Automated Family Histories Significantly Improve Risk Prediction in an EHR.

We recently demonstrated that electronically constructed family pedigrees (e-pedigrees) have great value in epidemiologic research using electronic health record (EHR) data. Prior to this work, it has been well accepted that family health history is a major predictor for a wide spectrum of diseases, reflecting shared effects of genetics, environment, and lifestyle. With the widespread digitalization of patient data via EHRs, there is an unprecedented opportunity to use machine learning algorithms to better predict disease risk. Although predictive models have previously been constructed for a few important diseases, we currently know very little about how accurately the risk for most diseases can be predicted. It is further unknown if the incorporation of e-pedigrees in machine learning can improve the value of these models. In this study, we devised a family pedigree-driven high-throughput machine learning pipeline to simultaneously predict risks for thousands of diagnosis codes using thousands of input features. Models were built to predict future disease risk for three time windows using both Logistic Regression and XGBoost. For example, we achieved average areas under the receiver operating characteristic curves (AUCs) of 0.82, 0.77 and 0.71 for 1, 6, and 24 months, respectively using XGBoost and without e-pedigrees. When adding e-pedigree features to the XGBoost pipeline, AUCs increased to 0.83, 0.79 and 0.74 for the same three time periods, respectively. E-pedigrees similarly improved the predictions when using Logistic Regression. These results emphasize the potential value of incorporating family health history via e-pedigrees into machine learning with no further human time.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction.

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Assessment of the current status of real-world pharmacogenomic testing: informed consent, patient education, and related practices.

Introduction: The practice of informed consent (IC) for pharmacogenomic testing in clinical settings varies, and there is currently no consensus on which elements of IC to provide to patients. This study aims to assess current IC practices for pharmacogenomic testing. Methods: An online survey was developed and sent to health providers at institutions that offer clinical germline pharmacogenomic testing to assess current IC practices. Results: Forty-six completed surveys representing 43 clinical institutions offering pharmacogenomic testing were received. Thirty-two (74%) respondents obtain IC from patients with variability in elements incorporated. Results revealed that twenty-nine (67%) institutions discuss the benefits, description, and purpose of pharmacogenomic testing with patients. Less commonly discussed elements included methodology and accuracy of testing, and laboratory storage of samples. Discussion: IC practices varied widely among survey respondents. Most respondents desire the establishment of consensus IC recommendations from a trusted pharmacogenomics organization to help address these disparities.

Estimating the efficacy of pharmacogenomics over a lifetime.

It is well known that common variants in specific genes influence drug metabolism and response, but it is currently unknown what fraction of patients are given prescriptions over a lifetime that could be contraindicated by their pharmacogenomic profiles. To determine the clinical utility of pharmacogenomics over a lifetime in a general patient population, we sequenced the genomes of 300 deceased Marshfield Clinic patients linked to lifelong medical records. Genetic variants in 33 pharmacogenes were evaluated for their lifetime impact on drug prescribing using extensive electronic health records. Results show that 93% of the 300 deceased patients carried clinically relevant variants. Nearly 80% were prescribed approximately three medications on average that may have been impacted by these variants. Longitudinal data suggested that the optimal age for pharmacogenomic testing was prior to age 50, but the optimal age is greatly influenced by the stability of the population in the healthcare system. This study emphasizes the broad clinical impact of pharmacogenomic testing over a lifetime and demonstrates the potential application of genomic medicine in a general patient population for the advancement of precision medicine.

Genetic and clinical determinants of telomere length.

Many epidemiologic studies have identified important relationships between leukocyte telomere length (LTL) with genetics and health. Most of these studies have been significantly limited in scope by focusing predominantly on individual diseases or restricted to GWAS analysis. Using two large patient populations derived from Vanderbilt University and Marshfield Clinic biobanks linked to genomic and phenomic data from medical records, we investigated the inter-relationship between LTL, genomics, and human health. Our GWAS confirmed 11 genetic loci previously associated with LTL and two novel loci in SCNN1D and PITPNM1. PheWAS of LTL identified 67 distinct clinical phenotypes associated with both short and long LTL. We demonstrated that several diseases associated with LTL were related to one another but were largely independent from LTL genetics. Age of death was correlated with LTL independent of age. Those with very short LTL (<-1.5 standard deviation [SD]) died 10.4 years (p < 0.0001) younger than those with average LTL (±0.5 SD; mean age of death = 74.2 years). Likewise, those with very long LTL (>1.5 SD) died 1.9 years (p = 0.0175) younger than those with average LTL. This is consistent with the PheWAS results showing diseases associating with both short and long LTL. Finally, we estimated that the genome (12.8%) and age (8.5%) explain the largest proportion of LTL variance, whereas the phenome (1.5%) and sex (0.9%) explained a smaller fraction. In total, 23.7% of LTL variance was explained. These observations provide the rationale for expanded research to understand the multifaceted correlations between TL biology and human health over time, leading to effective LTL usage in medical applications.

