Daniel I. Chasman

Multiple loci associated with indices of renal function and chronic kidney disease

Chronic kidney disease (CKD) has a heritable component and is an important global public health problem because of its high prevalence and morbidity. We conducted genome-wide association studies (GWAS) to identify susceptibility loci for glomerular filtration rate, estimated by serum creatinine (eGFRcrea) and cystatin C (eGFRcys), and CKD (eGFRcrea < 60 ml/min/1.73 m2) in European-ancestry participants of four population-based cohorts (ARIC, CHS, FHS, RS; n = 19,877; 2,388 CKD cases), and tested for replication in 21,466 participants (1,932 CKD cases). We identified significant SNP associations (P < 5 × 10−8) with CKD at the UMOD locus, with eGFRcrea at UMOD, SHROOM3 and GATM-SPATA5L1, and with eGFRcys at CST and STC1. UMOD encodes the most common protein in human urine, Tamm-Horsfall protein, and rare mutations in UMOD cause mendelian forms of kidney disease. Our findings provide new insights into CKD pathogenesis and underscore the importance of common genetic variants influencing renal function and disease.

Meta-analysis identifies six new susceptibility loci for atrial fibrillation

Atrial fibrillation is a highly prevalent arrhythmia and a major risk factor for stroke, heart failure and death. We conducted a genome-wide association study (GWAS) in individuals of European ancestry, including 6,707 with and 52,426 without atrial fibrillation. Six new atrial fibrillation susceptibility loci were identified and replicated in an additional sample of individuals of European ancestry, including 5,381 subjects with and 10,030 subjects without atrial fibrillation (P < 5 × 10−8). Four of the loci identified in Europeans were further replicated in silico in a GWAS of Japanese individuals, including 843 individuals with and 3,350 individuals without atrial fibrillation. The identified loci implicate candidate genes that encode transcription factors related to cardiopulmonary development, cardiac-expressed ion channels and cell signaling molecules.

Association between a literature-based genetic risk score and cardiovascular events in women.

CONTEXT While multiple genetic markers associated with cardiovascular disease have been identified by genome-wide association studies, their aggregate effect on risk beyond traditional factors is uncertain, particularly among women. OBJECTIVE To test the predictive ability of a literature-based genetic risk score for cardiovascular disease. DESIGN, SETTING, AND PARTICIPANTS Prospective cohort of 19,313 initially healthy white women in the Womens Genome Health Study followed up over a median of 12.3 years (interquartile range, 11.6-12.8 years). Genetic risk scores were constructed from the National Human Genome Research Institutes catalog of genome-wide association study results published between 2005 and June 2009. MAIN OUTCOME MEASURE Incident myocardial infarction, stroke, arterial revascularization, and cardiovascular death. RESULTS A total of 101 single nucleotide polymorphisms reported to be associated with cardiovascular disease or at least 1 intermediate cardiovascular disease phenotype at a published P value of less than 10(-7) were identified and risk alleles were added to create a genetic risk score. During follow-up, 777 cardiovascular disease events occurred (199 myocardial infarctions, 203 strokes, 63 cardiovascular deaths, 312 revascularizations). After adjustment for age, the genetic risk score had a hazard ratio (HR) for cardiovascular disease of 1.02 per risk allele (95% confidence interval [CI], 1.00-1.03/risk allele; P = .006). This corresponds to an absolute cardiovascular disease risk of 3% over 10 years in the lowest tertile of genetic risk (73-99 risk alleles) and 3.7% in the highest tertile (106-125 risk alleles). However, after adjustment for traditional factors, the genetic risk score did not improve discrimination or reclassification (change in c index from Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults [ATP III] risk score, 0; net reclassification improvement, 0.5%; [P = .24]). The genetic risk score was not associated with cardiovascular disease risk (ATP III-adjusted HR/allele, 1.00; 95% CI, 0.99-1.01). In contrast, self-reported family history remained significantly associated with cardiovascular disease in multivariable models. CONCLUSION After adjustment for traditional cardiovascular risk factors, a genetic risk score comprising 101 single nucleotide polymorphisms was not significantly associated with the incidence of total cardiovascular disease.

Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies.

