Laura J. Rasmussen-Torvik

Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study

OBJECTIVE Genome-wide association studies (GWAS) require high specificity and large numbers of subjects to identify genotype-phenotype correlations accurately. The aim of this study was to identify type 2 diabetes (T2D) cases and controls for a GWAS, using data captured through routine clinical care across five institutions using different electronic medical record (EMR) systems. MATERIALS AND METHODS An algorithm was developed to identify T2D cases and controls based on a combination of diagnoses, medications, and laboratory results. The performance of the algorithm was validated at three of the five participating institutions compared against clinician review. A GWAS was subsequently performed using cases and controls identified by the algorithm, with samples pooled across all five institutions. RESULTS The algorithm achieved 98% and 100% positive predictive values for the identification of diabetic cases and controls, respectively, as compared against clinician review. By standardizing and applying the algorithm across institutions, 3353 cases and 3352 controls were identified. Subsequent GWAS using data from five institutions replicated the TCF7L2 gene variant (rs7903146) previously associated with T2D. DISCUSSION By applying stringent criteria to EMR data collected through routine clinical care, cases and controls for a GWAS were identified that subsequently replicated a known genetic variant. The use of standard terminologies to define data elements enabled pooling of subjects and data across five different institutions to achieve the robust numbers required for GWAS. CONCLUSIONS An algorithm using commonly available data from five different EMR can accurately identify T2D cases and controls for genetic study across multiple institutions.

Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems.

We describe here the design and initial implementation of the eMERGE‐PGx project. eMERGE‐PGx, a partnership of the Electronic Medical Records and Genomics Network and the Pharmacogenomics Research Network, has three objectives: (i) to deploy PGRNseq, a next‐generation sequencing platform assessing sequence variation in 84 proposed pharmacogenes, in nearly 9,000 patients likely to be prescribed drugs of interest in a 1‐ to 3‐year time frame across several clinical sites; (ii) to integrate well‐established clinically validated pharmacogenetic genotypes into the electronic health record with associated clinical decision support and to assess process and clinical outcomes of implementation; and (iii) to develop a repository of pharmacogenetic variants of unknown significance linked to a repository of electronic health record–based clinical phenotype data for ongoing pharmacogenomics discovery. We describe site‐specific project implementation and anticipated products, including genetic variant and phenotype data repositories, novel variant association studies, clinical decision support modules, clinical and process outcomes, approaches to managing incidental findings, and patient and clinician education methods.

Ideal Cardiovascular Health is Inversely Associated with Incident Cancer: The Atherosclerosis Risk in Communities Study

Background— The American Heart Association (AHA) has defined the concept of ideal cardiovascular health in promotion of the 2020 Strategic Impact Goals. We examined whether adherence to ideal levels of the 7 AHA cardiovascular health metrics was associated with incident cancers in the Atherosclerosis Risk In Communities (ARIC) study over 17 to 19 years of follow-up. Methods and Results— After exclusions for missing data and prevalent cancer, 13 253 ARIC participants were included for analysis. Baseline measurements were used to classify participants according to 7 AHA cardiovascular health metrics. Combined cancer incidence (excluding nonmelanoma skin cancers) from 1987 to 2006 was captured using cancer registries and hospital surveillance; 2880 incident cancer cases occurred over follow-up. Cox regression was used to calculate hazard ratios for incident cancer. There was a significant ( P trend <0.0001), graded, inverse association between the number of ideal cardiovascular health metrics at baseline and cancer incidence. Participants meeting goals for 6 to 7 ideal health metrics (2.7% of the population) had 51% lower risk of incident cancer than those meeting goals for 0 ideal health metrics. When smoking was removed from the sum of ideal health metrics, the association was attenuated with participants meeting goals for 5 to 6 health metrics having 25% lower cancer risk than those meeting goals for 0 ideal health metrics ( P trend =0.03). Conclusions— Adherence to the 7 ideal health metrics defined in the AHA 2020 goals is associated with lower cancer incidence. The AHA should continue to pursue partnerships with cancer advocacy groups to achieve reductions in chronic disease prevalence. # Clinical Perspective {#article-title-29}Background— The American Heart Association (AHA) has defined the concept of ideal cardiovascular health in promotion of the 2020 Strategic Impact Goals. We examined whether adherence to ideal levels of the 7 AHA cardiovascular health metrics was associated with incident cancers in the Atherosclerosis Risk In Communities (ARIC) study over 17 to 19 years of follow-up. Methods and Results— After exclusions for missing data and prevalent cancer, 13 253 ARIC participants were included for analysis. Baseline measurements were used to classify participants according to 7 AHA cardiovascular health metrics. Combined cancer incidence (excluding nonmelanoma skin cancers) from 1987 to 2006 was captured using cancer registries and hospital surveillance; 2880 incident cancer cases occurred over follow-up. Cox regression was used to calculate hazard ratios for incident cancer. There was a significant (P trend <0.0001), graded, inverse association between the number of ideal cardiovascular health metrics at baseline and cancer incidence. Participants meeting goals for 6 to 7 ideal health metrics (2.7% of the population) had 51% lower risk of incident cancer than those meeting goals for 0 ideal health metrics. When smoking was removed from the sum of ideal health metrics, the association was attenuated with participants meeting goals for 5 to 6 health metrics having 25% lower cancer risk than those meeting goals for 0 ideal health metrics (P trend =0.03). Conclusions— Adherence to the 7 ideal health metrics defined in the AHA 2020 goals is associated with lower cancer incidence. The AHA should continue to pursue partnerships with cancer advocacy groups to achieve reductions in chronic disease prevalence.

