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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.

OBJECTIVES/GOALS: Congenital anomalies (CAs) affect 3% of live births, yet the cause of 80% of CAs is unknown and for the 20% with an identified cause, variability in penetrance suggests additional risk drivers exist. Our method for identifying and categorizing CAs in electronic health record (EHR) linked biobank databases can expand and improve CA etiologic research. METHODS/STUDY POPULATION: We identified individuals with CAs in three groups: 1. Those with at least one CA 2. Those with multiple CAs (MCA), those with two or more ‘major’ CAs, and 3. Those with CAs in a specific organ system. We also created a novel quantitative approach, using phenome-wide association studies (pheWAS), for determining CA-associated genetic disease billing codes in order to separate individuals that have a known genetic cause for their CAs from those with idiopathic CAs. We updated CA phecodes, aggregates of clinical billing codes, which we used to identify CA cases in Vanderbilt’s EHR-linked biobank database, BioVU. We create a new phecode, ‘All CAs’, for researchers to quickly identify all individuals with at least one CA. We evaluate the definition of MCA using pheWAS analyses to compare ‘minor’ vs ‘major’ CA. RESULTS/ANTICIPATED RESULTS: The new CA phecode nomenclature includes 5.8 times more codes for CAs compared with the previous version (365 vs 56), improving granularity. 85 (19.7%) CA-associated genetic disease billing codes were identified through literature review. PheWAS analyses revealed an additional 16 (3.7%) genetic disease billing codes with one or more significant (p< 2.75 x10-5) association with CA-related phecodes. Identifying CA-associated genetic disease billing codes allows researchers to differentiate between idiopathic CAs and those that have a known genetic cause. PheWAS analyses of individuals with previously considered “minor” CAs showed many associated severe health problems, revealing that the differentiation between “minor” vs “major” CAs when identifying individuals with MCA in the EHR is arbitrary. DISCUSSION/SIGNIFICANCE: Our CA identification method is scalable for the growing number of EHR-linked biobanks. Differentiating between idiopathic CAs from those with known causes will increase power in studies discovering additional genetic drivers of CAs. Our novel method allows for expansion and acceleration of CA epidemiological research in EHR-linked biobank data.

Abstract Background Prior studies support a genetic basis for postoperative acute kidney injury (AKI). We conducted a genome-wide association study (GWAS), assessed the clinical utility of a polygenic risk score (PRS), and estimated the heritable component of AKI in patients who underwent noncardiac surgery. Methods We performed a retrospective large-scale genome-wide association study followed by a meta-analysis of patients who underwent noncardiac surgery at the Vanderbilt University Medical Center (“Vanderbilt” cohort) or Michigan Medicine, the academic medical center of the University of Michigan (“Michigan” cohort). In the Vanderbilt cohort, the relationship between polygenic risk score for estimated glomerular filtration rate and postoperative AKI was also tested to explore the predictive power of aggregating multiple common genetic variants associated with AKI risk. Similarly, in the Vanderbilt cohort genome-wide complex trait analysis was used to estimate the heritable component of AKI due to common genetic variants. Results The study population included 8248 adults in the Vanderbilt cohort (mean [SD] 58.05 [15.23] years, 50.2% men) and 5998 adults in Michigan cohort (56.24 [14.76] years, 49% men). Incident postoperative AKI events occurred in 959 patients (11.6%) and in 277 patients (4.6%), respectively. No loci met genome-wide significance in the GWAS and meta-analysis. PRS for estimated glomerular filtration rate explained a very small percentage of variance in rates of postoperative AKI and was not significantly associated with AKI (odds ratio 1.050 per 1 SD increase in polygenic risk score [95% CI, 0.971–1.134]). The estimated heritability among common variants for AKI was 4.5% (SE = 4.5%) suggesting low heritability. Conclusion The findings of this study indicate that common genetic variation minimally contributes to postoperative AKI after noncardiac surgery, and likely has little clinical utility for identifying high-risk patients.

Summary: 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 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.

Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury.Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models.Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10−8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10−6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000–1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320–3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372–0.835, p = 0.005) were significant in the full model.Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.

Alpha-1 antitrypsin deficiency (AATD), a relatively common autosomal recessive genetic disorder, is underdiagnosed in symptomatic individuals. We sought to compare the risk of liver transplantation associated with hepatitis C infection with AATD heterozygotes and homozygotes and determine if SERPINA1 sequencing would identify undiagnosed AATD. We performed a retrospective cohort study in a deidentified Electronic Health Record (EHR)-linked DNA biobank with 72,027 individuals genotyped for the M, Z, and S alleles in SERPINA1. We investigated liver transplantation frequency by genotype group and compared with hepatitis C infection. We performed SERPINA1 sequencing in carriers of pathogenic AATD alleles who underwent liver transplantation. Liver transplantation was associated with the Z allele (ZZ: odds ratio [OR] = 1.31, p

Abstract The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

Abstract Development of evidence‐based practice requires practice‐based evidence, which can be acquired through analysis of real‐world data from electronic health records (EHRs). The EHR contains volumes of information about patients—physical measurements, diagnoses, exposures, and markers of health behavior—that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real‐world data into reliable real‐world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real‐world data into high‐quality, fit‐for‐purpose analytical data sets used to generate real‐world evidence.