Summary Background Persistent inflammation has been proposed to contribute to various stages in the pathogenesis of cardiovascular disease. Interleukin-6 receptor (IL6R) signalling propagates downstream inflammation cascades. To assess whether this pathway is causally relevant to coronary heart disease, we studied a functional genetic variant known to affect IL6R signalling. Methods In a collaborative meta-analysis, we studied Asp358Ala (rs2228145) in IL6R in relation to a panel of conventional risk factors and inflammation biomarkers in 125 222 participants. We also compared the frequency of Asp358Ala in 51 441 patients with coronary heart disease and in 136 226 controls. To gain insight into possible mechanisms, we assessed Asp358Ala in relation to localised gene expression and to postlipopolysaccharide stimulation of interleukin 6. Findings The minor allele frequency of Asp358Ala was 39%. Asp358Ala was not associated with lipid concentrations, blood pressure, adiposity, dysglycaemia, or smoking (p value for association per minor allele ≥0·04 for each). By contrast, for every copy of 358Ala inherited, mean concentration of IL6R increased by 34·3% (95% CI 30·4–38·2) and of interleukin 6 by 14·6% (10·7–18·4), and mean concentration of C-reactive protein was reduced by 7·5% (5·9–9·1) and of fibrinogen by 1·0% (0·7–1·3). For every copy of 358Ala inherited, risk of coronary heart disease was reduced by 3·4% (1·8–5·0). Asp358Ala was not related to IL6R mRNA levels or interleukin-6 production in monocytes. Interpretation Large-scale human genetic and biomarker data are consistent with a causal association between IL6R-related pathways and coronary heart disease. Funding British Heart Foundation; UK Medical Research Council; UK National Institute of Health Research, Cambridge Biomedical Research Centre; BUPA Foundation.

Loci Related to Metabolic-Syndrome Pathways Including LEPR,HNF1A, IL6R, and GCKR Associate with Plasma C-Reactive Protein: The Women's Genome Health Study

Although elevated levels of C-reactive protein (CRP) independently predict increased risk of development of metabolic syndrome, diabetes, myocardial infarction, and stroke, comprehensive analysis of the influence of genetic variation on CRP is not available. To address this issue, we performed a genome-wide association study among 6345 apparently healthy women in which we evaluated 336,108 SNPs as potential determinants of plasma CRP concentration. Overall, seven loci that associate with plasma CRP at levels achieving genome-wide statistical significance were found (range of p values for lead SNPs within the seven loci: 1.9 x 10(-)(8) to 6.2 x 10(-)(28)). Two of these loci (GCKR and HNF1A) are suspected or known to be associated with maturity-onset diabetes of the young, one is a gene-desert region on 12q23.2, and the remaining four loci are in or near the leptin receptor protein gene, the apolipoprotein E gene, the interleukin-6 receptor protein gene, or the CRP gene itself. The protein products of six of these seven loci are directly involved in metabolic syndrome, insulin resistance, beta cell function, weight homeostasis, and/or premature atherothrombosis. Thus, common variation in several genes involved in metabolic and inflammatory regulation have significant effects on CRP levels, consistent with CRPs identification as a useful biomarker of risk for incident vascular disease and diabetes.

Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis.

While conventional LDL-C, HDL-C, and triglyceride measurements reflect aggregate properties of plasma lipoprotein fractions, NMR-based measurements more accurately reflect lipoprotein particle concentrations according to class (LDL, HDL, and VLDL) and particle size (small, medium, and large). The concentrations of these lipoprotein sub-fractions may be related to risk of cardiovascular disease and related metabolic disorders. We performed a genome-wide association study of 17 lipoprotein measures determined by NMR together with LDL-C, HDL-C, triglycerides, ApoA1, and ApoB in 17,296 women from the Womens Genome Health Study (WGHS). Among 36 loci with genome-wide significance (P<5×10−8) in primary and secondary analysis, ten (PCCB/STAG1 (3q22.3), GMPR/MYLIP (6p22.3), BTNL2 (6p21.32), KLF14 (7q32.2), 8p23.1, JMJD1C (10q21.3), SBF2 (11p15.4), 12q23.2, CCDC92/DNAH10/ZNF664 (12q24.31.B), and WIPI1 (17q24.2)) have not been reported in prior genome-wide association studies for plasma lipid concentration. Associations with mean lipoprotein particle size but not cholesterol content were found for LDL at four loci (7q11.23, LPL (8p21.3), 12q24.31.B, and LIPG (18q21.1)) and for HDL at one locus (GCKR (2p23.3)). In addition, genetic determinants of total IDL and total VLDL concentration were found at many loci, most strongly at LIPC (15q22.1) and APOC-APOE complex (19q13.32), respectively. Associations at seven more loci previously known for effects on conventional plasma lipid measures reveal additional genetic influences on lipoprotein profiles and bring the total number of loci to 43. Thus, genome-wide associations identified novel loci involved with lipoprotein metabolism—including loci that affect the NMR-based measures of concentration or size of LDL, HDL, and VLDL particles—all characteristics of lipoprotein profiles that may impact disease risk but are not available by conventional assay.