Genome-Wide Association of Body Fat Distribution in African Ancestry Populations Suggests New Loci

Central obesity, measured by waist circumference (WC) or waist-hip ratio (WHR), is a marker of body fat distribution. Although obesity disproportionately affects minority populations, few studies have conducted genome-wide association study (GWAS) of fat distribution among those of predominantly African ancestry (AA). We performed GWAS of WC and WHR, adjusted and unadjusted for BMI, in up to 33,591 and 27,350 AA individuals, respectively. We identified loci associated with fat distribution in AA individuals using meta-analyses of GWA results for WC and WHR (stage 1). Overall, 25 SNPs with single genomic control (GC)-corrected p-values<5.0×10−6 were followed-up (stage 2) in AA with WC and with WHR. Additionally, we interrogated genomic regions of previously identified European ancestry (EA) WHR loci among AA. In joint analysis of association results including both Stage 1 and 2 cohorts, 2 SNPs demonstrated association, rs2075064 at LHX2, p = 2.24×10−8 for WC-adjusted-for-BMI, and rs6931262 at RREB1, p = 2.48×10−8 for WHR-adjusted-for-BMI. However, neither signal was genome-wide significant after double GC-correction (LHX2: p = 6.5×10−8; RREB1: p = 5.7×10−8). Six of fourteen previously reported loci for waist in EA populations were significant (p<0.05 divided by the number of independent SNPs within the region) in AA studied here (TBX15-WARS2, GRB14, ADAMTS9, LY86, RSPO3, ITPR2-SSPN). Further, we observed associations with metabolic traits: rs13389219 at GRB14 associated with HDL-cholesterol, triglycerides, and fasting insulin, and rs13060013 at ADAMTS9 with HDL-cholesterol and fasting insulin. Finally, we observed nominal evidence for sexual dimorphism, with stronger results in AA women at the GRB14 locus (p for interaction = 0.02). In conclusion, we identified two suggestive loci associated with fat distribution in AA populations in addition to confirming 6 loci previously identified in populations of EA. These findings reinforce the concept that there are fat distribution loci that are independent of generalized adiposity.

Meta-analysis of Gene-Environment interaction: joint estimation of SNP and SNP×Environment regression coefficients

Introduction: Genetic discoveries are validated through the meta‐analysis of genome‐wide association scans in large international consortia. Because environmental variables may interact with genetic factors, investigation of differing genetic effects for distinct levels of an environmental exposure in these large consortia may yield additional susceptibility loci undetected by main effects analysis. We describe a method of joint meta‐analysis (JMA) of SNP and SNP by Environment (SNP × E) regression coefficients for use in gene‐environment interaction studies. Methods: In testing SNP × E interactions, one approach uses a two degree of freedom test to identify genetic variants that influence the trait of interest. This approach detects both main and interaction effects between the trait and the SNP. We propose a method to jointly meta‐analyze the SNP and SNP × E coefficients using multivariate generalized least squares. This approach provides confidence intervals of the two estimates, a joint significance test for SNP and SNP × E terms, and a test of homogeneity across samples. Results: We present a simulation study comparing this method to four other methods of meta‐analysis and demonstrate that the JMA performs better than the others when both main and interaction effects are present. Additionally, we implemented our methods in a meta‐analysis of the association between SNPs from the type 2 diabetes‐associated gene PPARG and log‐transformed fasting insulin levels and interaction by body mass index in a combined sample of 19,466 individuals from five cohorts. Genet. Epidemiol. 35:11–18, 2011.

