin-like 4 (ANGPTL4) is a protein secreted in response to hypoxic and nutrient-poor conditions that has a variety of biological effects including vascular injury and capillary leak. OBJECTIVES:. To assess the role of ANGPTL4 in COVID-19–related outcomes. DESIGN, SETTING, AND PARTICIPANTS:. Two hundred twenty-five COVID-19 ICU patients were enrolled from April 2020 to May 2021 in a prospective, multicenter cohort study from three different medical centers, University of Washington, University of Southern California and New York University. MAIN OUTCOMES AND MEASURES:. Plasma ANGPTL4 was measured on days 1, 7, and 14 after ICU admission. We used previously published tissue proteomic data and lung single nucleus RNA (snRNA) sequencing data from specimens collected from COVID-19 patients to determine the tissues and cells that produce ANGPTL4. RESULTS:. Higher plasma ANGPTL4 concentrations were significantly associated with worse hospital mortality (adjusted odds ratio per log2 increase, 1.53; 95% CI, 1.17–2.00; p = 0.002). Higher ANGPTL4 concentrations were also associated with higher proportions of venous thromboembolism and acute respiratory distress syndrome. Longitudinal ANGPTL4 concentrations were significantly different during the first 2 weeks of hospitalization in patients who subsequently died compared with survivors (p for interaction = 8.1 × 10–5). Proteomics analysis demonstrated abundance of ANGPTL4 in lung tissue compared with other organs in COVID-19. ANGPTL4 single-nuclear RNA gene expression was significantly increased in pulmonary alveolar type 2 epithelial cells and fibroblasts in COVID-19 lung tissue compared with controls. CONCLUSIONS AND RELEVANCE:. ANGPTL4 is expressed in pulmonary epithelial cells and fibroblasts and is associated with clinical prognosis in critically ill COVID-19 patients.

Abstract Inflammation increases the risk of cardiometabolic disease. Delineating specific inflammatory pathways and biomarkers of their activity could identify the mechanistic underpinnings of the increased risk. Plasma levels of kynurenine, a metabolite involved in inflammation, associates with cardiometabolic disease risk. We used genetic approaches to identify inflammatory mechanisms associated with kynurenine variability and their relationship to cardiometabolic disease. We identified single-nucleotide polymorphisms (SNPs) previously associated with plasma kynurenine, including a missense-variant (rs3184504) in the inflammatory gene SH2B3/LNK. We examined the association between rs3184504 and plasma kynurenine in independent human samples, and measured kynurenine levels in SH2B3-knock-out mice and during human LPS-evoked endotoxemia. We conducted phenome scanning to identify clinical phenotypes associated with each kynurenine-related SNP and with a kynurenine polygenic score using the UK-Biobank (n = 456,422), BioVU (n = 62,303), and Electronic Medical Records and Genetics (n = 32,324) databases. The SH2B3 missense variant associated with plasma kynurenine levels and SH2B3 −/− mice had significant tissue-specific differences in kynurenine levels.LPS, an acute inflammatory stimulus, increased plasma kynurenine in humans. Mendelian randomization showed increased waist-circumference, a marker of central obesity, associated with increased kynurenine, and increased kynurenine associated with C-reactive protein (CRP). We found 30 diagnoses associated (FDR q

Summary: Integrating data across heterogeneous research environments is a key challenge in multi-site, collaborative research projects. While it is important to allow for natural variation in data collection protocols across research sites, it is also important to achieve interoperability between datasets in order to reap the full benefits of collaborative work. However, there are few standards to guide the data coordination process from project conception to completion. In this paper, we describe the experiences of the Clinical Sequence Evidence-Generating Research (CSER) consortium Data Coordinating Center (DCC), which coordinated harmonized survey and genomic sequencing data from seven clinical research sites from 2020 to 2022. Using input from multiple consortium working groups and from CSER leadership, we first identify 14 lessons learned from CSER in the categories of communication, harmonization, informatics, compliance, and analytics. We then distill these lessons learned into 11 recommendations for future research consortia in the areas of planning, communication, informatics, and analytics. We recommend that planning and budgeting for data coordination activities occur as early as possible during consortium conceptualization and development to minimize downstream complications. We also find that clear, reciprocal, and continuous communication between consortium stakeholders and the DCC is equally important to maintaining a secure and centralized informatics ecosystem for pooling data. Finally, we discuss the importance of actively interrogating current approaches to data governance, particularly for research studies that straddle the research-clinical divide.