Familial Hypercholesterolemia in the Electronic Medical Records and Genomics Network: Prevalence, Penetrance, Cardiovascular Risk, and Outcomes After Return of Results.

BACKGROUND: The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network. METHODS: Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR, APOB, and PCSK9. FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review. RESULTS: The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00-4.53]) and premature CHD (odds ratio, 3.68 [2.34-5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results. CONCLUSIONS: In a multisite cohort of electronic health record-linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record-linked biobanks to detect FH.

Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits.

Immunoglobulin A (IgA) mediates mucosal responses to food antigens and the intestinal microbiome and is involved in susceptibility to mucosal pathogens, celiac disease, inflammatory bowel disease, and IgA nephropathy. We performed a genome-wide association study of serum IgA levels in 41,263 individuals of diverse ancestries and identified 20 genome-wide significant loci, including 9 known and 11 novel loci. Co-localization analyses with expression QTLs prioritized candidate genes for 14 of 20 significant loci. Most loci encoded genes that produced immune defects and IgA abnormalities when genetically manipulated in mice. We also observed positive genetic correlations of serum IgA levels with IgA nephropathy, type 2 diabetes, and body mass index, and negative correlations with celiac disease, inflammatory bowel disease, and several infections. Mendelian randomization supported elevated serum IgA as a causal factor in IgA nephropathy. African ancestry was consistently associated with higher serum IgA levels and greater frequency of IgA-increasing alleles compared to other ancestries. Our findings provide novel insights into the genetic regulation of IgA levels and its potential role in human disease.

Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

The All of Us Research Program: Data quality, utility, and diversity.

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders.

Clinical and epidemiological studies have shown that circulatory system diseases and nervous system disorders often co-occur in patients. However, genetic susceptibility factors shared between these disease categories remain largely unknown. Here, we characterized pleiotropy across 107 circulatory system and 40 nervous system traits using an ensemble of methods in the eMERGE Network and UK Biobank. Using a formal test of pleiotropy, five genomic loci demonstrated statistically significant evidence of pleiotropy. We observed region-specific patterns of direction of genetic effects for the two disease categories, suggesting potential antagonistic and synergistic pleiotropy. Our findings provide insights into the relationship between circulatory system diseases and nervous system disorders which can provide context for future prevention and treatment strategies.

Association of Pathogenic Variants in Hereditary Cancer Genes With Multiple Diseases.