Abstract Background The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for variation in the period of follow-up or the time at which an event occurs. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring). Results In comprehensive simulations, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the EHRs of 49,792 genotyped individuals. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog. In terms of effect sizes, the hazard ratios estimated by Cox regression were strongly correlated with the odds ratios estimated by logistic regression. Conclusions As longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.

Summary: We conducted PrediXcan analysis of hydrocephalus risk in ten neurological tissues and whole blood. Decreased expression of MAEL in the brain was significantly associated (Bonferroni-adjusted p < 0.05) with hydrocephalus. PrediXcan analysis of brain imaging and genomics data in the independent UK Biobank (N = 8,428) revealed that MAEL expression in the frontal cortex is associated with white matter and total brain volumes. Among the top differentially expressed genes in brain, we observed a significant enrichment for gene-level associations with these structural phenotypes, suggesting an effect on disease risk through regulation of brain structure and integrity. We found additional support for these genes through analysis of the choroid plexus transcriptome of a murine model of hydrocephalus. Finally, differential protein expression analysis in patient cerebrospinal fluid recapitulated disease-associated expression changes in neurological tissues, but not in whole blood. Our findings provide convergent evidence highlighting the importance of tissue-specific pathways and mechanisms in the pathophysiology of hydrocephalus.

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.

Examination of predicted loss-of-function (pLOF) genetic variants allows direct identification of genes with therapeutic potential. Here, Emdin et al. perform association analysis for 3759 pLOF variants with 24 traits and highlight protective variants against cardiometabolic and immune phenotypes.

Abstract Background Family screening of a 48‐year‐old male with recently diagnosed IgG4‐related disease (IgG4‐RD) revealed unanticipated elevations in plasma IgG4 in his two healthy teenaged sons. Methods We performed gene sequencing, immune cell studies, HLA typing, and analyses of circulating cytotoxic CD4+ T lymphocytes and plasmablasts to seek clues to pathogenesis. DNA from a separate cohort of 99 patients with known IgG4‐RD was also sequenced for the presence of genetic variants in a specific gene, FGFBP2. Results The three share a previously unreported heterozygous single base deletion in fibroblast growth factor binding protein type 2 (FGFBP2), which causes a frameshift in the coding sequence. The FGFBP2 protein is secreted by cytotoxic T‐lymphocytes and binds fibroblast growth factor. The variant sequence in the FGFBP2 protein is predicted to form a disordered random coil rather than a helical‐turn‐helix structure, unable to adopt a stable conformation. The proband and the two sons had 5–10‐fold higher numbers of circulating cytotoxic CD4 + T cells and plasmablasts compared to matched controls. The three members also share a homozygous missense common variant in FGFBP2 found in heterozygous form in ~40% of the population. This common variant was found in 73% of an independent, well characterized IgG4‐RD cohort, showing enrichment in idiopathic IgG4‐RD. Conclusions The presence of a shared deleterious variant and homozygous common variant in FGFBP2 in the proband and sons strongly implicates this cytotoxic T cell product in the pathophysiology of IgG4‐RD. The high prevalence of a common FGFBP2 variant in sporadic IgG4‐RD supports the likelihood of participation in disease.

Use of general linear mixed models (GLMMs) in genetic variance analysis can quantify the relative contribution of additive effects from genetic variation on a given trait. Here, Jonathan Mosley and colleagues apply GLMM in a phenome-wide analysis and show that genetic variations in the HLA region are associated with 44 phenotypes, 5 phenotypes which were not previously reported in GWASes.

Phenome-wide association is a novel method that links sequence variants to a spectrum of phenotypes and diseases. Here the authors generate detailed mouse genetic and phenome data which links their phenome-wide association study (PheWAS) of mouse to corresponding PheWAS in human.