The influence of X chromosome genetic variation on blood lipids and coronary heart disease (CHD) is not well understood. Here, the authors analyse X chromosome sequencing data across 65,322 multi-ancestry individuals, identifying associations of the Xq23 locus with lipid changes and reduced risk of CHD and diabetes mellitus.

Abstract Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.

Abstract Background Elevated triglycerides (TG) are associated with, and may be causal for, cardiovascular disease (CVD), and co-morbidities such as type II diabetes and metabolic syndrome. Pathogenic variants in APOA5 and APOC3 as well as risk SNVs in other genes [APOE (rs429358, rs7412), APOA1/C3/A4/A5 gene cluster (rs964184), INSR (rs7248104), CETP (rs7205804), GCKR (rs1260326)] have been shown to affect TG levels. Knowledge of genetic causes for elevated TG may lead to early intervention and targeted treatment for CVD. We previously identified linkage and association of a rare, highly conserved missense variant in SLC25A40, rs762174003, with hypertriglyceridemia (HTG) in a single large family, and replicated this association with rare, highly conserved missense variants in a European American and African American sample. Methods Here, we analyzed a longitudinal mixed-ancestry cohort (European, African and Asian ancestry, N = 8966) from the Electronic Medical Record and Genomics (eMERGE) Network. We tested associations between median TG and the genes of interest, using linear regression, adjusting for sex, median age, median BMI, and the first two principal components of ancestry. Results We replicated the association between TG and APOC3, APOA5, and risk variation at APOE, APOA1/C3/A4/A5 gene cluster, and GCKR. We failed to replicate the association between rare, highly conserved variation at SLC25A40 and TG, as well as for risk variation at INSR and CETP. Conclusions Analysis using data from electronic health records presents challenges that need to be overcome. Although large amounts of genotype data is becoming increasingly accessible, usable phenotype data can be challenging to obtain. We were able to replicate known, strong associations, but were unable to replicate moderate associations due to the limited sample size and missing drug information.

Abstract Background Genetic testing allows patients and clinicians to understand the risk of hereditary diseases. By testing early, individuals can make informed medical decisions about management which may minimize the risk of developing certain diseases. Importantly, genetic test results may also be applicable to patients’ biological relatives; thus, these results could also lead to minimizing their risk of disease. However, sharing genetic test results between patients and their relatives is scarce. The most frequently reported problems are that patients cannot clearly explain this information and relatives misinterpret the results. Smartphone apps in the healthcare field are a possible solution as they allow patients to accurately share sensitive information to others, while providing educational material to support understanding the information. However, these apps may not provide security to protect patients’ identifiable information. We developed ShareDNA, a smartphone app that (1) allows patients to securely share their genetic test results with others, (2) provides information on how to interpret these results, and (3) minimizes the amount of patient information needed to use the service. Results We recruited thirteen participants to test the usability of our app and provide feedback. We found overall that participants were comfortable with using this app and could easily learn each app function when filling out our questionnaire. Additionally, based on vocalized impressions of the usefulness of the app, participants indicated that the user-interface could be more intuitive and that we needed to add more text within the app to explain why ShareDNA is a secure service. Conclusions ShareDNA is a free smartphone app that allows patients to share their genetic test results with others, including their biological relatives. Sharing these results along with educational material will enable relatives to share accurate information and discuss their possible risk for disease with their clinical providers. As a result, appropriate testing in relatives could be improved.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition. Methods First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI). Results Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p = 1.70 × 10− 20). This effect was consistent in both pediatric (p = 9.92 × 10− 6) and adult (p = 9.73 × 10− 15) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) (p = 3.94 × 10− 8, beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout (p = 1.09 × 10− 4). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p = 3.80 × 10− 8), and another near ZFP90-CDH1 for fibrosis (rs698718, p = 2.74 × 10− 11). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses. Conclusions In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings.

