Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with P < 4.6 × 10−6 (false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (n = 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.

Clonal mosaicism for large chromosomal anomalies (duplications, deletions and uniparental disomy) was detected using SNP microarray data from over 50,000 subjects recruited for genome-wide association studies. This detection method requires a relatively high frequency of cells (>5–10%) with the same abnormal karyotype (presumably of clonal origin) in the presence of normal cells. The frequency of detectable clonal mosaicism in peripheral blood is low (<0.5%) from birth until 50 years of age, after which it rises rapidly to 2–3% in the elderly. Many of the mosaic anomalies are characteristic of those found in hematological cancers and identify common deleted regions that pinpoint the locations of genes previously associated with hematological cancers. Although only 3% of subjects with detectable clonal mosaicism had any record of hematological cancer prior to DNA sampling, those without a prior diagnosis have an estimated 10-fold higher risk of a subsequent hematological cancer (95% confidence interval = 6–18).

The Electronic Medical Records and Genomics (eMERGE) Network is a National Human Genome Research Institute (NHGRI)-funded consortium engaged in the development of methods and best-practices for utilizing the Electronic Medical Record (EMR) as a tool for genomic research. Now in its sixth year, its second funding cycle and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from EMRs can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and healthcare informatics, particularly electronic phenotyping, genome-wide association studies, genomic medicine implementation and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here we describe the evolution, accomplishments, opportunities and challenges of the network since its inception as a five-group consortium focused on genotype-phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting towards implementation of genomic medicine.

Background-Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events. Methods and Results-We performed mass-spectrometry-based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls ("initial"), and 140 CAD cases and 140 controls ("replication"). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined ("event" group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled ("eventreplication" group; nϭ63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis-derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial Pϭ0.002, replication Pϭ0.01), and 1 comprising urea cycle metabolites (factor 9, initial Pϭ0.0004, replication Pϭ0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; Pϭ0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; Pϭ0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; Pϭ0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; Pϭ0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; Pϭ0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; Pϭ0.01). Conclusions-Metabolite profiles are associated with CAD and subsequent cardiovascular events.(Circ Cardiovasc Genet. 2010;3:207-214.)Key Words: metabolism Ⅲ risk factors Ⅲ coronary artery disease C oronary artery disease (CAD) is the leading cause of death in industrialized countries. Many accepted risk factors for CAD are metabolic. However, there remains an incomplete mechanistic understanding of CAD risk and equally important, a need to refine our ability to identify individuals at highest risk of cardiovascular events. Given the complex nature of CAD, evaluation with more comprehensive tools may improve risk stratification and enhance our understanding of the disease process. Metabolomics, the study of small-molecule metabolites, may be particularly useful for the diagnosis of human disease. Studies have demonstrated heritability of metabolites in mice, 1 and we have shown that metabolite profiles are heritable in human families with early-onset CAD, 2 suggesting that the known heritability of CAD may be mediated at least in part through metabolic components measurable in peripheral blood. Clinical Perspective on p 214In this study, we performed quantitative profiling of 69 metabolites, including acylcarnitine species (byproducts of mitochondrial fatty aci...

Background Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype. Methods We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons. Results An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10−20), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P = 8×10−10). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P = 4×10−6). Conclusions Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.)

Many modern human genomes retain DNA inherited from interbreeding with archaic hominins, such as Neanderthals, yet the influence of this admixture on human traits is largely unknown. We analyzed the contribution of common Neanderthal variants to over 1,000 electronic health record (EHR)-derived phenotypes in ~28,000 adults of European ancestry. We discovered and replicated associations of Neanderthal alleles with neurological, psychiatric, immunological, and dermatological phenotypes. Neanderthal alleles together explain a significant fraction of the variation in risk for depression and skin lesions resulting from sun exposure (actinic keratosis), and individual Neanderthal alleles are significantly associated with specific human phenotypes, including hypercoagulation and tobacco use. Our results establish that archaic admixture influences disease risk in modern humans, provide hypotheses about the effects of hundreds of Neanderthal haplotypes and demonstrate the utility of EHR data in evolutionary analyses.

Recommendations for laboratories to report incidental findings from genomic tests have stimulated interest in such results. In order to investigate the criteria and processes for assigning the pathogenicity of specific variants and to estimate the frequency of such incidental findings in patients of European and African ancestry, we classified potentially actionable pathogenic single-nucleotide variants (SNVs) in all 4300 European- and 2203 African-ancestry participants sequenced by the NHLBI Exome Sequencing Project (ESP). We considered 112 gene-disease pairs selected by an expert panel as associated with medically actionable genetic disorders that may be undiagnosed in adults. The resulting classifications were compared to classifications from other clinical and research genetic testing laboratories, as well as with in silico pathogenicity scores. Among European-ancestry participants, 30 of 4300 (0.7%) had a pathogenic SNV and six (0.1%) had a disruptive variant that was expected to be pathogenic, whereas 52 (1.2%) had likely pathogenic SNVs. For African-ancestry participants, six of 2203 (0.3%) had a pathogenic SNV and six (0.3%) had an expected pathogenic disruptive variant, whereas 13 (0.6%) had likely pathogenic SNVs. Genomic Evolutionary Rate Profiling mammalian conservation score and the Combined Annotation Dependent Depletion summary score of conservation, substitution, regulation, and other evidence were compared across pathogenicity assignments and appear to have utility in variant classification. This work provides a refined estimate of the burden of adult onset, medically actionable incidental findings expected from exome sequencing, highlights challenges in variant classification, and demonstrates the need for a better curated variant interpretation knowledge base.

Aims/hypothesis Insulin resistance (IR) improves with weight loss, but this response is heterogeneous. We hypothesised that metabolomic profiling would identify biomarkers predicting changes in IR with weight loss. Methods Targeted mass spectrometry-based profiling of 60 metabolites, plus biochemical assays of NEFA, β-hydroxybutyrate, ketones, insulin and glucose were performed in baseline and 6 month plasma samples from 500 participants who had lost ≥4 kg during Phase I of the Weight Loss Maintenance (WLM) trial. Homeostatic model assessment of insulin resistance (HOMA-IR) and change in HOMA-IR with weight loss (ΔHOMA-IR) were calculated. Principal components analysis (PCA) and mixed models adjusted for race, sex, baseline weight, and amount of weight loss were used; findings were validated in an independent cohort of patients (n=22). Results Mean weight loss was 8.67±4.28 kg; mean ΔHOMA-IR was −0.80±1.73, range −28.9 to 4.82). Baseline PCA-derived factor 3 (branched chain amino acids [BCAAs] and associated catabolites) correlated with baseline HOMA-IR (r=0.50, p<0.0001) and independently associated with ΔHOMA-IR (p<0.0001). ΔHOMA-IR increased in a linear fashion with increasing baseline factor 3 quartiles. Amount of weight loss was only modestly correlated with ΔHOMA-IR (r=0.24). These findings were validated in the independent cohort, with a factor composed of BCAAs and related metabolites predicting ΔHOMA-IR (p=0.007). Conclusions/interpretation A cluster of metabolites comprising BCAAs and related analytes predicts improvement in HOMA-IR independent of the amount of weight lost. These results may help identify individuals most likely to benefit from moderate weight loss and elucidate novel mechanisms of IR in obesity.