Importance: Knowledge about the spectrum of diseases associated with hereditary cancer syndromes may improve disease diagnosis and management for patients and help to identify high-risk individuals. Objective: To identify phenotypes associated with hereditary cancer genes through a phenome-wide association study. Design, Setting, and Participants: This phenome-wide association study used health data from participants in 3 cohorts. The Electronic Medical Records and Genomics Sequencing (eMERGEseq) data set recruited predominantly healthy individuals from 10 US medical centers from July 16, 2016, through February 18, 2018, with a mean follow-up through electronic health records (EHRs) of 12.7 (7.4) years. The UK Biobank (UKB) cohort recruited participants from March 15, 2006, through August 1, 2010, with a mean (SD) follow-up of 12.4 (1.0) years. The Hereditary Cancer Registry (HCR) recruited patients undergoing clinical genetic testing at Vanderbilt University Medical Center from May 1, 2012, through December 31, 2019, with a mean (SD) follow-up through EHRs of 8.8 (6.5) years. Exposures: Germline variants in 23 hereditary cancer genes. Pathogenic and likely pathogenic variants for each gene were aggregated for association analyses. Main Outcomes and Measures: Phenotypes in the eMERGEseq and HCR cohorts were derived from the linked EHRs. Phenotypes in UKB were from multiple sources of health-related data. Results: A total of 214 020 participants were identified, including 23 544 in eMERGEseq cohort (mean [SD] age, 47.8 [23.7] years; 12 611 women [53.6%]), 187 234 in the UKB cohort (mean [SD] age, 56.7 [8.1] years; 104 055 [55.6%] women), and 3242 in the HCR cohort (mean [SD] age, 52.5 [15.5] years; 2851 [87.9%] women). All 38 established gene-cancer associations were replicated, and 19 new associations were identified. These included the following 7 associations with neoplasms: CHEK2 with leukemia (odds ratio [OR], 3.81 [95% CI, 2.64-5.48]) and plasma cell neoplasms (OR, 3.12 [95% CI, 1.84-5.28]), ATM with gastric cancer (OR, 4.27 [95% CI, 2.35-7.44]) and pancreatic cancer (OR, 4.44 [95% CI, 2.66-7.40]), MUTYH (biallelic) with kidney cancer (OR, 32.28 [95% CI, 6.40-162.73]), MSH6 with bladder cancer (OR, 5.63 [95% CI, 2.75-11.49]), and APC with benign liver/intrahepatic bile duct tumors (OR, 52.01 [95% CI, 14.29-189.29]). The remaining 12 associations with nonneoplastic diseases included BRCA1/2 with ovarian cysts (OR, 3.15 [95% CI, 2.22-4.46] and 3.12 [95% CI, 2.36-4.12], respectively), MEN1 with acute pancreatitis (OR, 33.45 [95% CI, 9.25-121.02]), APC with gastritis and duodenitis (OR, 4.66 [95% CI, 2.61-8.33]), and PTEN with chronic gastritis (OR, 15.68 [95% CI, 6.01-40.92]). Conclusions and Relevance: The findings of this genetic association study analyzing the EHRs of 3 large cohorts suggest that these new phenotypes associated with hereditary cancer genes may facilitate early detection and better management of cancers. This study highlights the potential benefits of using EHR data in genomic medicine.

Kidney failure in Bardet-Biedl syndrome.

The aim of this study was to explore kidney failure (KF) in Bardet-Biedl syndrome (BBS), focusing on high-risk gene variants, demographics, and morbidity. We employed the Clinical Registry Investigating BBS (CRIBBS) to identify 44 (7.2%) individuals with KF out of 607 subjects. Molecularly confirmed BBS was identified in 37 KF subjects and 364 CRIBBS registrants. KF was concomitant with recessive causal variants in 12 genes, with BBS10 the most predominant causal gene (26.6%), while disease penetrance was highest in SDCCAG8 (100%). Two truncating variants were present in 67.6% of KF cases. KF incidence was increased in genes not belonging to the BBSome or chaperonin-like genes (p < 0.001), including TTC21B, a new candidate BBS gene. Median age of KF was 12.5 years, with the vast majority of KF occurring by 30 years (86.3%). Females were disproportionately affected (77.3%). Diverse uropathies were identified, but were not more common in the KF group (p = 0.672). Kidney failure was evident in 11 of 15 (73.3%) deaths outside infancy. We conclude that KF poses a significant risk for premature morbidity in BBS. Risk factors for KF include female sex, truncating variants, and genes other than BBSome/chaperonin-like genes highlighting the value of comprehensive genetic investigation.

Genetic association of primary nonresponse to anti-TNFα therapy in patients with inflammatory bowel disease.