Summary: Homozygotes for the higher penetrance hemochromatosis risk allele, HFE c.845G>A (p.Cys282Tyr, or C282Y), have been reported to be at a 2- to 3-fold increased risk for colorectal cancer (CRC). These results have been reported for small sample size studies with no information about age at diagnosis for CRC. An association with age at diagnosis might alter CRC screening recommendations. We analyzed two large European ancestry datasets to assess the association of HFE genotype with CRC risk and age at CRC diagnosis. The first dataset included 59,733 CRC or advanced adenoma cases and 72,351 controls from a CRC epidemiological study consortium. The second dataset included 13,564 self-reported CRC cases and 2,880,218 controls from the personal genetics company, 23andMe. No association of the common hereditary hemochromatosis (HH) risk genotype and CRC was found in either dataset. The odds ratios (ORs) for the association of CRC and HFE C282Y homozygosity were 1.08 (95% confidence interval [CI], 0.91–1.29; p = 0.4) and 1.01 (95% CI, 0.78–1.31, p = 0.9) in the two cohorts, respectively. Age at CRC diagnosis also did not differ by HFE C282Y/C282Y genotype in either dataset. These results indicate no increased CRC risk in individuals with HH genotypes and suggest that persons with HH risk genotypes can follow population screening recommendations for CRC.

Biomarker identification requires prohibitively large cohorts with gene expression and phenotype data. The approach introduced here learns polygenic predictors of expression from genetic and expression data, used to infer biomarker levels in patients with genetic and disease information.

Abstract Background The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p

Genomic knowledge is being translated into clinical care. To fully realize the value, it is critical to place credible information in the hands of clinicians in time to support clinical decision making. The electronic health record is an essential component of clinician workflow. Utilizing the electronic health record to present information to support the use of genomic medicine in clinical care to improve outcomes represents a tremendous opportunity. However, there are numerous barriers that prevent the effective use of the electronic health record for this purpose. The electronic health record working groups of the Electronic Medical Records and Genomics (eMERGE) Network and the Clinical Genome Resource (ClinGen) project, along with other groups, have been defining these barriers, to allow the development of solutions that can be tested using implementation pilots. In this paper, we present “lessons learned” from these efforts to inform future efforts leading to the development of effective and sustainable solutions that will support the realization of genomic medicine.

Abstract Background As biobanks play an increasing role in the genomic research that will lead to precision medicine, input from diverse and large populations of patients in a variety of health care settings will be important in order to successfully carry out such studies. One important topic is participants’ views towards consent and data sharing, especially since the 2011 Advanced Notice of Proposed Rulemaking (ANPRM), and subsequently the 2015 Notice of Proposed Rulemaking (NPRM) were issued by the Department of Health and Human Services (HHS) and Office of Science and Technology Policy (OSTP). These notices required that participants consent to research uses of their de-identified tissue samples and most clinical data, and allowing such consent be obtained in a one-time, open-ended or “broad” fashion. Conducting a survey across multiple sites provides clear advantages to either a single site survey or using a large online database, and is a potentially powerful way of understanding the views of diverse populations on this topic. Methods A workgroup of the Electronic Medical Records and Genomics (eMERGE) Network, a national consortium of 9 sites (13 separate institutions, 11 clinical centers) supported by the National Human Genome Research Institute (NHGRI) that combines DNA biorepositories with electronic medical record (EMR) systems for large-scale genetic research, conducted a survey to understand patients’ views on consent, sample and data sharing for future research, biobank governance, data protection, and return of research results. Results Working across 9 sites to design and conduct a national survey presented challenges in organization, meeting human subjects guidelines at each institution, and survey development and implementation. The challenges were met through a committee structure to address each aspect of the project with representatives from all sites. Each committee’s output was integrated into the overall survey plan. A number of site-specific issues were successfully managed allowing the survey to be developed and implemented uniformly across 11 clinical centers. Conclusions Conducting a survey across a number of institutions with different cultures and practices is a methodological and logistical challenge. With a clear infrastructure, collaborative attitudes, excellent lines of communication, and the right expertise, this can be accomplished successfully.