Purpose: This methodological study describes a technique for extracting information from deidentified electronic health records (EHRs) to identify occurrences of permanent unilateral hearing loss (UHL) and associated educational comorbidities. Method: This was an exploratory methodological study utilizing approximately 3.3 million de-identified medical records. Structured and unstructured data were extracted using both automated and manual methods. When both methods were available, positive and negative predictive values were calculated to evaluate the utility of using automated methods. Results: We defined a cohort of 471 records that met our criteria of school-age children with permanent UHL and no additional significant disabilities/diagnoses. Fifty-one percent of the children reflected in this cohort had indicators of adverse educational progress, defined as documentation of receiving educational services, speech-language therapy, and/or parental/teacher concern, with 12% of records reflecting overlapping services/concerns. Negative predictive values were generally high and positive predictive values were generally low, suggesting automated searches are useful for excluding factors of interest, butnot finding them. Conclusions: This study demonstrates the feasibility of using EHRs in examining UHL in school-age children. By restricting our cohort to individuals who were seen in audiology clinic, we were able to capture variables such as educational difficulty that are not routinely ascertained in medical contexts. The proportion of children in this cohort demonstrating a marker of adverse educational progress is consistent with numerous prior observational studies, thus providing validity to this ascertainment approach. We describe challenges encountered in creating this cohort and detail our hybrid approach to ascertaining key variables accurately.

BACKGROUND:Observations from statin clinical trials and from Mendelian randomization studies suggest that low low-density lipoprotein cholesterol (LDL-C) concentrations may be associated with increased risk of type 2 diabetes mellitus (T2DM). Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations in increased risk of T2DM. METHODS AND FINDINGS:We used de-identified electronic health records (EHRs) at Vanderbilt University Medical Center to compare the risk of T2DM in a cross-sectional study among individuals with very low (≤60 mg/dl, N = 8,943) and normal (90-130 mg/dl, N = 71,343) LDL-C levels calculated using the Friedewald formula. LDL-C levels associated with statin use, hospitalization, or a serum albumin level < 3 g/dl were excluded. We used a 2-phase approach: in 1/3 of the sample (discovery) we used T2DM phenome-wide association study codes (phecodes) to identify cases and controls, and in the remaining 2/3 (validation) we identified T2DM cases and controls using a validated algorithm. The analysis plan for the validation phase was constructed at the time of the design of that component of the study. The prevalence of T2DM in the very low and normal LDL-C groups was compared using logistic regression with adjustment for age, race, sex, body mass index (BMI), high-density lipoprotein cholesterol, triglycerides, and duration of care. Secondary analyses included prespecified stratification by sex, race, BMI, and LDL-C level. In the discovery cohort, phecodes related to T2DM were significantly more frequent in the very low LDL-C group. In the validation cohort (N = 33,039 after applying the T2DM algorithm to identify cases and controls), the risk of T2DM was increased in the very low compared to normal LDL-C group (odds ratio [OR] 2.06, 95% CI 1.80-2.37; P < 2 × 10-16). The findings remained significant in sensitivity analyses. The association between low LDL-C levels and T2DM was significant in males (OR 2.43, 95% CI 2.00-2.95; P < 2 × 10-16) and females (OR 1.74, 95% CI 1.42-2.12; P = 6.88 × 10-8); in normal weight (OR 2.18, 95% CI 1.59-2.98; P = 1.1× 10-6), overweight (OR 2.17, 95% CI 1.65-2.83; P = 1.73× 10-8), and obese (OR 2.00, 95% CI 1.65-2.41; P = 8 × 10-13) categories; and in individuals with LDL-C < 40 mg/dl (OR 2.31, 95% CI 1.71-3.10; P = 3.01× 10-8) and LDL-C 40-60 mg/dl (OR 1.99, 95% CI 1.71-2.32; P < 2.0× 10-16). The association was significant in individuals of European ancestry (OR 2.67, 95% CI 2.25-3.17; P < 2 × 10-16) but not in those of African ancestry (OR 1.09, 95% CI 0.81-1.46; P = 0.56). A limitation was that we only compared groups with very low and normal LDL-C levels; also, since this was not an inception cohort, we cannot exclude the possibility of reverse causation. CONCLUSIONS:Very low LDL-C concentrations occurring in the absence of statin treatment were significantly associated with T2DM risk in a large EHR population; this increased risk was present in both sexes and all BMI categories, and in individuals of European ancestry but not of African ancestry. Longitudinal cohort studies to assess the relationship between very low LDL-C levels not associated with lipid-lowering therapy and risk of developing T2DM will be important.