Polygenic type 2 diabetes prediction at the limit of common variant detection.

Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)–associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants.

Association of Arrhythmia-Related Genetic Variants With Phenotypes Documented in Electronic Medical Records.

IMPORTANCE Large-scale DNA sequencing identifies incidental rare variants in established Mendelian disease genes, but the frequency of related clinical phenotypes in unselected patient populations is not well established. Phenotype data from electronic medical records (EMRs) may provide a resource to assess the clinical relevance of rare variants. OBJECTIVE To determine the clinical phenotypes from EMRs for individuals with variants designated as pathogenic by expert review in arrhythmia susceptibility genes. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study included 2022 individuals recruited for nonantiarrhythmic drug exposure phenotypes from October 5, 2012, to September 30, 2013, for the Electronic Medical Records and Genomics Network Pharmacogenomics project from 7 US academic medical centers. Variants in SCN5A and KCNH2, disease genes for long QT and Brugada syndromes, were assessed for potential pathogenicity by 3 laboratories with ion channel expertise and by comparison with the ClinVar database. Relevant phenotypes were determined from EMRs, with data available from 2002 (or earlier for some sites) through September 10, 2014. EXPOSURES One or more variants designated as pathogenic in SCN5A or KCNH2. MAIN OUTCOMES AND MEASURES Arrhythmia or electrocardiographic (ECG) phenotypes defined by International Classification of Diseases, Ninth Revision (ICD-9) codes, ECG data, and manual EMR review. RESULTS Among 2022 study participants (median age, 61 years [interquartile range, 56-65 years]; 1118 [55%] female; 1491 [74%] white), a total of 122 rare (minor allele frequency <0.5%) nonsynonymous and splice-site variants in 2 arrhythmia susceptibility genes were identified in 223 individuals (11% of the study cohort). Forty-two variants in 63 participants were designated potentially pathogenic by at least 1 laboratory or ClinVar, with low concordance across laboratories (Cohen κ = 0.26). An ICD-9 code for arrhythmia was found in 11 of 63 (17%) variant carriers vs 264 of 1959 (13%) of those without variants (difference, +4%; 95% CI, -5% to +13%; P = .35). In the 1270 (63%) with ECGs, corrected QT intervals were not different in variant carriers vs those without (median, 429 vs 439 milliseconds; difference, -10 milliseconds; 95% CI, -16 to +3 milliseconds; P = .17). After manual review, 22 of 63 participants (35%) with designated variants had any ECG or arrhythmia phenotype, and only 2 had corrected QT interval longer than 500 milliseconds. CONCLUSIONS AND RELEVANCE Among laboratories experienced in genetic testing for cardiac arrhythmia disorders, there was low concordance in designating SCN5A and KCNH2 variants as pathogenic. In an unselected population, the putatively pathogenic genetic variants were not associated with an abnormal phenotype. These findings raise questions about the implications of notifying patients of incidental genetic findings.

Pleiotropic genes for metabolic syndrome and inflammation

Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation.

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 sites 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 sites 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. Genet. Epidemiol. 35:887–898, 2011.

Transferability and Fine Mapping of Type 2 Diabetes Loci in African Americans The Candidate Gene Association Resource Plus Study

Type 2 diabetes (T2D) disproportionally affects African Americans (AfA) but, to date, genetic variants identified from genome-wide association studies (GWAS) are primarily from European and Asian populations. We examined the single nucleotide polymorphism (SNP) and locus transferability of 40 reported T2D loci in six AfA GWAS consisting of 2,806 T2D case subjects with or without end-stage renal disease and 4,265 control subjects from the Candidate Gene Association Resource Plus Study. Our results revealed that seven index SNPs at the TCF7L2, KLF14, KCNQ1, ADCY5, CDKAL1, JAZF1, and GCKR loci were significantly associated with T2D (P < 0.05). The strongest association was observed at TCF7L2 rs7903146 (odds ratio [OR] 1.30; P = 6.86 × 10−8). Locus-wide analysis demonstrated significant associations (Pemp < 0.05) at regional best SNPs in the TCF7L2, KLF14, and HMGA2 loci as well as suggestive signals in KCNQ1 after correction for the effective number of SNPs at each locus. Of these loci, the regional best SNPs were in differential linkage disequilibrium (LD) with the index and adjacent SNPs. Our findings suggest that some loci discovered in prior reports affect T2D susceptibility in AfA with similar effect sizes. The reduced and differential LD pattern in AfA compared with European and Asian populations may facilitate fine mapping of causal variants at loci shared across populations.