Mayo Genome Consortia: a genotype-phenotype resource for genome-wide association studies with an application to the analysis of circulating bilirubin levels

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

Phenotypic distinctions in depression and anxiety: a comparative analysis of comorbid and isolated cases

BACKGROUND

Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety.

METHODS

Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses.

RESULTS

Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10-27), sleep apnea (OR 1.71; p = 1 × 10-22), and type II diabetes (OR 1.74; p = 9 × 10-18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10-25), benign skin neoplasms (OR 1.61; p = 2 × 10-17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10-12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression.

CONCLUSIONS

While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people

BACKGROUND

Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.

METHODS

Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.

RESULTS

In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity.

CONCLUSIONS

This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.

Genetics of hair graying with age

Hair graying is an early and obvious phenotypic and physiological trait with age in humans. Several recent advances in molecular biology and genetics have increased our understanding of the mechanisms of hair graying, which elucidate genes related to the synthesis, transport, and distribution of melanin in hair follicles, as well as genes regulating these processes above. Therefore, we review these advances and examine the trends in the genetic aspects of hair graying from enrichment theory, Genome-Wide association studies, whole exome sequencing, gene expression studies, and animal models for hair graying with age, aiming to overview the changes in hair graying at the genetic level and establish the foundation for future research. Meanwhile, by summarizing the genetics, it's of great value to explore the possible mechanism, treatment, or even prevention of hair graying with age.

Characterizing common and rare variations in non-traditional glycemic biomarkers using multivariate approaches on multi-ancestry ARIC study

Glycated hemoglobin, fasting glucose, glycated albumin, and fructosamine are biomarkers that reflect different aspects of the glycemic process. Genetic studies of these glycemic biomarkers can shed light on unknown aspects of type 2 diabetes genetics and biology. While there exists several GWAS of glycated hemoglobin and fasting glucose, very few GWAS have focused on glycated albumin or fructosamine. We performed a multi-phenotype GWAS of glycated albumin and fructosamine from 7,395 White and 2,016 Black participants in the Atherosclerosis Risk in Communities (ARIC) study on the common variants from genotyped/imputed data. We found 2 genome-wide significant loci, one mapping to known type 2 diabetes gene (ARAP1/STARD10, p = 2.8 × 10-8) and another mapping to a novel gene (UGT1A, p = 1.4 × 10-8) using multi-omics gene mapping strategies in diabetes-relevant tissues. We identified additional loci that were ancestry-specific (e.g., PRKCA from African ancestry individuals, p = 1.7 × 10-8) and sex-specific (TEX29 locus in males only, p = 3.0 × 10-8). Further, we implemented multi-phenotype gene-burden tests on whole-exome sequence data from 6,590 White and 2,309 Black ARIC participants. Eleven genes across different rare variant aggregation strategies were exome-wide significant only in multi-ancestry analysis. Four out of 11 genes had notable enrichment of rare predicted loss of function variants in African ancestry participants despite smaller sample size. Overall, 8 out of 15 loci/genes were implicated to influence these biomarkers via glycemic pathways. This study illustrates improved locus discovery and potential effector gene discovery by leveraging joint patterns of related biomarkers across entire allele frequency spectrum in multi-ancestry analyses. Most of the loci/genes we identified have not been previously implicated in studies of type 2 diabetes, and future investigation of the loci/genes potentially acting through glycemic pathways may help us better understand risk of developing type 2 diabetes.

The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Biobanks of linked clinical patient histories and biological samples are an efficient strategy to generate large cohorts for modern genetics research. Biobank recruitment varies by factors such as geographic catchment and sampling strategy, which affect biobank demographics and research utility. Here, we describe the Michigan Genomics Initiative (MGI), a single-health-system biobank currently consisting of >91,000 participants recruited primarily during surgical encounters at Michigan Medicine. The surgical enrollment results in a biobank enriched for many diseases and ideally suited for a disease genetics cohort. Compared with the much larger population-based UK Biobank, MGI has higher prevalence for nearly all diagnosis-code-based phenotypes and larger absolute case counts for many phenotypes. Genome-wide association study (GWAS) results replicate known findings, thereby validating the genetic and clinical data. Our results illustrate that opportunistic biobank sampling within single health systems provides a unique and complementary resource for exploring the genetics of complex diseases.

Identifying the prevalence of clinically actionable drug-gene interactions in a health system biorepository to guide pharmacogenetics implementation services

Understanding patterns of drug-gene interactions (DGIs) is important for advancing the clinical implementation of pharmacogenetics (PGx) into routine practice. Prior studies have estimated the prevalence of DGIs, but few have confirmed DGIs in patients with known genotypes and prescriptions, nor have they evaluated clinician characteristics associated with DGI-prescribing. This retrospective chart review assessed prevalence of DGI, defined as a medication prescription in a patient with a PGx phenotype that has a clinical practice guideline recommendation to adjust therapy or monitor drug response, for patients enrolled in a research genetic biorepository linked to electronic health records (EHRs). The prevalence of prescriptions for medications with pharmacogenetic (PGx) guidelines, proportion of prescriptions with DGI, location of DGI prescription, and clinical service of the prescriber were evaluated descriptively. Seventy-five percent (57,058/75,337) of patients had a prescription for a medication with a PGx guideline. Up to 60% (n = 26,067/43,647) of patients had at least one DGI when considering recommendations to adjust or monitor therapy based on genotype. The majority (61%) of DGIs occurred in outpatient prescriptions. Proton pump inhibitors were the most common DGI medication for 11 of 12 clinical services. Almost 25% of patients (n = 10,706/43,647) had more than one unique DGI, and, among this group of patients, 61% had a DGI with more than one gene. These findings can inform future clinical implementation by identifying key stakeholders for initial DGI prescriptions, helping to inform workflows. The high prevalence of multigene interactions identified also support the use of panel PGx testing as an implementation strategy.

Serum bilirubin and kidney function: a Mendelian randomization study

Background

Further investigation is needed to determine the causal effects of serum bilirubin on the risk of chronic kidney disease (CKD).

Methods

This study is a Mendelian randomization (MR) analysis. Among the well-known single-nucleotide polymorphisms (SNPs) related to serum bilirubin levels, rs4149056 in the SLCO1B1 gene was selected as the genetic instrument for single-variant MR analysis, as it was found to be less related to possible confounders than other SNPs. The association between genetic predisposition for bilirubin levels and estimated glomerular filtration rate (eGFR) or CKD was assessed in 337 129 individuals of white British ancestry from the UK Biobank cohort. Two-sample MR based on summary-level data was also performed. SNPs related to total or direct bilirubin levels were collected from a previous genome-wide association study and confounder-associated SNPs were discarded. The independent CKDGen meta-analysis data for CKD were employed as the outcome summary statistics.

Results

The alleles of rs4149056 associated with higher bilirubin levels were associated with better kidney function in the UK Biobank data. In the summary-level MR, both of the genetically predicted total bilirubin {per 5 µmol/L increase; odds ratio [OR] 0.931 [95% confidence interval (CI) 0.871-0.995]} and direct bilirubin [per 1 µmol/L increase; OR 0.910 (95% CI 0.834-0.993)] levels were significantly associated with a lower risk of CKD, supported by the causal estimates from various MR sensitivity analyses.

Conclusion

Genetic predisposition for higher serum bilirubin levels is associated with better kidney function. This result suggests that higher serum bilirubin levels may have causal protective effects against kidney function impairment.

The individual and global impact of copy-number variants on complex human traits

The impact of copy-number variations (CNVs) on complex human traits remains understudied. We called CNVs in 331,522 UK Biobank participants and performed genome-wide association studies (GWASs) between the copy number of CNV-proxy probes and 57 continuous traits, revealing 131 signals spanning 47 phenotypes. Our analysis recapitulated well-known associations (e.g., 1q21 and height), revealed the pleiotropy of recurrent CNVs (e.g., 26 and 16 traits for 16p11.2-BP4-BP5 and 22q11.21, respectively), and suggested gene functionalities (e.g., MARF1 in female reproduction). Forty-eight CNV signals (38%) overlapped with single-nucleotide polymorphism (SNP)-GWASs signals for the same trait. For instance, deletion of PDZK1, which encodes a urate transporter scaffold protein, decreased serum urate levels, while deletion of RHD, which encodes the Rhesus blood group D antigen, associated with hematological traits. Other signals overlapped Mendelian disorder regions, suggesting variable expressivity and broad impact of these loci, as illustrated by signals mapping to Rotor syndrome (SLCO1B1/3), renal cysts and diabetes syndrome (HNF1B), or Charcot-Marie-Tooth (PMP22) loci. Total CNV burden negatively impacted 35 traits, leading to increased adiposity, liver/kidney damage, and decreased intelligence and physical capacity. Thirty traits remained burden associated after correcting for CNV-GWAS signals, pointing to a polygenic CNV architecture. The burden negatively correlated with socio-economic indicators, parental lifespan, and age (survivorship proxy), suggesting a contribution to decreased longevity. Together, our results showcase how studying CNVs can expand biological insights, emphasizing the critical role of this mutational class in shaping human traits and arguing in favor of a continuum between Mendelian and complex diseases.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

Genetically raised serum bilirubin levels and lung cancer: a cohort study and Mendelian randomisation using UK Biobank

BACKGROUND

Moderately raised serum bilirubin levels are associated with lower rates of lung cancer, particularly among smokers. It is not known whether these relationships reflect antioxidant properties or residual confounding.

OBJECTIVE

This study aimed to investigate potential causal relationships between serum total bilirubin and lung cancer incidence using one-sample Mendelian randomisation (MR) and UK Biobank.

METHODS

We instrumented serum total bilirubin level using two variants (rs887829 and rs4149056) that together explain ~40% of population-level variability and are linked to mild hereditary hyperbilirubinaemia. Lung cancer events occurring after recruitment were identified from national cancer registries. Observational and genetically instrumented incidence rate ratios (IRRs) and rate differences per 10 000 person-years (PYs) by smoking status were estimated.

RESULTS

We included 377 294 participants (median bilirubin 8.1 μmol/L (IQR 6.4-10.4)) and 2002 lung cancer events in the MR analysis. Each 5 μmol/L increase in observed bilirubin levels was associated with 1.2/10 000 PY decrease (95% CI 0.7 to 1.8) in lung cancer incidence. The corresponding MR estimate was a decrease of 0.8/10 000 PY (95% CI 0.1 to 1.4). The strongest associations were in current smokers where a 5 μmol/L increase in observed bilirubin levels was associated with a decrease in lung cancer incidence of 10.2/10 000 PY (95% CI 5.5 to 15.0) and an MR estimate of 6.4/10 000 PY (95% CI 1.4 to 11.5). For heavy smokers (≥20/day), the MR estimate was an incidence decrease of 23.1/10 000 PY (95% CI 7.3 to 38.9). There was no association in never smokers and no mediation by respiratory function.

CONCLUSION

Genetically raised serum bilirubin, common across human populations, may protect people exposed to high levels of smoke oxidants against lung cancers.

Identification of Susceptibility Loci for Spontaneous Coronary Artery Dissection

Importance

Spontaneous coronary artery dissection (SCAD), an idiopathic disorder that predominantly affects young to middle-aged women, has emerged as an important cause of acute coronary syndrome, myocardial infarction, and sudden cardiac death.

Objective

To identify common single-nucleotide variants (SNVs) associated with SCAD susceptibility.

Design, Setting, and Participants

This single-center genome-wide association study examined approximately 5 million genotyped and imputed SNVs and subsequent SNV-targeted replication analysis results in individuals enrolled in the Mayo Clinic SCAD registry from August 30, 2011, to August 2, 2018. Data analysis was performed from June 21, 2017, to December 30, 2019.

Main Outcomes and Measures

Genetic loci and positional candidate genes associated with SCAD.

Results

This study included 484 white women with SCAD (mean [SD] age, 46.6 [9.2] years) and 1477 white female controls in the discovery cohort (mean [SD] age, 64.0 [14.5] years) and 183 white women with SCAD (mean [SD] age, 47.1 [9.9] years) and 340 white female controls in the replication cohort (mean [SD] age, 51.0 [15.3] years). Associations with SCAD risk reached genome-wide significance at 3 loci (1q21.3 [OR, 1.78; 95% CI, 1.51-2.09; P = 2.63 × 10-12], 6p24.1 [OR, 1.77; 95% CI, 1.51-2.09; P = 7.09 × 10-12], and 12q13.3 [OR, 1.67; 95% CI, 1.42-1.97; P = 3.62 × 10-10]), and 7 loci had evidence suggestive of an association (1q24.2 [OR, 2.10; 95% CI, 1.58-2.79; P = 2.88 × 10-7], 3q22.3 [OR, 1.47; 95% CI, 1.26-1.71; P = 6.65 × 10-7], 4q34.3 [OR, 1.84; 95% CI, 1.44-2.35; P = 9.80 × 10-7], 8q24.3 [OR, 2.57; 95% CI, 1.76-3.75; P = 9.65 × 10-7], 15q21.1 [OR, 1.75; 95% CI, 1.40-2.18; P = 7.23 × 10-7], 16q24.1 [OR, 1.91; 95% CI, 1.49-2.44; P = 2.56 × 10-7], and 21q22.11 [OR, 2.11; 95% CI, 1.59-2.82; P = 3.12 × 10-7]) after adjusting for the top 5 principal components. Associations were validated for 5 of the 10 risk alleles in the replication cohort. In a meta-analysis of the discovery and replication cohorts, associations for the 5 SNVs were significant, with relatively large effect sizes (1q21.3 [OR, 1.77; 95% CI, 1.54-2.03; P = 3.26 × 10-16], 6p24.1 [OR, 1.71; 95% CI, 1.49-1.97; P = 4.59 × 10-14], 12q13.3 [OR, 1.69; 95% CI, 1.47-1.94; P = 1.42 × 10-13], 15q21.1 [OR, 1.79; 95% CI, 1.48-2.17; P = 2.12 × 10-9], and 21q22.11 [OR, 2.18; 95% CI, 1.70-2.81; P = 1.09 × 10-9]). Each index SNV was within or near a gene highly expressed in arterial tissue and previously linked to SCAD (PHACTR1) and/or other vascular disorders (LRP1, LINC00310, and FBN1).

Conclusions and Relevance

This study revealed 5 replicated risk loci and positional candidate genes for SCAD, most of which are associated with extracoronary arteriopathies. Moreover, the alternate alleles of 3 SNVs have been previously associated with atherosclerotic coronary artery disease, further implicating allelic susceptibility to coronary artery atherosclerosis vs dissection.

CORR® ORS Richard A. Brand Award: Disruption in Peroxisome Proliferator-Activated Receptor-γ (PPARG) Increases Osteonecrosis Risk Through Genetic Variance and Pharmacologic Modulation

BACKGROUND

The pathophysiology of osteonecrosis of the femoral head (ONFH) is poorly understood, and the diagnosis is idiopathic in as many as 40% of patients. Genetic and epigenetic etiologies have been postulated, yet no single nucleotide polymorphisms (SNPs) with intuitive biologic implications have been elucidated.

QUESTIONS/PURPOSES

(1) Do individuals with ONFH share common biologically relevant genetic variants associated with disease development? (2) What is the mechanism by which these SNPs may impact the expression or function of the affected gene or protein?

METHODS

This retrospective genome-wide association study (GWAS) evaluated participants from the Mayo Clinic Biobank and Mayo Clinic Genome Consortium between August 2009 and March 2017. We included every patient with atraumatic ONFH in each of these respective registries and every control patient in a previous GWAS with an acceptable platform to perform statistical imputation. The study was performed in two phases, with an initial discovery cohort and a subsequent validation cohort. The initial discovery cohort consisted of 102 patients with ONFH and 4125 controls. A logistic regression analysis was used to evaluate associations between SNPs and the risk of ONFH, adjusted for age and sex. Seven SNPs were identified in a gene of biological interest, peroxisome proliferator-activated receptor gamma (PPARG), which were then evaluated in a subsequent validation cohort of 38 patients with ONFH and 464 controls. Age, sex, race, and previous steroid exposure were similar between patients with ONFH and controls in both the discovery and validation cohorts. Separate from the two-phase genetic investigation, we performed targeted pharmacosurveillance to evaluate the risk association between the use of antidiabetic thiazolidinediones, a class of PPARG agonists, and development of ONFH by referencing 9,638,296 patient records for individuals treated at Mayo Clinic.

RESULTS

A combined analysis of the discovery and validation cohorts revealed that seven SNPs were tightly clustered adjacent to the 3' end of PPARG, suggesting an association with the risk of ONFH (p = 1.58 x 10-5.50 x10). PPARG gene-level significance was achieved (p = 3.33 x 10) when all seven SNPs were considered. SNP rs980990 had the strongest association with the risk of ONFH (odds ratio [OR], 1.95; 95% CI, 1.46-2.59; p = 5.50 x 10).The seven identified SNPs were mapped to a region near the PPARG gene and fell in a highly conserved region consisting of several critical transcription factor binding sites. Nucleotide polymorphisms at these sites may compromise three-dimensional chromatin organization and alter PPARG 3' end interactions with its 5' promoter and transcription start site. Pharmacosurveillance identified that patients who were exposed to thiazolidinediones had an increased relative risk of developing ONFH of 5.6 (95% CI, 4.5-7.1).

CONCLUSIONS

We found that disruption of PPARG regulatory domains is linked to an increased risk of ONFH. Mechanistically, aberrant regulation of PPARG compromises musculoskeletal differentiation because this master regulator creates a proadipogenic and antiosteogenic state. Furthermore, PPARG alters steroid metabolism and vasculogenesis, processes that are inextricably linked with ONFH. Pharmacologically, predisposition to ONFH was further exposed with thiazolidinedione use, which upregulates the expression of PPARG and is known to alter bone metabolism. Collectively, these findings provide a foundation to perform confirmatory studies of our proposed mechanism in preclinical models to develop screening diagnostics and potential therapies in patients with limited options.

LEVEL OF EVIDENCE

Level III, prognostic study.

Genome-Wide Association Study (GWAS) on Bilirubin Concentrations in Subjects with Metabolic Syndrome: Sex-Specific GWAS Analysis and Gene-Diet Interactions in a Mediterranean Population

Although, for decades, increased serum bilirubin concentrations were considered a threatening sign of underlying liver disease and had been associated with neonatal jaundice, data from recent years show that bilirubin is a powerful antioxidant and suggest that slightly increased serum bilirubin concentrations are protective against oxidative stress-related diseases, such as cardiovascular diseases. Therefore, a better understanding of the gene-diet interactions in determining serum bilirubin concentrations is needed. None of the previous genome-wide association studies (GWAS) on bilirubin concentrations has been stratified by sex. Therefore, considering the increasing interest in incorporating the gender perspective into nutritional genomics, our main aim was to carry out a GWAS on total serum bilirubin concentrations in a Mediterranean population with metabolic syndrome, stratified by sex. Our secondary aim was to explore, as a pilot study, the presence of gene-diet interactions at the GWAS level. We included 430 participants (188 men and 242 women, aged 55–75 years, and with metabolic syndrome) in the PREDIMED Plus-Valencia study. Global and sex-specific GWAS were undertaken to analyze associations and gene-diet interaction on total serum bilirubin. Adherence (low and high) to the Mediterranean diet (MedDiet) was analyzed as the dietary modulator. In the GWAS, we detected more than 55 SNPs associated with serum bilirubin at p < 5 × 10⁻⁸ (GWAS level). The top-ranked were four SNPs (rs4148325 (p = 9.25 × 10⁻²⁴), rs4148324 (p = 9.48 × 10⁻²⁴), rs6742078 (p = 1.29 × 10⁻²³), rs887829 (p = 1.39 × 10⁻²³), and the rs4148324 (p = 9.48 × 10⁻²⁴)) in the UGT1A1 (UDP glucuronosyltransferase family 1 member A1) gene, which replicated previous findings revealing the UGT1A1 as the major locus. In the sex-specific GWAS, the top-ranked SNPs at the GWAS level were similar in men and women (the lead SNP was the rs4148324-UGT1A1 in both men (p = 4.77 × 10⁻¹¹) and women (p = 2.15 × 10⁻¹⁴), which shows homogeneous genetic results for the major locus. There was more sex-specific heterogeneity for other minor genes associated at the suggestive level of GWAS significance (p < 1 × 10⁻⁵). We did not detect any gene-MedDiet interaction at p < 1 × 10⁻⁵ for the major genetic locus, but we detected some gene-MedDiet interactions with other genes at p < 1 × 10⁻⁵, and even at the GWAS level for the IL17B gene (p = 3.14 × 10⁻⁸). These interaction results, however, should be interpreted with caution due to our small sample size. In conclusion, our study provides new data, with a gender perspective, on genes associated with total serum bilirubin concentrations in men and women, and suggests possible additional modulations by adherence to MedDiet.

Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) have recently gained great attention due to their ability to prioritize complex trait-associated genes and promote potential therapeutics development for complex human diseases. TWAS integrates genotypic data with expression quantitative trait loci (eQTLs) to predict genetically regulated gene expression components and associates predictions with a trait of interest. As such, TWAS can prioritize genes whose differential expressions contribute to the trait of interest and provide mechanistic explanation of complex trait(s). Tissue-specific eQTL information grants TWAS the ability to perform association analysis on tissues whose gene expression profiles are otherwise hard to obtain, such as liver and heart. However, as eQTLs are tissue context-dependent, whether and how the tissue-specificity of eQTLs influences TWAS gene prioritization has not been fully investigated. In this study, we addressed this question by adopting two distinct TWAS methods, PrediXcan and UTMOST, which assume single tissue and integrative tissue effects of eQTLs, respectively. Thirty-eight baseline laboratory traits in 4,360 antiretroviral treatment-naïve individuals from the AIDS Clinical Trials Group (ACTG) studies comprised the input dataset for TWAS. We performed TWAS in a tissue-specific manner and obtained a total of 430 significant gene-trait associations (q-value < 0.05) across multiple tissues. Single tissue-based analysis by PrediXcan contributed 116 of the 430 associations including 64 unique gene-trait pairs in 28 tissues. Integrative tissue-based analysis by UTMOST found the other 314 significant associations that include 50 unique gene-trait pairs across all 44 tissues. Both analyses were able to replicate some associations identified in past variant-based genome-wide association studies (GWAS), such as high-density lipoprotein (HDL) and CETP (PrediXcan, q-value = 3.2e-16). Both analyses also identified novel associations. Moreover, single tissue-based and integrative tissuebased analysis shared 11 of 103 unique gene-trait pairs, for example, PSRC1-low-density lipoprotein (PrediXcan's lowest q-value = 8.5e-06; UTMOST's lowest q-value = 1.8e-05). This study suggests that single tissue-based analysis may have performed better at discovering gene-trait associations when combining results from all tissues. Integrative tissue-based analysis was better at prioritizing genes in multiple tissues and in trait-related tissue. Additional exploration is needed to confirm this conclusion. Finally, although single tissue-based and integrative tissue-based analysis shared significant novel discoveries, tissue context-dependency of eQTLs impacted TWAS gene prioritization. This study provides preliminary data to support continued work on tissue contextdependency of eQTL studies and TWAS.

Mutation responsible for congenital photosensitivity and hyperbilirubinemia in Southdown sheep

OBJECTIVE To identify the genetic cause for congenital photosensitivity and hyperbilirubinemia (CPH) in Southdown sheep. ANIMALS 73 Southdown sheep from a CPH research flock and 48 sheep of various breeds from commercial flocks without CPH. PROCEDURES Whole-genome sequencing was performed for a phenotypically normal Southdown sheep heterozygous for CPH. Heterozygous variants within Slco1b3 coding exons were identified, and exons that contained candidate mutations were amplified by PCR assay methods for Sanger sequencing. Blood samples from the other 72 Southdown sheep of the CPH research flock were used to determine plasma direct and indirect bilirubin concentrations. Southdown sheep with a plasma total bilirubin concentration < 0.3 mg/dL were classified as controls, and those with a total bilirubin concentration ≥ 0.3 mg/dL and signs of photosensitivity were classified as mutants. Sanger sequencing was used to determine the Slco1b3 genotype for all sheep. Genotypes were compared between mutants and controls of the CPH research flock and among all sheep. Protein homology was measured across 8 species to detect evolutionary conservation of Slco1b. RESULTS A nonsynonymous mutation at ovine Chr3:193,691,195, which generated a glycine-to-arginine amino acid change within the predicted Slco1b3 protein, was significantly associated with hyperbilirubinemia and predicted to be deleterious. That amino acid was conserved across 7 other mammalian species. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested a nonsynonymous mutation in Slco1b3 causes CPH in Southdown sheep. This disease appears to be similar to Rotor syndrome in humans. Sheep with CPH might be useful animals for Rotor syndrome research.

Leveraging electronic health records to study pleiotropic effects on bipolar disorder and medical comorbidities

Patients with bipolar disorder (BD) have a high prevalence of comorbid medical illness. However, the mechanisms underlying these comorbidities with BD are not well known. Certain genetic variants may have pleiotropic effects, increasing the risk of BD and other medical illnesses simultaneously. In this study, we evaluated the association of BD-susceptibility genetic variants with various medical conditions that tend to co-exist with BD, using electronic health records (EHR) data linked to genome-wide single-nucleotide polymorphism (SNP) data. Data from 7316 Caucasian subjects were used to test the association of 19 EHR-derived phenotypes with 34 SNPs that were previously reported to be associated with BD. After Bonferroni multiple testing correction, P<7.7 × 10(-5) was considered statistically significant. The top association findings suggested that the BD risk alleles at SNP rs4765913 in CACNA1C gene and rs7042161 in SVEP1 may be associated with increased risk of 'cardiac dysrhythmias' (odds ratio (OR)=1.1, P=3.4 × 10(-3)) and 'essential hypertension' (OR=1.1, P=3.5 × 10(-3)), respectively. Although these associations are not statistically significant after multiple testing correction, both genes have been previously implicated with cardiovascular phenotypes. Moreover, we present additional evidence supporting these associations, particularly the association of the SVEP1 SNP with hypertension. This study shows the potential for EHR-based analyses of large cohorts to discover pleiotropic effects contributing to complex psychiatric traits and commonly co-occurring medical conditions.

Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records

BACKGROUND

Major depressive disorder (MDD) is often comorbid with other chronic mental and physical health conditions. Although the literature widely acknowledges the association of many chronic conditions with the risk of MDD, the relative importance of these conditions on MDD risk in the presence of other conditions is not well investigated. In this study, we aimed to quantify the relative contribution of selected chronic conditions to identify the conditions most influential to MDD risk in adults and identify differences by age.

METHODS

This study used electronic health record (EHR) data on patients empanelled with primary care at Mayo Clinic in June 2013. A validated EHR-based algorithm was applied to identify newly diagnosed MDD patients between 2000 and 2013. Non-MDD controls were matched 1:1 to MDD cases on birth year (±2 years), sex, and outpatient clinic visits in the same year of MDD case diagnosis. Twenty-four chronic conditions defined by Chronic Conditions Data Warehouse were ascertained in both cases and controls using diagnosis codes within 5 years of index dates (diagnosis dates for cases, and the first clinic visit dates for matched controls). For each age group (45 years or younger, between 46 and 60, and over 60 years), conditional logistic regression models were used to test the association between each condition and subsequent MDD risk, adjusting for educational attainment and obesity. The relative influence of these conditions on the risk of MDD was quantified using gradient boosting machine models.

RESULTS

A total of 11,375 incident MDD cases were identified between 2000 and 2013. Most chronic conditions (except for eye conditions) were associated with risk of MDD, with different association patterns observed depending on age. Among 24 chronic conditions, the greatest relative contribution was observed for diabetes mellitus for subjects aged ≤ 60 years and rheumatoid arthritis/osteoarthritis for those over 60 years.

CONCLUSIONS

Our results suggest that specific chronic conditions such as diabetes mellitus and rheumatoid arthritis/osteoarthritis may have greater influence than others on the risk of MDD.

Identifying genetically driven clinical phenotypes using linear mixed models

We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1-1.2), P=9.8 × 10(-11)) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3-1.6), P=1.3 × 10(-10)). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations.

r2VIM: A new variable selection method for random forests in genome-wide association studies

BACKGROUND

Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses.

RESULTS

We propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS.

CONCLUSIONS

The novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.

Serum bilirubin concentration is modified by UGT1A1 haplotypes and influences risk of type-2 diabetes in the Norfolk Island genetic isolate

Background

Located in the Pacific Ocean between Australia and New Zealand, the unique population isolate of Norfolk Island has been shown to exhibit increased prevalence of metabolic disorders (type-2 diabetes, cardiovascular disease) compared to mainland Australia. We investigated this well-established genetic isolate, utilising its unique genomic structure to increase the ability to detect related genetic markers. A pedigree-based genome-wide association study of 16 routinely collected blood-based clinical traits in 382 Norfolk Island individuals was performed.

Results

A striking association peak was located at chromosome 2q37.1 for both total bilirubin and direct bilirubin, with 29 SNPs reaching statistical significance (P < 1.84 × 10⁻⁷). Strong linkage disequilibrium was observed across a 200 kb region spanning the UDP-glucuronosyltransferase family, including UGT1A1, an enzyme known to metabolise bilirubin. Given the epidemiological literature suggesting negative association between CVD-risk and serum bilirubin we further explored potential associations using stepwise multivariate regression, revealing significant association between direct bilirubin concentration and type-2 diabetes risk. In the Norfolk Island cohort increased direct bilirubin was associated with a 28 % reduction in type-2 diabetes risk (OR: 0.72, 95 % CI: 0.57-0.91, P = 0.005). When adjusted for genotypic effects the overall model was validated, with the adjusted model predicting a 30 % reduction in type-2 diabetes risk with increasing direct bilirubin concentrations (OR: 0.70, 95 % CI: 0.53-0.89, P = 0.0001).

Conclusions

In summary, a pedigree-based GWAS of blood-based clinical traits in the Norfolk Island population has identified variants within the UDPGT family directly associated with serum bilirubin levels, which is in turn implicated with reduced risk of developing type-2 diabetes within this population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0291-z) contains supplementary material, which is available to authorized users.

Big data and the electronic health record

The electronic medical record has evolved from a digital representation of individual patient results and documents to information of large scale and complexity. Big Data refers to new technologies providing management and processing capabilities, targeting massive and disparate data sets. For an individual patient, techniques such as Natural Language Processing allow the integration and analysis of textual reports with structured results. For groups of patients, Big Data offers the promise of large-scale analysis of outcomes, patterns, temporal trends, and correlations. The evolution of Big Data analytics moves us from description and reporting to forecasting, predictive modeling, and decision optimization.

A GWAS Study on Liver Function Test Using eMERGE Network Participants

Introduction

Liver enzyme levels and total serum bilirubin are under genetic control and in recent years genome-wide population-based association studies have identified different susceptibility loci for these traits. We conducted a genome-wide association study in European ancestry participants from the Electronic Medical Records and Genomics (eMERGE) Network dataset of patient medical records with available genotyping data in order to identify genetic contributors to variability in serum bilirubin levels and other liver function tests and to compare the effects between adult and pediatric populations.

Methods

The process of whole genome imputation of eMERGE samples with standard quality control measures have been described previously. After removing missing data and outliers based on principal components (PC) analyses, 3294 samples from European ancestry were used for the GWAS study. The association between each single nucleotide polymorphism (SNP) and total serum bilirubin and other liver function tests was tested using linear regression, adjusting for age, gender, site, platform and ancestry principal components (PC).

Results

Consistent with previous results, a strong association signal has been detected for UGT1A gene cluster (best SNP rs887829, beta = 0.15, p = 1.30x10^-118) for total serum bilirubin level. Indeed, in this region more than 176 SNPs (or indels) had p<10⁻⁸ spanning 150Kb on the long arm of chromosome 2q37.1. In addition, we found a similar level of magnitude in a pediatric group (p = 8.26x10^-47, beta = 0.17). Further imputation using sequencing data as a reference panel revealed association of other markers including known TA7 repeat indels (rs8175347) (p = 9.78x10^-117) and rs111741722 (p = 5.41x10^-119) which were in proxy (r2 = 0.99) with rs887829. Among rare variants, two Asian subjects homozygous for coding SNP rs4148323 (G71R) were identified. Additional known effects for total serum bilirubin were also confirmed including organic anion transporters SLCO1B1-SLCO1B3, TDRP and ZMYND8 at FDR<0.05 with no gene-gene interaction effects. Phenome-wide association studies (PheWAS) suggest a protective effect of TA7 repeat against cerebrovascular disease in an adult cohort (OR = 0.75, p = 0.0008). Among other liver function tests, we also confirmed the previous effect of the ABO blood group locus for variation in serum alkaline phosphatase (rs579459, p = 9.44x10^-15).

Conclusions

Taken together, our data present interesting findings with strong confirmation of previous effects by simply using the eMERGE electronic health record phenotyping. In addition, our findings indicate that similar to the adult population, the UGT1A1 is the main locus responsible for normal variation of serum bilirubin in pediatric populations.

Desiderata for computable representations of electronic health records-driven phenotype algorithms

BACKGROUND

Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).

METHODS

A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.

RESULTS

We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.

CONCLUSION

A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

Association between variants in inflammation and cancer-associated genes and risk and survival of cholangiocarcinoma

Genetic risk factors for cholangiocarcinoma (CCA) remain poorly understood. We assessed the effect of single-nucleotide polymorphisms (SNPs) of genes modulating inflammation or carcinogenesis on CCA risk and survival. We conducted a case-control, candidate gene association study of 370 CCA patients and 740 age-, sex-, and residential area-matched healthy controls. Eighteen functional or putatively functional SNPs in nine genes were genotyped. The log-additive genotype effects of SNPs on CCA risk and survival were determined using logistic regression and the log-rank test, respectively. Initial analysis identified significant associations between SNP rs2143417 and rs689466 in cyclooxygenase 2 (COX-2) and CCA risk, after adjusting for multiple comparisons (cutoff of P = 0.0028). However, these findings were not replicated in another independent cohort of 212 CCA cases and 424 matched controls. No significant association was found between any SNP and survival of CCA patients. Although COX-2 has been shown to contribute to cholangiocarcinogenesis, the COX-2 SNPs tested were not associated with risk of CCA. This study shows a lack of association between variants of genes related to inflammation and carcinogenesis and CCA risk and survival. Other factors than these genetic variants may play more important roles in CCA risk and survival.

A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network

Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging given the syndromic nature of HF and the need to distinguish HF with preserved or reduced ejection fraction. Using a gold standard cohort of manually abstracted cases, an EMR-driven phenotype algorithm based on structured and unstructured data was developed to identify all the cases. The resulting algorithm was executed in two cohorts from the Electronic Medical Records and Genomics (eMERGE) Network with a positive predictive value of >95 %. The algorithm was expanded to include three hierarchical definitions of HF (i.e., definite, probable, possible) based on the degree of confidence of the classification to capture HF cases in a whole population whereby increasing the algorithm utility for use in e-Epidemiologic research.

SLC transporters as therapeutic targets: emerging opportunities

Solute carrier (SLC) transporters - a family of more than 300 membrane-bound proteins that facilitate the transport of a wide array of substrates across biological membranes - have important roles in physiological processes ranging from the cellular uptake of nutrients to the absorption of drugs and other xenobiotics. Several classes of marketed drugs target well-known SLC transporters, such as neurotransmitter transporters, and human genetic studies have provided powerful insight into the roles of more-recently characterized SLC transporters in both rare and common diseases, indicating a wealth of new therapeutic opportunities. This Review summarizes knowledge on the roles of SLC transporters in human disease, describes strategies to target such transporters, and highlights current and investigational drugs that modulate SLC transporters, as well as promising drug targets.

Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors

BACKGROUND

The prediction of clinical behavior, response to therapy, and outcome of infiltrative glioma is challenging. On the basis of previous studies of tumor biology, we defined five glioma molecular groups with the use of three alterations: mutations in the TERT promoter, mutations in IDH, and codeletion of chromosome arms 1p and 19q (1p/19q codeletion). We tested the hypothesis that within groups based on these features, tumors would have similar clinical variables, acquired somatic alterations, and germline variants.

METHODS

We scored tumors as negative or positive for each of these markers in 1087 gliomas and compared acquired alterations and patient characteristics among the five primary molecular groups. Using 11,590 controls, we assessed associations between these groups and known glioma germline variants.

RESULTS

Among 615 grade II or III gliomas, 29% had all three alterations (i.e., were triple-positive), 5% had TERT and IDH mutations, 45% had only IDH mutations, 7% were triple-negative, and 10% had only TERT mutations; 5% had other combinations. Among 472 grade IV gliomas, less than 1% were triple-positive, 2% had TERT and IDH mutations, 7% had only IDH mutations, 17% were triple-negative, and 74% had only TERT mutations. The mean age at diagnosis was lowest (37 years) among patients who had gliomas with only IDH mutations and was highest (59 years) among patients who had gliomas with only TERT mutations. The molecular groups were independently associated with overall survival among patients with grade II or III gliomas but not among patients with grade IV gliomas. The molecular groups were associated with specific germline variants.

CONCLUSIONS

Gliomas were classified into five principal groups on the basis of three tumor markers. The groups had different ages at onset, overall survival, and associations with germline variants, which implies that they are characterized by distinct mechanisms of pathogenesis. (Funded by the National Institutes of Health and others.).

Exome-Wide Association Study Identifies New Low-Frequency and Rare UGT1A1 Coding Variants and UGT1A6 Coding Variants Influencing Serum Bilirubin in Elderly Subjects: A Strobe Compliant Article

Genome-wide association studies (GWASs) have identified loci contributing to total serum bilirubin level. However, no exome-wide approaches have been performed to address this question. Using exome-wide approach, we assessed the influence of protein-coding variants on unconjugated, conjugated, and total serum bilirubin levels in a well-characterized cohort of 773 ambulatory elderly subjects from Italy. Coding variants were replicated in 227 elderly subjects from the same area. We identified 4 missense rare (minor allele frequency, MAF < 0.5%) and low-frequency (MAF, 0.5%-5%) coding variants located in the first exon of the UGT1A1 gene, which encodes for the substrate-binding domain (rs4148323 [MAF = 0.06%; p.Gly71Arg], rs144398951 [MAF = 0.06%; p.Ile215Val], rs35003977 [MAF = 0.78%; p.Val225Gly], and rs57307513 [MAF = 0.06%; p.Ser250Pro]). These variants were in strong linkage disequilibrium with 3 intronic UGT1A1 variants (rs887829, rs4148325, rs6742078), which were significantly associated with total bilirubin level (P = 2.34 × 10(-34), P = 7.02 × 10(-34), and P = 8.27 × 10(-34)), as well as unconjugated, and conjugated bilirubin levels. We also identified UGT1A6 variants in association with total (rs6759892, p.Ser7Ala, P = 1.98 × 10(-26); rs2070959, p.Thr181Ala, P = 2.87 × 10(-27); and rs1105879, p.Arg184Ser, P = 3.27 × 10(-29)), unconjugated, and conjugated bilirubin levels. All UGT1A1 intronic variants (rs887829, rs6742078, and rs4148325) and UGT1A6 coding variants (rs6759892, rs2070959, and rs1105879) were significantly associated with gallstone-related cholecystectomy risk. The UGT1A6 variant rs2070959 (p.Thr181Ala) was associated with the highest risk of gallstone-related cholecystectomy (OR, 4.58; 95% CI, 1.58-13.28; P = 3.21 × 10(-3)). Using an exome-wide approach we identified coding variants on UGT1A1 and UGT1A6 genes in association with serum bilirubin level and hyperbilirubinemia risk in elderly subjects. UGT1A1 intronic single-nucleotide polymorphisms (SNPs) (rs6742078, rs887829, rs4148324) serve as proxy markers for the low-frequency and rare UGT1A1 variants, thereby providing mechanistic explanation to the relationship between UGT1A1 intronic SNPs and the UGT1A1 enzyme activity. UGT1A1 and UGT1A6 variants might be potentially associated with gallstone-related cholecystectomy risk.

Leveraging an Electronic Health Record-Linked Biorepository to Generate a Metformin Pharmacogenomics Hypothesis

Metformin is a first-line antihyperglycemic agent commonly prescribed in type 2 diabetes mellitus (T2DM), but whose pharmacogenomics are not clearly understood. Further, due to accumulating evidence highlighting the potential for metformin in cancer prevention and treatment efforts it is imperative to understand molecular mechanisms of metformin. In this electronic health record(EHR)-based study we explore the potential association of the flavin-containing monooxygenase(FMO)-5 gene, a biologically plausible biotransformer of metformin, and modifying glycemic response to metformin treatment. Using a cohort of 258 T2DM patients who had new metformin exposure, existing genetic data, and longitudinal electronic health records, we compared genetic variation within FMO5 to change in glycemic response. Gene-level and SNP-level analysis identified marginally significant associations for FMO5 variation, representing an EHR-driven pharmacogenetics hypothesis for a potential novel mechanism for metformin biotransformation. However, functional validation of this EHR-based hypothesis is necessary to ascertain its clinical and biological significance.

Using EHR-Linked Biobank Data to Study Metformin Pharmacogenomics

Metformin is a commonly prescribed diabetes medication whose mechanism of action is poorly understood. In this study we utilized EHR-linked biobank data to elucidate the impact of genomic variation on glycemic response to metformin. Our study found significant gene- and SNP-level associations within the beta-2 subunit of the heterotrimeric adenosine monophosphate-activated protein kinase complex. Using EHR phenotypes where were able to add additional clarity to ongoing metformin pharmacogenomic dialogue.

Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols

Background.  Phenome-Wide Association Studies (PheWAS) identify genetic associations across multiple phenotypes. Clinical trials offer opportunities for PheWAS to identify pharmacogenomic associations. We describe the first PheWAS to use genome-wide genotypic data and to utilize human immunodeficiency virus (HIV) clinical trials data. As proof-of-concept, we focused on baseline laboratory phenotypes from antiretroviral therapy-naive individuals. Methods.  Data from 4 AIDS Clinical Trials Group (ACTG) studies were split into 2 datasets: Dataset I (1181 individuals from protocol A5202) and Dataset II (1366 from protocols A5095, ACTG 384, and A5142). Final analyses involved 2547 individuals and 5 954 294 imputed polymorphisms. We calculated comprehensive associations between these polymorphisms and 27 baseline laboratory phenotypes. Results.  A total of 10 584 (0.17%) polymorphisms had associations with P < .01 in both datasets and with the same direction of association. Twenty polymorphisms replicated associations with identical or related phenotypes reported in the Catalog of Published Genome-Wide Association Studies, including several not previously reported in HIV-positive cohorts. We also identified several possibly novel associations. Conclusions.  These analyses define PheWAS properties and principles with baseline laboratory data from HIV clinical trials. This approach may be useful for evaluating on-treatment HIV clinical trials data for associations with various clinical phenotypes.

The intelligent use and clinical benefits of electronic medical records in multiple sclerosis

Electronic medical records (EMRs) are being quickly adopted in clinics around the world. This advancement can greatly enhance the clinical care of patients with multiple sclerosis (MS) by providing formats that allow easier review of medical documents and more structured avenues to store relevant information. MS clinicians should be involved with implementing and updating EMRs at their institutions to ensure EMR formats that benefit MS clinics. EMRs also provide opportunities for research studies of MS to access detailed, longitudinal data of MS disease course that would otherwise be difficult to collect.

Genome-wide discovery of drug-dependent human liver regulatory elements

Inter-individual variation in gene regulatory elements is hypothesized to play a causative role in adverse drug reactions and reduced drug activity. However, relatively little is known about the location and function of drug-dependent elements. To uncover drug-associated elements in a genome-wide manner, we performed RNA-seq and ChIP-seq using antibodies against the pregnane X receptor (PXR) and three active regulatory marks (p300, H3K4me1, H3K27ac) on primary human hepatocytes treated with rifampin or vehicle control. Rifampin and PXR were chosen since they are part of the CYP3A4 pathway, which is known to account for the metabolism of more than 50% of all prescribed drugs. We selected 227 proximal promoters for genes with rifampin-dependent expression or nearby PXR/p300 occupancy sites and assayed their ability to induce luciferase in rifampin-treated HepG2 cells, finding only 10 (4.4%) that exhibited drug-dependent activity. As this result suggested a role for distal enhancer modules, we searched more broadly to identify 1,297 genomic regions bearing a conditional PXR occupancy as well as all three active regulatory marks. These regions are enriched near genes that function in the metabolism of xenobiotics, specifically members of the cytochrome P450 family. We performed enhancer assays in rifampin-treated HepG2 cells for 42 of these sequences as well as 7 sequences that overlap linkage-disequilibrium blocks defined by lead SNPs from pharmacogenomic GWAS studies, revealing 15/42 and 4/7 to be functional enhancers, respectively. A common African haplotype in one of these enhancers in the GSTA locus was found to exhibit potential rifampin hypersensitivity. Combined, our results further suggest that enhancers are the predominant targets of rifampin-induced PXR activation, provide a genome-wide catalog of PXR targets and serve as a model for the identification of drug-responsive regulatory elements.

Drug response varies between individuals and can be caused by genetic factors. Nucleotide variation in gene regulatory elements can have a significant effect on drug response, but due to the difficulty in identifying these elements, they remain understudied. Here, we used various genomic assays to analyze human liver cells treated with or without the antibiotic rifampin and identified drug-induced regulatory elements genome-wide. The testing of numerous active promoters in human liver cells showed only a few to be induced by rifampin treatment. A similar analysis of enhancers found several of them to be induced by the drug. Nucleotide variants in one of these enhancers were found to alter its activity. Combined, this work identifies numerous novel gene regulatory elements that can be activated due to drug response and thus provides candidate sequences in the human genome where nucleotide variation can lead to differences in drug response. It also provides a universally applicable method to detect these elements for other drugs.

Integrating EMR-linked and in vivo functional genetic data to identify new genotype-phenotype associations

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)<10% collected on 8,546 subjects. For each nsSNP, we identified diagnoses shared by at least 2 minor allele homozygotes and with an association p<0.05. The diagnoses were reviewed by a clinician to ascertain whether they may share a common mechanistic basis. While a number of biologically compelling clinical patterns of association were observed, the frequency of these associations was identical to that observed using genotype-permuted data sets, indicating that the associations were likely due to chance. To refine our analysis associations, we then restricted the analysis to 711 nsSNPs in genes with phenotypes in the On-line Mendelian Inheritance in Man (OMIM) or knock-out mouse phenotype databases. An initial comparison of the EMR diagnoses to the known in vivo functions of the gene identified 25 candidate nsSNPs, 19 of which had significant genotype-phenotype associations when tested using matched controls. Twleve of the 19 nsSNPs associations were confirmed by a detailed record review. Four of 12 nsSNP-phenotype associations were successfully replicated in an independent data set: thrombosis (F5,rs6031), seizures/convulsions (GPR98,rs13157270), macular degeneration (CNGB3,rs3735972), and GI bleeding (HGFAC,rs16844401). These analyses demonstrate the feasibility and challenges of using reverse genetics approaches to identify novel gene-phenotype associations in human subjects using low frequency variants. As increasing amounts of rare variant data are generated from modern genotyping and sequence platforms, model organism data may be an important tool to enable discovery.

Pathogenesis of idiosyncratic drug-induced liver injury and clinical perspectives

Idiosyncratic drug-induced liver injury (DILI) is a rare disease that develops independently of drug dose, route, or duration of administration. Furthermore, idiosyncratic DILI is not a single disease entity but rather a spectrum of rare diseases with varying clinical, histological, and laboratory features. The pathogenesis of DILI is not fully understood. Standardization of the DILI nomenclature and methods to assess causality, along with the information provided by the LiverTox Web site, will harmonize and accelerate research on DILI. Studies of new serum biomarkers such as glutamate dehydrogenase, high mobility group box protein 1, and microRNA-122 could provide information for use in diagnosis and prognosis and provide important insights into the mechanisms of the pathogenesis of DILI. Single nucleotide polymorphisms in the HLA region have been associated with idiosyncratic hepatotoxicity attributed to flucloxacillin, ximelagatran, lapatinib, and amoxicillin-clavulanate. However, genome-wide association studies of pooled cases have not associated any genetic factors with idiosyncratic DILI. Whole genome and whole exome sequencing analyses are under way to study cases of DILI attributed to a single medication. Serum proteomic, transcriptome, and metabolome as well as intestinal microbiome analyses will increase our understanding of the mechanisms of this disorder. Further improvements to in vitro and in vivo test systems should advance our understanding of the causes, risk factors, and mechanisms of idiosyncratic DILI.

Admixture mapping and subsequent fine-mapping suggests a biologically relevant and novel association on chromosome 11 for type 2 diabetes in African Americans

Type 2 diabetes (T2D) is a complex metabolic disease that disproportionately affects African Americans. Genome-wide association studies (GWAS) have identified several loci that contribute to T2D in European Americans, but few studies have been performed in admixed populations. We first performed a GWAS of 1,563 African Americans from the Vanderbilt Genome-Electronic Records Project and Northwestern University NUgene Project as part of the electronic Medical Records and Genomics (eMERGE) network. We successfully replicate an association in TCF7L2, previously identified by GWAS in this African American dataset. We were unable to identify novel associations at p<5.0×10(-8) by GWAS. Using admixture mapping as an alternative method for discovery, we performed a genome-wide admixture scan that suggests multiple candidate genes associated with T2D. One finding, TCIRG1, is a T-cell immune regulator expressed in the pancreas and liver that has not been previously implicated for T2D. We performed subsequent fine-mapping to further assess the association between TCIRG1 and T2D in >5,000 African Americans. We identified 13 independent associations between TCIRG1, CHKA, and ALDH3B1 genes on chromosome 11 and T2D. Our results suggest a novel region on chromosome 11 identified by admixture mapping is associated with T2D in African Americans.

The SLCO (former SLC21) superfamily of transporters

The members of the organic anion transporting polypeptide superfamily (OATPs) are classified within the SLCO solute carrier family. All functionally well characterized members are predicted to have 12 transmembrane domains and are sodium-independent transport systems that mediate the transport of a broad range of endo- as well as xenobiotics. Substrates are mainly amphipathic organic anions with a molecular weight of more than 300Da, but some of the known transported substrates are also neutral or even positively charged. Among the well characterized substrates are numerous drugs including statins, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, antibiotics, antihistaminics, antihypertensives and anticancer drugs. Based on their amino acid sequence identities, the different OATPs cluster into families (in general with more than 40% amino acid sequence identity) and subfamilies (more than 60% amino acid identity). With the sequencing of genomes from different species and the computerized prediction of encoded proteins more than 300 OATPs can be found in the databases, however only a fraction of them have been identified in humans, rodents, and some additional species important for pharmaceutical research like the rhesus monkey (Macaca mulatta), the dog (Canis lupus familiaris) and the pig (Sus scrofa). These OATPs form 6 families (OATP1-OATP6) and 13 subfamilies. In this review we try to summarize what is currently known about OATPs with respect to endogenous substrates, tissue distribution, transport mechanisms, regulation of expression, structure-function relationship and mutations and polymorphisms.

Polymorphic variants of SLCO1B1 in neonatal hyperbilirubinemia in China

Background

To evaluate the association between the genetic polymorphism of the solute carrier organic anion transporter family member 1B1 (SLCO1B1, also known as organic anion transport polypeptide C) and hyperbilirubinemia in Chinese neonates.

Methods

183 infants with hyperbilirubinemia and 192 control subjects from the Fifth People’s Hospital of Shenzhen were recruited. Polymerase chain reaction, restriction fragment length polymorphisms and agarose gel electrophoresis techniques were used to detect genetic variants of SLCO1B1.

Results

The study revealed that SLCO1B1 388 G > A occurred significantly more frequently in neonates with hyperbilirubinemia than in controls (RR = 1.50; 95% CI: 1.13–2.00). There were no significant differences in SLCO1B1 521 T > C between the hyperbilirubinemia and the control group (RR, 1.00; 95% CI, 0.72–1.40). No carriage of the C to A substitution at nucleotide 463 was detected.

Conclusion

The SLCO1B1 388 G > A variant is associated with neonatal hyperbilirubinemia in Chinese neonates.

Association of SNPs in the UGT1A gene cluster with total bilirubin and mortality in the Diabetes Heart Study

OBJECTIVE

A negative relationship between total bilirubin concentration (TBili) and CVD risk has been documented in a series of epidemiological studies. In addition, TBili is thought to be under strong genetic regulation via the UGT1A gene family, suggesting it may be a heritable CVD risk factor. However, few studies directly relate TBili-associated UGT1A variants to CVD severity or outcome. This study replicated the genetic association for TBili in the Diabetes Heart Study (DHS), and examined the relationships of TBili-associated SNPs with measures of subclinical CVD and mortality.

METHODS

This investigation included 1220 self-described European American (EA) individuals from the DHS, a family-based study examining risk for macrovascular complications in type 2 diabetes (T2D). Genetic associations with TBili were examined using the Affymetrix Genome-wide Human SNP Array 5.0 and the Illumina Infinium Human Exome beadchip v1.0. Subsequent analyses assessed the relationships of the top TBili-associated SNPs with measures of vascular calcified plaque and mortality.

RESULTS

A genome-wide association study detected 18 SNPs within the UGT1A gene family associated with TBili at p < 5 × 10(-8). The top hit was rs887829 (p = 8.67 × 10(-20)). There was no compelling evidence of association between the top TBili-associated SNPs and vascular calcified plaque (p = 0.05-0.88). There was, however, evidence of association with all-cause mortality (p = 0.0004-0.06), the top hit being rs2741034.

CONCLUSION

These findings support a potential role for UGT1A genetic variants in risk for mortality in T2D. Further quantification of the extent of CVD risk conferred by UGT1A gene family variants in a high risk cohort with T2D is still required.

Genetic characterization to improve interpretation and clinical management of hepatotoxicity caused by tyrosine kinase inhibitors

Tyrosine kinase inhibitors (TKIs) represent important therapeutic alternatives to, or combinations with, traditional cytotoxic chemotherapy. Despite their selective molecular targeting and demonstrated clinical benefit, TKIs produce a range of serious adverse events, including drug-induced liver injury, that require careful patient management to maintain treatment benefit without harm. Genetic characterization of serious adverse events can identify mechanisms of injury and improve safety risk management. This review presents pharmacogenetic comparisons of two approved TKIs, lapatinib and pazopanib, which reveal different mechanisms of injury and inform the characteristics and risk of serious liver injury in treated patients. The data presented demonstrate the utility of genetic studies to investigate drug-induced liver injury and potentially support its management in patients.

An information extraction framework for cohort identification using electronic health records

Information extraction (IE), a natural language processing (NLP) task that automatically extracts structured or semi-structured information from free text, has become popular in the clinical domain for supporting automated systems at point-of-care and enabling secondary use of electronic health records (EHRs) for clinical and translational research. However, a high performance IE system can be very challenging to construct due to the complexity and dynamic nature of human language. In this paper, we report an IE framework for cohort identification using EHRs that is a knowledge-driven framework developed under the Unstructured Information Management Architecture (UIMA). A system to extract specific information can be developed by subject matter experts through expert knowledge engineering of the externalized knowledge resources used in the framework.

Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank

Background

The ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. However, historically GWAS have been limited by inadequate sample size due to associated costs for genotyping and phenotyping of study subjects. This has prompted several academic medical centers to form “biobanks” where biospecimens linked to personal health information, typically in electronic health records (EHRs), are collected and stored on a large number of subjects. This provides tremendous opportunities to discover novel genotype-phenotype associations and foster hypotheses generation.

Results

In this work, we study how emerging Semantic Web technologies can be applied in conjunction with clinical and genotype data stored at the Mayo Clinic Biobank to mine the phenotype data for genetic associations. In particular, we demonstrate the role of using Resource Description Framework (RDF) for representing EHR diagnoses and procedure data, and enable federated querying via standardized Web protocols to identify subjects genotyped for Type 2 Diabetes and Hypothyroidism to discover gene-disease associations. Our study highlights the potential of Web-scale data federation techniques to execute complex queries.

Conclusions

This study demonstrates how Semantic Web technologies can be applied in conjunction with clinical data stored in EHRs to accurately identify subjects with specific diseases and phenotypes, and identify genotype-phenotype associations.

Mining the human phenome using semantic web technologies: a case study for Type 2 Diabetes

The ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. However, historically GWAS have been limited by inadequate sample size due to associated costs for genotyping and phenotyping of study subjects. This has prompted several academic medical centers to form "biobanks" where biospecimens linked to personal health information, typically in electronic health records (EHRs), are collected and stored on large number of subjects. This provides tremendous opportunities to discover novel genotype-phenotype associations and foster hypothesis generation. In this work, we study how emerging Semantic Web technologies can be applied in conjunction with clinical and genotype data stored at the Mayo Clinic Biobank to mine the phenotype data for genetic associations. In particular, we demonstrate the role of using Resource Description Framework (RDF) for representing EHR diagnoses and procedure data, and enable federated querying via standardized Web protocols to identify subjects genotyped with Type 2 Diabetes for discovering gene-disease associations. Our study highlights the potential of Web-scale data federation techniques to execute complex queries.

Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Genome-wide association studies on metabolomics data have demonstrated that genetic variation in metabolic enzymes and transporters leads to concentration changes in the respective metabolite levels. The conventional goal of these studies is the detection of novel interactions between the genome and the metabolic system, providing valuable insights for both basic research as well as clinical applications. In this study, we borrow the metabolomics GWAS concept for a novel, entirely different purpose. Metabolite measurements frequently produce signals where a certain substance can be reliably detected in the sample, but it has not yet been elucidated which specific metabolite this signal actually represents. The concept is comparable to a fingerprint: each one is uniquely identifiable, but as long as it is not registered in a database one cannot tell to whom this fingerprint belongs. Obviously, this issue tremendously reduces the usability of a metabolomics analyses. The genetic associations of such an “unknown,” however, give us concrete evidence of the metabolic pathway this substance is most probably involved in. Moreover, we complement the approach with a specific measure of correlation between metabolites, providing further evidence of the metabolic processes of the unknown. For a number of cases, this even allows for a concrete identity prediction, which we then experimentally validate in the lab.