Recent studies have failed to demonstrate a causal cardioprotective effect of HDL cholesterol levels, shifting focus to the functional aspects of HDL. Phospholipid transfer protein (PLTP) is an HDL-associated protein involved in reverse cholesterol transport. This study sought to determine the genetic and nongenetic predictors of plasma PLTP activity (PLTPa), and separately, to determine whether PLTPa predicted carotid artery disease (CAAD). PLTPa was measured in 1,115 European ancestry participants from a case-control study of CAAD. A multivariate logistic regression model was used to elucidate the relationship between PLTPa and CAAD. Separately, a stepwise linear regression determined the nongenetic clinical and laboratory characteristics that best predicted PLTPa. A final stepwise regression considering both nongenetic and genetic variables identified the combination of covariates that explained maximal PLTPa variance. PLTPa was significantly associated with CAAD (7.90 × 10−9), with a 9% decrease in odds of CAAD per 1 unit increase in PLTPa (odds ratio = 0.91). Triglyceride levels (P = 0.0042), diabetes (P = 7.28 × 10−5), paraoxonase 1 (PON1) activity (P = 0.019), statin use (P = 0.026), PLTP SNP rs4810479 (P = 6.38 × 10−7), and PCIF1 SNP rs181914932 (P = 0.041) were all significantly associated with PLTPa. PLTPa is significantly inversely correlated with CAAD. Furthermore, we report a novel association between PLTPa and PON1 activity, a known predictor of CAAD.

HDL-associated paraoxonase-1 (PON1) is an enzyme whose activity is associated with cerebrovascular disease. Common PON1 genetic variants have not been consistently associated with cerebrovascular disease. Rare coding variation that likely alters PON1 enzyme function may be more strongly associated with stroke. The National Heart, Lung, and Blood Institute Exome Sequencing Project sequenced the coding regions (exomes) of the genome for heart, lung, and blood-related phenotypes (including ischemic stroke). In this sample of 4,204 unrelated participants, 496 had verified, noncardioembolic ischemic stroke. After filtering, 28 nonsynonymous PON1 variants were identified. Analysis with the sequence kernel association test, adjusted for covariates, identified significant associations between PON1 variants and ischemic stroke (P = 3.01 × 10−3). Stratified analyses demonstrated a stronger association of PON1 variants with ischemic stroke in African ancestry (AA) participants (P = 5.03 × 10−3). Ethnic differences in the association between PON1 variants with stroke could be due to the effects of PON1Val109Ile (overall P = 7.88 × 10−3; AA P = 6.52 × 10−4), found at higher frequency in AA participants (1.16% vs. 0.02%) and whose protein is less stable than the common allele. In summary, rare genetic variation in PON1 was associated with ischemic stroke, with stronger associations identified in those of AA. Increased focus on PON1 enzyme function and its role in cerebrovascular disease is warranted.