Imputation of human leukocyte antigen (HLA) alleles from SNP-level data is attractive due to importance of HLA alleles in human disease, widespread availability of genome-wide association study (GWAS) data, and expertise required for HLA sequencing. However, comprehensive evaluations of HLA imputations programs are limited. We compared HLA imputation results of HIBAG, SNP2HLA, and HLA*IMP:02 to sequenced HLA alleles in 3,265 samples from BioVU, a de-identified electronic health record database coupled to a DNA biorepository. We performed four-digit HLA sequencing for HLA-A, -B, -C, -DRB1, -DPB1, and -DQB1 using long-read 454 FLX sequencing. All samples were genotyped using both the Illumina HumanExome BeadChip platform and a GWAS platform. Call rates and concordance rates were compared by platform, frequency of allele, and race/ethnicity. Overall concordance rates were similar between programs in European Americans (EA) (0.975 [SNP2HLA]; 0.939 [HLA*IMP:02]; 0.976 [HIBAG]). SNP2HLA provided a significant advantage in terms of call rate and the number of alleles imputed. Concordance rates were lower overall for African Americans (AAs). These observations were consistent when accuracy was compared across HLA loci. All imputation programs performed similarly for low frequency HLA alleles. Higher concordance rates were observed when HLA alleles were imputed from GWAS platforms versus the HumanExome BeadChip, suggesting that high genomic coverage is preferred as input for HLA allelic imputation. These findings provide guidance on the best use of HLA imputation methods and elucidate their limitations.

Systemic loxoscelism is a rare illness resulting from the bite of the recluse spider and, in its most severe form, can lead to widespread hemolysis, coagulopathy, and death. We aim to describe the clinical features and outcomes of the largest known cohort of individuals with moderate to severe loxoscelism.We performed a retrospective, cross sectional study from January 1, 1995, to December 31, 2015, at a tertiary-care academic medical center, to determine individuals with clinical records consistent with moderate to severe loxoscelism. Age-, sex-, and race-matched controls were compared. Demographics, clinical characteristics, laboratory measures, and outcomes of individuals with loxoscelism are described. Case and control groups were compared with descriptive statistics and phenome-wide association study (PheWAS).During the time period, 57 individuals were identified as having moderate to severe loxoscelism. Of these, only 33% had an antecedent spider bite documented. Median age of individuals diagnosed with moderate to severe loxoscelism was 14 years old (IQR 9.0-24.0 years). PheWAS confirmed associations of systemic loxoscelism with 29 other phenotypes, e.g., rash, hemolytic anemia, and sepsis. Hemoglobin level dropped an average of 3.1 g/dL over an average of 2.0 days (IQR 2.0-6.0). Lactate dehydrogenase and total bilirubin levels were on average over two times their upper limit of normal values. Eighteen individuals of 32 tested had a positive direct antiglobulin (Coombs') test. Mortality was 3.5% (2/57 individuals).Systemic loxoscelism is a rare but devastating process with only a minority of patients recalling the toxic exposure; hemolysis reaches a peak at 2 days after admission, with some cases taking more than a week before recovery. In endemic areas, suspicion for systemic loxoscelism should be high in individuals, especially children and younger adults, presenting with a cutaneous ulcer and hemolysis or coagulopathy, even in the absence of a bite exposure history.

Despite the success of genome-wide association studies (GWAS) in detecting a large number of loci for complex phenotypes such as rheumatoid arthritis (RA) susceptibility, the lack of information on the causal genes leaves important challenges to interpret GWAS results in the context of the disease biology. Here, we genetically fine-map the RA risk locus at 19p13 to define causal variants, and explore the pleiotropic effects of these same variants in other complex traits. First, we combined Immunochip dense genotyping (n = 23,092 case/control samples), Exomechip genotyping (n = 18,409 case/control samples) and targeted exon-sequencing (n = 2,236 case/controls samples) to demonstrate that three protein-coding variants in TYK2 (tyrosine kinase 2) independently protect against RA: P1104A (rs34536443, OR = 0.66, P = 2.3 x 10(-21)), A928V (rs35018800, OR = 0.53, P = 1.2 x 10(-9)), and I684S (rs12720356, OR = 0.86, P = 4.6 x 10(-7)). Second, we show that the same three TYK2 variants protect against systemic lupus erythematosus (SLE, Pomnibus = 6 x 10(-18)), and provide suggestive evidence that two of the TYK2 variants (P1104A and A928V) may also protect against inflammatory bowel disease (IBD; P(omnibus) = 0.005). Finally, in a phenome-wide association study (PheWAS) assessing >500 phenotypes using electronic medical records (EMR) in >29,000 subjects, we found no convincing evidence for association of P1104A and A928V with complex phenotypes other than autoimmune diseases such as RA, SLE and IBD. Together, our results demonstrate the role of TYK2 in the pathogenesis of RA, SLE and IBD, and provide supporting evidence for TYK2 as a promising drug target for the treatment of autoimmune diseases.

The coupling of electronic medical records (EMR) with genetic data has created the potential for implementing reverse genetic approaches in humans, whereby the function of a gene is inferred from the shared pattern of morbidity among homozygotes of a genetic variant. We explored the feasibility of this approach to identify phenotypes associated with low frequency variants using Vanderbilt's EMR-based BioVU resource. We analyzed 1,658 low frequency non-synonymous SNPs (nsSNPs) with a minor allele frequency (MAF)

A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF