Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk.

BACKGROUND: ECG QRS duration, a measure of cardiac intraventricular conduction, varies ≈2-fold in individuals without cardiac disease. Slow conduction may promote re-entrant arrhythmias. METHODS AND RESULTS: We performed a genome-wide association study to identify genomic markers of QRS duration in 5272 individuals without cardiac disease selected from electronic medical record algorithms at 5 sites in the Electronic Medical Records and Genomics (eMERGE) network. The most significant loci were evaluated within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium QRS genome-wide association study meta-analysis. Twenty-three single-nucleotide polymorphisms in 5 loci, previously described by CHARGE, were replicated in the eMERGE samples; 18 single-nucleotide polymorphisms were in the chromosome 3 SCN5A and SCN10A loci, where the most significant single-nucleotide polymorphisms were rs1805126 in SCN5A with P=1.2×10(-8) (eMERGE) and P=2.5×10(-20) (CHARGE) and rs6795970 in SCN10A with P=6×10(-6) (eMERGE) and P=5×10(-27) (CHARGE). The other loci were in NFIA, near CDKN1A, and near C6orf204. We then performed phenome-wide association studies on variants in these 5 loci in 13859 European Americans to search for diagnoses associated with these markers. Phenome-wide association study identified atrial fibrillation and cardiac arrhythmias as the most common associated diagnoses with SCN10A and SCN5A variants. SCN10A variants were also associated with subsequent development of atrial fibrillation and arrhythmia in the original 5272 "heart-healthy" study population. CONCLUSIONS: We conclude that DNA biobanks coupled to electronic medical records not only provide a platform for genome-wide association study but also may allow broad interrogation of the longitudinal incidence of disease associated with genetic variants. The phenome-wide association study approach implicated sodium channel variants modulating QRS duration in subjects without cardiac disease as predictors of subsequent arrhythmias.

Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies.

We repurposed existing genotypes in DNA biobanks across the Electronic Medical Records and Genomics network to perform a genome-wide association study for primary hypothyroidism, the most common thyroid disease. Electronic selection algorithms incorporating billing codes, laboratory values, text queries, and medication records identified 1317 cases and 5053 controls of European ancestry within five electronic medical records (EMRs); the algorithms' positive predictive values were 92.4% and 98.5% for cases and controls, respectively. Four single-nucleotide polymorphisms (SNPs) in linkage disequilibrium at 9q22 near FOXE1 were associated with hypothyroidism at genome-wide significance, the strongest being rs7850258 (odds ratio [OR] 0.74, p = 3.96 × 10(-9)). This association was replicated in a set of 263 cases and 1616 controls (OR = 0.60, p = 5.7 × 10(-6)). A phenome-wide association study (PheWAS) that was performed on this locus with 13,617 individuals and more than 200,000 patient-years of billing data identified associations with additional phenotypes: thyroiditis (OR = 0.58, p = 1.4 × 10(-5)), nodular (OR = 0.76, p = 3.1 × 10(-5)) and multinodular (OR = 0.69, p = 3.9 × 10(-5)) goiters, and thyrotoxicosis (OR = 0.76, p = 1.5 × 10(-3)), but not Graves disease (OR = 1.03, p = 0.82). Thyroid cancer, previously associated with this locus, was not significantly associated in the PheWAS (OR = 1.29, p = 0.09). The strongest association in the PheWAS was hypothyroidism (OR = 0.76, p = 2.7 × 10(-13)), which had an odds ratio that was nearly identical to that of the curated case-control population in the primary analysis, providing further validation of the PheWAS method. Our findings indicate that EMR-linked genomic data could allow discovery of genes associated with many diseases without additional genotyping cost.

Interrogating the complex role of chromosome 16p13.13 in multiple sclerosis susceptibility: independent genetic signals in the CIITA-CLEC16A-SOCS1 gene complex.

Multiple sclerosis (MS) is a neurodegenerative, autoimmune disease of the central nervous system, and numerous studies have shown that MS has a strong genetic component. Independent studies to identify MS-associated genes have often indicated multiple signals in physically close genomic regions, although by their proximity it is not always clear if these data indicate redundant or truly independent genetic signals. Recently, three MS study samples were genotyped in parallel using an Illumina Custom BeadChip. These revealed multiple significantly associated single-nucleotide polymorphisms within a 600 kb stretch on chromosome 16p13. Here we present a detailed analysis of variants in this region that clarifies the independent nature of these signals. The linkage disequilibrium patterns in the region and logistic regression analysis of the associations suggest that this region likely harbors three independent MS disease loci. Further, we examined cis-expression QTLs, histone modifications and CCCTC-binding factor (CTCF) binding data in the region. We also tested for correlated expression of the genes from the region using whole-genome expression array data from lymphoblastoid cell lines. Three of the genes show expression correlations across loci. Furthermore, in the GM12878 lymphoblastoid cell line, these three genes are in a continuous region devoid of H3K27 methylation, suggesting an open chromatin configuration. This region likely only contributes minimal risk to MS; however, investigation of this region will undoubtedly provide insight into the functional mechanisms of these genes. These data highlight the importance of taking a closer look at the expression and function of chromosome 16p13 in the pathogenesis of MS.

Genetic variants associated with angiotensin-converting enzyme inhibitor-associated angioedema.

OBJECTIVE: The objective of this study was to identify genetic variants associated with angiotensin-converting enzyme (ACE) inhibitor-associated angioedema. PARTICIPANTS AND METHODS: We carried out a genome-wide association study in 175 individuals with ACE inhibitor-associated angioedema and 489 ACE inhibitor-exposed controls from Nashville (Tennessee) and Marshfield (Wisconsin). We tested for replication in 19 cases and 57 controls who participated in Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET). RESULTS: There were no genome-wide significant associations of any single-nucleotide polymorphism (SNP) with angioedema. Sixteen SNPs in African Americans and 41 SNPs in European Americans were associated moderately with angioedema (P<10) and evaluated for association in ONTARGET. The T allele of rs500766 in PRKCQ was associated with a reduced risk, whereas the G allele of rs2724635 in ETV6 was associated with an increased risk of ACE inhibitor-associated angioedema in the Nashville/Marshfield sample and ONTARGET. In a candidate gene analysis, rs989692 in the gene encoding neprilysin (MME), an enzyme that degrades bradykinin and substance P, was significantly associated with angioedema in ONTARGET and Nashville/Marshfield African Americans. CONCLUSION: Unlike other serious adverse drug effects, ACE inhibitor-associated angioedema is not associated with a variant with a large effect size. Variants in MME and genes involved in immune regulation may be associated with ACE inhibitor-associated angioedema.

Mapping the incidentalome: estimating incidental findings generated through clinical pharmacogenomics testing.

PURPOSE: Greater clinical validity and economic feasibility are driving the more widespread use of multiplex genetic technologies in routine clinical care, especially for applications in pharmacogenomics. Empirical data on the numbers and types of incidental findings generated through such testing are needed to develop policies and practices related to their clinical use. Of particular importance are disparities in findings relevant to different ancestry groups. METHODS: The Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment Resource, or PREDICT, is an institutional program to implement prospective clinical genotyping of 34 pharmacogenomic-related genes to guide drug selection and dosing. We curated 5,566 journal articles to quantify and characterize the incidental, non-pharmacogenomic genotype-phenotype associations that could be generated through this clinical genotyping project. RESULTS: We identified 372 putative incidental genotype-phenotype associations that might be revealed in patients undergoing clinical genotyping for pharmacogenomic purposes. Of these, 287 associations were supported by at least one study demonstrating an odds ratio ≥2.0 or ≤0.5. Numbers of potentially relevant findings varied widely by ancestry group. CONCLUSION: Rigorous clinical policies for the clinical management of incidental findings are needed because the sheer number of significant findings could prove overwhelming to health-care institutions, providers, and patients.

Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality.

Genome-wide association studies (GWAS) are a useful approach in the study of the genetic components of complex phenotypes. Aside from large cohorts, GWAS have generally been limited to the study of one or a few diseases or traits. The emergence of biobanks linked to electronic medical records (EMRs) allows the efficient reuse of genetic data to yield meaningful genotype-phenotype associations for multiple phenotypes or traits. Phase I of the electronic MEdical Records and GEnomics (eMERGE-I) Network is a National Human Genome Research Institute-supported consortium composed of five sites to perform various genetic association studies using DNA repositories and EMR systems. Each eMERGE site has developed EMR-based algorithms to comprise a core set of 14 phenotypes for extraction of study samples from each site's DNA repository. Each eMERGE site selected samples for a specific phenotype, and these samples were genotyped at either the Broad Institute or at the Center for Inherited Disease Research using the Illumina Infinium BeadChip technology. In all, approximately 17,000 samples from across the five sites were genotyped. A unified quality control (QC) pipeline was developed by the eMERGE Genomics Working Group and used to ensure thorough cleaning of the data. This process includes examination of sample and marker quality and various batch effects. Upon completion of the genotyping and QC analyses for each site's primary study, eMERGE Coordinating Center merged the datasets from all five sites. This larger merged dataset reentered the established eMERGE QC pipeline. Based on lessons learned during the process, additional analyses and QC checkpoints were added to the pipeline to ensure proper merging. Here, we explore the challenges associated with combining datasets from different genotyping centers and describe the expansion to eMERGE QC pipeline for merged datasets. These additional steps will be useful as the eMERGE project expands to include additional sites in eMERGE-II, and also serve as a starting point for investigators merging multiple genotype datasets accessible through the National Center for Biotechnology Information in the database of Genotypes and Phenotypes. Our experience demonstrates that merging multiple datasets after additional QC can be an efficient use of genotype data despite new challenges that appear in the process.

CIITA variation in the presence of HLA-DRB1*1501 increases risk for multiple sclerosis.

The MHC class II transactivator gene (CIITA) is an important transcription factor regulating gene required for HLA class II MHC-restricted antigen presentation. Association with HLA class II variation, particularly HLA-DRB1*1501, has been well-established for multiple sclerosis (MS). In addition, the -168A/G CIITA promoter variant (rs3087456) has been reported to be associated with MS. Thus, a multi-stage investigation of variation within CIITA, DRB1*1501 and MS was undertaken in 6108 individuals. In stage 1, 24 SNPs within CIITA were genotyped in 1320 cases and 1363 controls (n = 2683). Rs4774 (missense +1614G/C; G500A) was associated with MS (P = 4.9 x 10(-3)), particularly in DRB1*1501 +individuals (P = 1 x 10(-4)). No association was observed for the -168A/G promoter variant. In stage 2, rs4774 was genotyped in 973 extended families; rs4774*C was also associated with increased risk for MS in DRB1*1501+ families (P = 2.3 x 10(-2)). In a third analysis, rs4774 was tested in cases and controls (stage 1) combined with one case per family (stage 2) for increased power. Rs4774*C was associated with MS (P = 1 x 10(-3)), particularly in DRB1*1501+ cases and controls (P = 1 x 10(-4)). Results obtained from logistic regression analysis showed evidence for interaction between rs4774*C and DRB1*1501 associated with risk for MS (ratio of ORs = 1.72, 95% CI 1.28-2.32, P = 3 x 10(-4)). Furthermore, rs4774*C was associated with DRB1*1501+ MS when conditioned on the presence (OR = 1.67, 95% CI = 1.19-2.37, P = 1.9 x 10(-3)) and absence (OR = 1.49, 95% CI = 1.15-1.95, P = 2.3 x 10(-3)) of CLEC16A rs6498169*G, a putative MS risk allele adjacent to CIITA. Our results provide strong evidence supporting a role for CIITA variation in MS risk, which appears to depend on the presence of DRB1*1501.

Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12,566 individuals.

The primary genetic risk factor in multiple sclerosis (MS) is the HLA-DRB1*1501 allele; however, much of the remaining genetic contribution to MS has yet to be elucidated. Several lines of evidence support a role for neuroendocrine system involvement in autoimmunity which may, in part, be genetically determined. Here, we comprehensively investigated variation within eight candidate hypothalamic-pituitary-adrenal (HPA) axis genes and susceptibility to MS. A total of 326 SNPs were investigated in a discovery dataset of 1343 MS cases and 1379 healthy controls of European ancestry using a multi-analytical strategy. Random Forests, a supervised machine-learning algorithm, identified eight intronic SNPs within the corticotrophin-releasing hormone receptor 1 or CRHR1 locus on 17q21.31 as important predictors of MS. On the basis of univariate analyses, six CRHR1 variants were associated with decreased risk for disease following a conservative correction for multiple tests. Independent replication was observed for CRHR1 in a large meta-analysis comprising 2624 MS cases and 7220 healthy controls of European ancestry. Results from a combined meta-analysis of all 3967 MS cases and 8599 controls provide strong evidence for the involvement of CRHR1 in MS. The strongest association was observed for rs242936 (OR = 0.82, 95% CI = 0.74-0.90, P = 9.7 × 10(-5)). Replicated CRHR1 variants appear to exist on a single associated haplotype. Further investigation of mechanisms involved in HPA axis regulation and response to stress in MS pathogenesis is warranted.

Variation within DNA repair pathway genes and risk of multiple sclerosis.

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with a prominent genetic component. The primary genetic risk factor is the human leukocyte antigen (HLA)-DRB1*1501 allele; however, much of the remaining genetic contribution to MS has not been elucidated. The authors investigated the relation between variation in DNA repair pathway genes and risk of MS. Single-locus association testing, epistatic tests of interactions, logistic regression modeling, and nonparametric Random Forests analyses were performed by using genotypes from 1,343 MS cases and 1,379 healthy controls of European ancestry. A total of 485 single nucleotide polymorphisms within 72 genes related to DNA repair pathways were investigated, including base excision repair, nucleotide excision repair, and double-strand breaks repair. A single nucleotide polymorphism variant within the general transcription factor IIH, polypeptide 4 gene, GTF2H4, on chromosome 6p21.33 was significantly associated with MS (odds ratio = 0.7, P = 3.5 x 10(-5)) after accounting for multiple testing and was not due to linkage disequilibrium with HLA-DRB1*1501. Although other candidate genes examined here warrant further follow-up studies, collectively, these results derived from a well-powered study do not support a strong role for common variation within DNA repair pathway genes in MS.

Genetic variation in the IL7RA/IL7 pathway increases multiple sclerosis susceptibility.

Multiple sclerosis (MS) is characterized as an autoimmune demyelinating disease. Numerous family studies have confirmed a strong genetic component underlying its etiology. After several decades of frustrating research, the advent and application of affordable genotyping of dense SNP maps in large data sets has ushered in a new era in which rapid progress is being made in our understanding of the genetics underlying many complex traits. For MS, one of the first discoveries to emerge in this new era was the association with rs6897932[T244I] in the interleukin-7 receptor alpha chain (IL7RA) gene (Gregory et al. in Nat Genet 39(9):1083-1091, 2007; International Multiple Sclerosis Genetics Consortium in N Engl J Med 357(9):851-862, 2007; Lundmark in Nat Genet 39(9):1108-1113, 2007), a discovery that was accompanied by functional data that suggest this variant is likely to be causative rather than a surrogate proxy (Gregory et al. in Nat Genet 39(9):1083-1091, 2007). We hypothesized that variations in other genes functionally related to IL7RA might also influence MS. We investigated this hypothesis by examining genes in the extended biological pathway related to IL7RA to identify novel associations. We identified 73 genes with putative functional relationships to IL7RA and subsequently genotyped 7,865 SNPs in and around these genes using an Illumina Infinium BeadChip assay. Using 2,961 case-control data sets, two of the gene regions examined, IL7 and SOCS1, had significantly associated single-nucleotide polymorphisms (SNPs) that further replicated in an independent case-control data set (4,831 samples) with joint p values as high as 8.29 x 10(-6) and 3.48 x 10(-7), respectively, exceeding the threshold for experiment-wise significance. Our results also implicate two additional novel gene regions that are likely to be associated with MS: PRKCE with p values reaching 3.47 x 10(-4), and BCL2 with p values reaching 4.32 x 10(-4). The TYK2 gene, which also emerged in our analysis, has recently been associated with MS (Ban et al. 2009). These results help to further delineate the genetic architecture of MS and validate our pathway approach as an effective method to identify novel associations in a complex disease.

Enhancing the power of genetic association studies through the use of silver standard cases derived from electronic medical records.

The feasibility of using imperfectly phenotyped "silver standard" samples identified from electronic medical record diagnoses is considered in genetic association studies when these samples might be combined with an existing set of samples phenotyped with a gold standard technique. An analytic expression is derived for the power of a chi-square test of independence using either research-quality case/control samples alone, or augmented with silver standard data. The subset of the parameter space where inclusion of silver standard samples increases statistical power is identified. A case study of dementia subjects identified from electronic medical records from the Electronic Medical Records and Genomics (eMERGE) network, combined with subjects from two studies specifically targeting dementia, verifies these results.

Predicting warfarin dosage in European-Americans and African-Americans using DNA samples linked to an electronic health record.

AIM: Warfarin pharmacogenomic algorithms reduce dosing error, but perform poorly in non-European-Americans. Electronic health record (EHR) systems linked to biobanks may allow for pharmacogenomic analysis, but they have not yet been used for this purpose. PATIENTS & METHODS: We used BioVU, the Vanderbilt EHR-linked DNA repository, to identify European-Americans (n = 1022) and African-Americans (n = 145) on stable warfarin therapy and evaluated the effect of 15 pharmacogenetic variants on stable warfarin dose. RESULTS: Associations between variants in VKORC1, CYP2C9 and CYP4F2 with weekly dose were observed in European-Americans as well as additional variants in CYP2C9 and CALU in African-Americans. Compared with traditional 5 mg/day dosing, implementing the US FDA recommendations or the International Warfarin Pharmacogenomics Consortium (IWPC) algorithm reduced error in weekly dose in European-Americans (13.5-12.4 and 9.5 mg/week, respectively) but less so in African-Americans (15.2-15.0 and 13.8 mg/week, respectively). By further incorporating associated variants specific for European-Americans and African-Americans in an expanded algorithm, dose-prediction error reduced to 9.1 mg/week (95% CI: 8.4-9.6) in European-Americans and 12.4 mg/week (95% CI: 10.0-13.2) in African-Americans. The expanded algorithm explained 41 and 53% of dose variation in African-Americans and European-Americans, respectively, compared with 29 and 50%, respectively, for the IWPC algorithm. Implementing these predictions via dispensable pill regimens similarly reduced dosing error. CONCLUSION: These results validate EHR-linked DNA biorepositories as real-world resources for pharmacogenomic validation and discovery.

High density GWAS for LDL cholesterol in African Americans using electronic medical records reveals a strong protective variant in APOE.

Only one low-density lipoprotein cholesterol (LDL-C) genome-wide association study (GWAS) has been previously reported in -African Americans. We performed a GWAS of LDL-C in African Americans using data extracted from electronic medical records (EMR) in the eMERGE network. African Americans were genotyped on the Illumina 1M chip. All LDL-C measurements, prescriptions, and diagnoses of concomitant disease were extracted from EMR. We created two analytic datasets; one dataset having median LDL-C calculated after the exclusion of some lab values based on comorbidities and medication (n= 618) and another dataset having median LDL-C calculated without any exclusions (n= 1,249). SNP rs7412 in APOE was strongly associated with LDL-C in both datasets (p < 5 × 10(-8) ). In the dataset with exclusions, a decrease of 20.0 mg/dL per minor allele was observed. The effect size was attenuated (12.3 mg/dL) in the dataset without any lab values excluded. Although other signals in APOE have been detected in previous GWAS, this large and important SNP association has not been well detected in large GWAS because rs7412 was not included on many genotyping arrays. Use of median LDL-C extracted from EMR after exclusions for medications and comorbidities increased the percentage of trait variance explained by genetic variation.

Quality control procedures for genome-wide association studies.

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of complex disease. Regardless of context, the practical utility of this information will ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the electronic MEdical Records and Genomics (eMERGE) network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of GWAS data, including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We propose best practices and discuss areas of ongoing and future research.