OBJECTIVES: Primary nonresponse (PNR) to antitumor necrosis factor-α (TNFα) biologics is a serious concern in patients with inflammatory bowel disease (IBD). We aimed to identify the genetic variants associated with PNR. PATIENTS AND METHODS: Patients were recruited from outpatient GI clinics and PNR was determined using both clinical and endoscopic findings. A case-control genome-wide association study was performed in 589 IBD patients and associations were replicated in an independent cohort of 293 patients. Effect of the associated variant on gene expression and TNFα secretion was assessed by cell-based assays. Pleiotropic effects were investigated by Phenome-wide association study (PheWAS). RESULTS: We identified rs34767465 as associated with PNR to anti-TNFα therapy (odds ratio: 2.07, 95% CI, 1.46-2.94, P = 2.43 × 10-7, [replication odds ratio: 1.8, 95% CI, 1.04-3.16, P = 0.03]). rs34767465 is a multiple-tissue expression quantitative trait loci for FAM114A2. Using RNA-sequencing and protein quantification from HapMap lymphoblastoid cell lines (LCLs), we found a significant decrease in FAM114A2 mRNA and protein expression in both heterozygous and homozygous genotypes when compared to wild type LCLs. TNFα secretion was significantly higher in THP-1 cells [differentiated into macrophages] with FAM114A2 knockdown versus controls. Immunoblotting experiments showed that depletion of FAM114A2 impaired autophagy-related pathway genes suggesting autophagy-mediated TNFα secretion as a potential mechanism. PheWAS showed rs34767465 was associated with comorbid conditions found in IBD patients (derangement of joints [P = 3.7 × 10-4], pigmentary iris degeneration [P = 5.9 × 10-4], diverticulum of esophagus [P = 7 × 10-4]). CONCLUSIONS: We identified a variant rs34767465 associated with PNR to anti-TNFα biologics, which increases TNFα secretion through mechanism related to autophagy. rs34767465 may also explain the comorbidities associated with IBD.

An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records.

Electronic health records (EHR) provide an unprecedented opportunity to conduct large, cost-efficient, population-based studies. However, the studies of heterogeneous diseases, such as chronic obstructive pulmonary disease (COPD), often require labor-intensive clinical review and testing, limiting widespread use of these important resources. To develop a generalizable and efficient method for accurate identification of large COPD cohorts in EHRs, a COPD datamart was developed from 3420 participants meeting inclusion criteria in the Mass General Brigham Biobank. Training and test sets were selected and labeled with gold-standard COPD classifications obtained from chart review by pulmonologists. Multiple classes of algorithms were built utilizing both structured (e.g. ICD codes) and unstructured (e.g. medical notes) data via elastic net regression. Models explicitly including and excluding spirometry features were compared. External validation of the final algorithm was conducted in an independent biobank with a different EHR system. The final COPD classification model demonstrated excellent positive predictive value (PPV; 91.7%), sensitivity (71.7%), and specificity (94.4%). This algorithm performed well not only within the MGBB, but also demonstrated similar or improved classification performance in an independent biobank (PPV 93.5%, sensitivity 61.4%, specificity 90%). Ancillary comparisons showed that the classification model built including a binary feature for FEV1/FVC produced substantially higher sensitivity than those excluding. This study fills a gap in COPD research involving population-based EHRs, providing an important resource for the rapid, automated classification of COPD cases that is both cost-efficient and requires minimal information from unstructured medical records.

The genetic architecture of plasma kynurenine includes cardiometabolic disease mechanisms associated with the SH2B3 gene.

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 < 0.05) with the SH2B3 variant, but not with SNPs mapping to genes known to regulate tryptophan-kynurenine metabolism. Plasma kynurenine may be a biomarker of acute and chronic inflammation involving the SH2B3 pathways. Its regulation lies upstream of CRP, suggesting that kynurenine may be a biomarker of one inflammatory mechanism contributing to increased cardiometabolic disease risk.

Penetrance of Breast Cancer Susceptibility Genes From the eMERGE III Network.

Background: Unbiased estimates of penetrance are challenging but critically important to make informed choices about strategies for risk management through increased surveillance and risk-reducing interventions. Methods: We studied the penetrance and clinical outcomes of 7 breast cancer susceptibility genes (BRCA1, BRCA2, TP53, CHEK2, ATM, PALB2, and PTEN) in almost 13 458 participants unselected for personal or family history of breast cancer. We identified 242 female participants with pathogenic or likely pathogenic variants in 1 of the 7 genes for penetrance analyses, and 147 women did not previously know their genetic results. Results: Out of the 147 women, 32 women were diagnosed with breast cancer at an average age of 52.8 years. Estimated penetrance by age 60 years ranged from 17.8% to 43.8%, depending on the gene. In clinical-impact analysis, 42.3% (95% confidence interval = 31.3% to 53.3%) of women had taken actions related to their genetic results, and 2 new breast cancer cases were identified within the first 12 months after genetic results disclosure. Conclusions: Our study provides population-based penetrance estimates for the understudied genes CHEK2, ATM, and PALB2 and highlights the importance of using unselected populations for penetrance studies. It also demonstrates the potential clinical impact of genetic testing to improve health care through early diagnosis and preventative screening.