Cardiovascular disease (CVD) is the leading cause of death in developed countries. Plasma cholesterol level is a key risk factor in CVD pathogenesis. Genetic and dietary variation both influence plasma cholesterol; however, little is known about dietary interactions with genetic variants influencing the absorption and transport of dietary cholesterol. We sought to determine whether gut expressed variants predicting plasma cholesterol differentially affected the relationship between dietary and plasma cholesterol levels in 1,128 subjects (772/356 in the discovery/replication cohorts, respectively). Four single nucleotide polymorphisms (SNPs) within three genes (APOB, CETP, and NPC1L1) were significantly associated with plasma cholesterol in the discovery cohort. These were subsequently evaluated for gene-by-environment (GxE) interactions with dietary cholesterol for the prediction of plasma cholesterol, with significant findings tested for replication. Novel GxE interactions were identified and replicated for two variants: rs1042034, an APOB Ser4338Asn missense SNP and rs2072183 (in males only), a synonymous NPC1L1 SNP in linkage disequilibrium with SNPs 5′ of NPC1L1. This study identifies the presence of novel GxE and gender interactions implying that differential gut absorption is the basis for the variant associations with plasma cholesterol. These GxE interactions may account for part of the “missing heritability” not accounted for by genetic associations.

HDL-associated paraoxonase 1 (PON1) activity is associated with cardiovascular and other human diseases. As the role of genetic variants outside of the PON gene cluster on PON1 activity is unknown, we sought to identify common and rare variants in such loci. We typed 33,057 variants on the CVD chip in 1,362 subjects to test for their effects on adjusted-PON1 activity. Three novel genes (FTO, ITGAL, and SERPINA12) and the PON gene cluster had SNPs associated with PON1 arylesterase (AREase) activity. These loci were carried forward for rare-variant analysis using Exome chip genotypes in an overlapping subset of 1,051 subjects using sequence kernel association testing. PON1 (P = 2.24 × 10−4), PON3 (P = 0.022), FTO (P = 0.019), and SERPINA12 (P = 0.039) had both common and rare variants associated with PON1 AREase. ITGAL variants were associated with PON1 activity when using weighted sequence kernel association testing (SKAT) analysis (P = 2.63 × 10−3). When adjusting for the initial common variants, SERPINA12 became marginally significant (P = 0.09), whereas all other findings remained significant (P < 0.05), suggesting independent rare-variant effects. We present novel findings that common and rare variants in FTO, SERPINA12, and ITGAL predict PON1 activity. These results further link PON1 to diabetes and inflammation and may inform the role of HDL in human disease.

BackgroundRecent studies have failed to establish a causal relationship between high‐density lipoprotein cholesterol levels (HDL‐C) and cardiovascular disease (CVD), shifting focus to other HDL measures. We previously reported that smaller/denser HDL levels are protective against cerebrovascular disease. This study sought to determine which of small+medium HDL particle concentration (HDL‐P) or large HDL‐P was more strongly associated with carotid intima‐media thickening (cIMT) in an ethnically diverse cohort. Methods and ResultsIn cross‐sectional analyses of participants from the Multi Ethnic Study of Atherosclerosis (MESA), we evaluated the associations of nuclear magnetic resonance spectroscopy–measured small+medium versus large HDL‐P with cIMT measured in the common and internal carotid arteries, through linear regression. After adjustment for CVD confounders, low‐density lipoprotein cholesterol (LDL‐C), HDL‐C, and small+medium HDL‐P remained significantly and inversely associated with common (coefficient=−1.46 μm; P=0.00037; n=6512) and internal cIMT (coefficient=−3.82 μm; P=0.0051; n=6418) after Bonferroni correction for 4 independent tests (threshold for significance=0.0125; α=0.05/4). Large HDL‐P was significantly and inversely associated with both cIMT outcomes before HDL‐C adjustment; however, after adjustment for HDL‐C, the association of large HDL‐P with both common (coefficient=1.55 μm; P=0.30; n=6512) and internal cIMT (coefficient=4.84 μm; P=0.33; n=6418) was attenuated. In a separate sample of 126 men, small/medium HDL‐P was more strongly correlated with paraoxonase 1 activity (rp=0.32; P=0.00023) as compared to both total HDL‐P (rp=0.27; P=0.0024) and large HDL‐P (rp=0.02; P=0.41) measures. ConclusionsSmall+medium HDL‐P is significantly and inversely correlated with cIMT measurements. Correlation of small+medium HDL‐P with cardioprotective paraoxonase 1 activity may reflect a functional aspect of HDL responsible for this finding.

HDL-associated paraoxonase 1 (PON1) activity has been consistently associated with cardiovascular and other diseases. Vitamins C and E intake have previously been positively associated with PON1 in a subset of the Carotid Lesion Epidemiology and Risk (CLEAR) cohort. The goal of this study was to replicate these findings and determine whether other nutrient intake affected PON1 activity. To predict nutrient and mineral intake values, 1,402 subjects completed a standardized food frequency survey of their dietary habits over the past year. Stepwise regression was used to evaluate dietary and covariate effects on PON1 arylesterase activity. Five dietary components, cholesterol (P < 2.0 × 10−16), alcohol (P = 8.51 × 10−8), vitamin C (P = 7.97 × 10−5), iron (P = 0.0026), and folic acid (0.037) were independently predictive of PON1 activity. Dietary cholesterol was positively associated and predicted 5.5% of PON1 activity, second in variance explained. This study presents a novel finding of dietary cholesterol, iron, and folic acid predicting PON1 activity in humans and confirms prior reported associations, including that with vitamin C. Identifying and understanding environmental factors that affect PON1 activity is necessary to understand its role and that of HDL in human disease.

Phospholipid transfer protein activity (PLTPa) is associated with insulin levels and has been implicated in atherosclerotic disease in both mice and humans. Variation at the PLTP structural locus on chromosome 20 explains some, but not all, heritable variation in PLTPa. In order to detect quantitative trait loci (QTLs) elsewhere in the genome that affect PLTPa, we performed both oligogenic and single QTL linkage analysis on four large families (n = 227 with phenotype, n = 330 with genotype, n = 462 total), ascertained for familial combined hyperlipidemia. We detected evidence of linkage between PLTPa and chromosome 19p (lod = 3.2) for a single family and chromosome 2q (lod = 2.8) for all families. Inclusion of additional marker and exome sequence data in the analysis refined the linkage signal on chromosome 19 and implicated coding variation in LASS4, a gene regulated by leptin that is involved in ceramide synthesis. Association between PLTPa and LASS4 variation was replicated in the other three families (P= 0.02), adjusting for pedigree structure. To our knowledge, this is the first example for which exome data was used in families to identify a complex QTL that is not the structural locus.

Phospholipid transfer protein (PLTP) belongs to the lipid transfer/lipopolysaccharide-binding protein gene family. Expression of PLTP has been implicated in the development of atherosclerosis. We evaluated the effects of PLTP region tagging single nucleotide polymorphisms (SNPs) on the prediction of both carotid artery disease (CAAD) and PLTP activity. CAAD effects were evaluated in 442 Caucasian male subjects with severe CAAD and 497 vascular disease-free controls. SNP prediction of PLTP transfer activity was evaluated in both a subsample of 87 subjects enriched for an allele of interest and in a confirmation sample of 210 Caucasian males and females. Hemoglobin A1c or insulin level predicted 11–14% of age- and sex-adjusted PLTP activity. PLTP SNPs that predicted ∼11–30% of adjusted PLTP activity variance were identified in the two cohorts. For rs6065904, the allele that was associated with CAAD was also associated with elevated PLTP activity in both cohorts. SNPs associated with PLTP activity also predicted variation in LDL-cholesterol and LDL-B level only in the replication cohort. These results demonstrate that PLTP activity is strongly influenced by PLTP region polymorphisms and metabolic factors.

Plasma lipoprotein(a) (Lp[a]) level is an independent risk factor of cardiovascular disease that is under strong genetic control. We conducted a genome-wide association study of plasma Lp(a) in 386 members of a founder population that adheres to a communal lifestyle, proscribes cigarette smoking, and prepares and eats meals communally. We identified associations with 77 single nucleotide polymorphisms (SNPs) spanning 12.5 Mb on chromosome 6q26-q27 that met criteria for genome-wide significance (P ≤ 1.3 × 10−7) and were within or flanking nine genes, including LPA. We show that variation in at least six genes in addition to LPA are significantly associated with Lp(a) levels independent of each other and of the kringle IV repeat polymorphism in the LPA gene. One novel SNP in intron 37 of the LPA gene was also associated with Lp(a) levels and carotid artery disease number in unrelated Caucasians (P = 7.3 × 10−12 and 0.024, respectively), also independent of kringle IV number. This study suggests a complex genetic architecture of Lp(a) levels that may involve multiple loci on chromosome 6q26-q27.

The genetic factors associated with carotid artery disease (CAAD) are not fully known. Because of its role in lipid metabolism, we hypothesized that common genetic variation in the very low density lipoprotein receptor (VLDLR) gene is associated with severe CAAD (>80% stenosis), body mass index (BMI), and lipid traits in humans. VLDLR was resequenced for variation discovery in 92 subjects, and single nucleotide polymorphisms (tagSNPs) were chosen for genotyping in a larger cohort (n = 1,027). Of the 17 tagSNPs genotyped, one tagSNP (SNP 1226; rs1454626) located in the 5′ flanking region of VLDLR was associated with CAAD, BMI, and LDL-associated apolipoprotein B (apoB). We also identified receptor-ligand genetic interactions between VLDLR 1226 and APOE genotype for predicting CAAD case status. These findings may further our understanding of VLDLR function, its ligand APOE, and ultimately the pathogenesis of CAAD in the general population.

Paraoxonase 1 (PON1) activity is consistently predictive of vascular disease, although the genotype at four functional PON1 polymorphisms is not. To address this inconsistency, we investigated the role of all common PON1 genetic variability, as measured by tagging single-nucleotide polymorphisms (tagSNPs), in predicting PON1 activity for phenylacetate hydrolysis, LDL susceptibility to oxidation ex vivo, plasma homocysteine (Hcy) levels, and carotid artery disease (CAAD) status. The biological goal was to establish whether additional common genetic variation beyond consideration of the four known functional SNPs improves prediction of these phenotypes. PON2 and PON3 tagSNPs were secondarily evaluated. Expanded analysis of an additional 26 tagSNPs found evidence of previously undescribed common PON1 polymorphisms that affect PON1 activity independently of the four known functional SNPs. PON1 activity was not significantly correlated with LDL oxidative susceptibility, but genotypes at the PON1-108 promoter polymorphism and several other PON1 SNPs were. Neither PON1 activity nor PON1 genotype was significantly correlated with plasma Hcy levels. This study revealed previously undetected common functional PON1 polymorphisms that explain 4% of PON1 activity and a high rate of recombination in PON1, but the sum of the common PON1 locus variation does not explain the relationship between PON1 activity and CAAD.

Paraoxonase (PON1) is an HDL-associated enzyme. Low PON1 activity predicts vascular disease status and is a more reliable predictor of vascular disease than are functional PON1 genotypes. There is evidence that the relationship of PON1 to vascular disease is, in part, due to its antioxidant activity. However, the physical relationship of PON1 with HDL and the existence of cholesterol pathway regulatory elements at the PON1 locus suggest a further relationship of PON1 with lipoproteins, which may contribute to its role in vascular disease. We investigated the relationship of PON1 activity and genotype to lipid-related traits in 91 Caucasian men with severe carotid artery disease and 184 without vascular disease who were not on lipid-lowering medications. Prior studies of PON1 relationship to lipids have not stratified by disease status..We found that PON1 activity was correlated with HDL traits in controls and with LDL- and VLDL-related traits in cases. We hypothesize differences in the joint regulation of PON1 and lipoproteins in cases and controls.

Combining samples across multiple cohorts in large-scale scientific research programs is often required to achieve the necessary power for genome-wide association studies. Controlling for genomic ancestry through principal component analysis (PCA) to address the effect of population stratification is a common practice. In addition to local genomic variation, such as copy number variation and inversions, other factors directly related to combining multiple studies, such as platform and site recruitment bias, can drive the correlation patterns in PCA. In this report, we describe combination and analysis of multi-ethnic cohort with biobanks linked to electronic health records for large-scale genomic association discovery analyses. First, we outline the observed site and platform bias, in addition to ancestry differences. Second, we outline a general protocol for selecting variants for input into the subject variance-covariance matrix, the conventional PCA approach. Finally, we introduce an alternative approach to PCA by deriving components from subject loadings calculated from a reference sample. This alternative approach of generating principal components controlled for site and platform bias, in addition to ancestry differences, with the advantage of fewer covariates and degrees of freedom.principal component analysis, ancestry, biobank, loadings, genetic association study

The electronic MEdical Records & GEnomics (eMERGE) network was established in 2007 by the National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) in part to explore the utility of electronic medical records (EMRs) in genome science. The initial focus was on discovery primarily using the genome-wide association paradigm, but more recently, the network has begun evaluating mechanisms to implement new genomic information couple to clinical decision support into EMRs. Herein, we describe this evolution including the development of the individual and merged eMERGE genomic datasets, the contribution the network has made towards genomic discovery and human health, and the steps taken towards the next generation genotype-phenotype association studies and clinical implementation.

The electronic Medical Records and Genomics (eMERGE) (Phase I) network was established in 2007 to further genomic discovery using biorepositories linked to the electronic health record (EHR). In Phase II, which began in 2011, genomic discovery efforts continue and in addition the network is investigating best practices for implementing genomic medicine for patient care, in particular, the return of genomic results in the EHR for use by physicians at point-of-care. To develop strategies for addressing the challenges of implementing genomic medicine in the clinical setting, the eMERGE network is conducting studies that return clinically-relevant genomic results to research participants and their health care providers. These genomic medicine pilot studies include returning individual genetic variants associated with disease susceptibility or drug response, as well as genetic risk scores for common ‘complex’ disorders. Additionally, as part of a network-wide pharmacogenomics-related project, targeted resequencing of 84 pharmacogenes is being performed and select genotypes of pharmacogenetic relevance are being placed in the EHR to guide individualized drug therapy. Individual sites within the eMERGE network are exploring mechanisms to address incidental findings generated by resequencing of the 84 pharmacogenes. In this paper, we describe studies being conducted within the eMERGE network to develop best practices for integrating genomic findings into the EHR, and the challenges associated with such work.

Background. Paraoxonase 1 (PON1) enzymatic activity has been consistently predictive of cardiovascular disease, while the genotypes at the four functional polymorphisms at PON1 have not. The goal of this study was to identify additional variation at the PON gene cluster that improved prediction of PON1 activity and determine if these variants predict carotid artery disease (CAAD). Methods. We considered 1,328 males in a CAAD cohort. 51 tagging single-nucleotide polymorphisms (tag SNPs) across the PON cluster were evaluated to determine their effects on PON1 activity and CAAD status. Results. Six SNPs (four in PON1 and one each in PON2/3) predicted PON1 arylesterase (AREase) activity, in addition to the four previously known functional SNPs. In total, the 10 SNPs explained 30.1% of AREase activity, 5% of which was attributable to the six identified predictive SNPs. We replicate rs854567 prediction of 2.3% of AREase variance, the effects of rs3917510, and a PON3 haplotype that includes rs2375005. While AREase activity strongly predicted CAAD, none of the 10 SNPs predicting AREase predicted CAAD. Conclusions. This study identifies new genetic variants that predict additional PON1 AREase activity. Identification of SNPs associated with PON1 activity is required when evaluating the many phenotypes associated with genetic variation near PON1.