Associations of Genetically Predicted Lp(a) (Lipoprotein [a]) Levels With Cardiovascular Traits in Individuals of European and African Ancestry.

BACKGROUND: Lp(a) (lipoprotein [a]) levels are higher in individuals of African ancestry (AA) than in individuals of European ancestry (EA). We examined associations of genetically predicted Lp(a) levels with (1) atherosclerotic cardiovascular disease subtypes: coronary heart disease, cerebrovascular disease, peripheral artery disease, and abdominal aortic aneurysm and (2) nonatherosclerotic cardiovascular disease phenotypes, stratified by ancestry. METHODS: We performed (1) Mendelian randomization analyses for previously reported cardiovascular associations and (2) Mendelian randomization-phenome-wide association analyses for novel associations. Analyses were stratified by ancestry in electronic Medical Records and Genomics, United Kingdom Biobank, and Million Veteran Program cohorts separately and in a combined cohort of 804 507 EA and 103 580 AA participants. RESULTS: In Mendelian randomization analyses using the combined cohort, a 1-SD genetic increase in Lp(a) level was associated with atherosclerotic cardiovascular disease subtypes in EA-odds ratio and 95% CI for coronary heart disease 1.28 (1.16-1.41); cerebrovascular disease 1.14 (1.07-1.21); peripheral artery disease 1.22 (1.11-1.34); abdominal aortic aneurysm 1.28 (1.17-1.40); in AA, the effect estimate was lower than in EA and nonsignificant for coronary heart disease 1.11 (0.99-1.24) and cerebrovascular disease 1.06 (0.99-1.14) but similar for peripheral artery disease 1.16 (1.01-1.33) and abdominal aortic aneurysm 1.34 (1.11-1.62). In EA, a 1-SD genetic increase in Lp(a) level was associated with aortic valve disorders 1.34 (1.10-1.62), mitral valve disorders 1.18 (1.09-1.27), congestive heart failure 1.12 (1.05-1.19), and chronic kidney disease 1.07 (1.01-1.14). In AA, no significant associations were noted for aortic valve disorders 1.08 (0.94-1.25), mitral valve disorders 1.02 (0.89-1.16), congestive heart failure 1.02 (0.95-1.10), or chronic kidney disease 1.05 (0.99-1.12). Mendelian randomization-phenome-wide association analyses identified novel associations in EA with arterial thromboembolic disease, nonaortic aneurysmal disease, atrial fibrillation, cardiac conduction disorders, and hypertension. CONCLUSIONS: Many cardiovascular associations of genetically increased Lp(a) that were significant in EA were not significant in AA. Lp(a) was associated with atherosclerotic cardiovascular disease in four major arterial beds in EA but only with peripheral artery disease and abdominal aortic aneurysm in AA. Additionally, novel cardiovascular associations were detected in EA.

E-Pedigrees: a large-scale automatic family pedigree prediction application.

MOTIVATION: The use and functionality of Electronic Health Records (EHR) have increased rapidly in the past few decades. EHRs are becoming an important depository of patient health information and can capture family data. Pedigree analysis is a longstanding and powerful approach that can gain insight into the underlying genetic and environmental factors in human health, but traditional approaches to identifying and recruiting families are low-throughput and labor-intensive. Therefore, high-throughput methods to automatically construct family pedigrees are needed. RESULTS: We developed a stand-alone application: Electronic Pedigrees, or E-Pedigrees, which combines two validated family prediction algorithms into a single software package for high throughput pedigrees construction. The convenient platform considers patients' basic demographic information and/or emergency contact data to infer high-accuracy parent-child relationship. Importantly, E-Pedigrees allows users to layer in additional pedigree data when available and provides options for applying different logical rules to improve accuracy of inferred family relationships. This software is fast and easy to use, is compatible with different EHR data sources, and its output is a standard PED file appropriate for multiple downstream analyses. AVAILABILITY AND IMPLEMENTATION: The Python 3.3+ version E-Pedigrees application is freely available on: https://github.com/xiayuan-huang/E-pedigrees.

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis.

OBJECTIVES: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. METHODS: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. RESULTS: The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10-5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. CONCLUSION: A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.