Cardiovascular Disease Risk Prediction With and Without Knowledge of Genetic Variation at Chromosome 9p21.3

Context Although genetic variation at chromosome 9p21.3 is associated with cardiovascular disease, it is not known whether evaluating this polymorphism adds in a clinically meaningful way to the evaluation of cardiovascular risk using more conventional risk factors, such as family history of early cardiovascular disease, smoking, blood pressure, cholesterol level, and C-reactive protein level. Contribution This study evaluated 9p21.3 polymorphism and more conventional cardiovascular risk factors in 22129 white, female health professionals followed for a median of 10 years and found that addition of the genetic information did not improve clinical classification of a womans risk for cardiovascular disease. Caution The findings may not apply to men or to nonwhite women. The Editors The success of risk prediction models for cardiovascular disease reflects an increasing understanding of the molecular basis of atherothrombosis. Aside from age, which integrates many biological activities and environmental exposures at once, other important components of risk prediction include plasma biomarkers for lipid metabolism, inflammation, thrombosis, and metabolic status (1). Current prediction models, however, cannot anticipate many cases of incident cardiovascular disease, motivating both the identification of new risk factors and the optimization of analytic methods for their use. Traditionally aggregated by the notion of family history, individual genetic variants may represent a class of risk factors with new prognostic information; they may be more removed from underlying disease processes than plasma risk factors or other clinical variables but may also be more general. For example, a recent exploration of the influence of lipid-associated variation on prediction of incident cardiovascular disease found residual predictive value for the genetic variation after adjustment for plasma lipid levels (2). Genetic variation at the chromosome 9p21.3 region is a good candidate for adding more information to risk prediction. Variation at this locus has consistently been associated with coronary artery disease (35) and diabetes (69). In addition, the risk allele is carried by almost 75% of the white population and the lack of correlation between any of the disease-associated 9p21.3 genetic variants and major cardiovascular risk factors suggests novel influences on disease progression. To assess whether knowledge of variation at 9p21.3 improves global risk prediction, we examined the effect of adding genetic information from a single nucleotide polymorphism (SNP) in the 9p21.3 region to previously published prediction models for the WHS (Womens Health Study), which has a large, prospective cohort of initially healthy U.S. women who were followed over 10 years for incident cardiovascular events. Methods Study Population Study participants were members of the WGHS (Womens Genome Health Study) (10), an ongoing prospective genetic evaluation study being conducted among initially healthy U.S. women who enrolled in the WHS, a trial of aspirin and vitamin E for the primary prevention of cardiovascular disease and cancer in women 45 years or older (11). Beginning in 1992, the WHS recruited female health professionals in the United States who had no major chronic disease at baseline, including cancer and cardiovascular disease, and followed them prospectively for incident myocardial infarction, stroke, coronary revascularization, and cardiovascular death. The institutional review board of the Brigham and Womens Hospital, Boston, Massachusetts, approved the study. Among the WHS participants, 28345 provided blood samples that were stored in liquid nitrogen until the time of analysis, as well as consent for ongoing analyses linking blood-derived observations with baseline risk factor profiles and incident disease events. Of these women, 23226 had standard risk factor information available and were genotyped for the rs10757274 polymorphism. To reduce the potential for population stratification to affect our results, we included only the 22129 (95.3%) white women. Risk Factor Ascertainment Baseline information on age, diabetes, smoking status, parental history of myocardial infarction before 60 years of age, blood pressure, and hypertension treatment were collected at study initiation. Plasma biomarkers were analyzed in a core laboratory facility, certified by the National Heart, Lung, and Blood Institute/Centers for Disease Control and Prevention Lipid Standardization Program, for total cholesterol, high-density lipoprotein cholesterol, apolipoprotein B-100, apolipoprotein A-I, high-sensitivity C-reactive protein, lipoprotein(a), and hemoglobin A1c. We determined genotypes for rs10757274 in the WGHS participants by using an oligonucleotide ligation procedure that combined polymerase chain reaction amplification of target sequences from 3 ng of genomic DNA with subsequent allele-specific oligonucleotide ligation (12). The ligation products of the 2 alleles were separated by hybridization to product-specific oligonucleotides, each coupled to spectrally distinct Luminex100 xMAP microspheres (Luminex, Austin, Texas). The captured products were fluorescently labeled with streptavidin Rphycoerythrin (Prozyme, San Leandro, California), sorted on the basis of microsphere spectrum, and detected by a Luminex100 instrument (12). Outcome Ascertainment Study participants were followed through March 2004 for total cardiovascular disease, which comprised incident myocardial infarction, ischemic stroke, coronary revascularization, and cardiovascular deaths. An end-points committee adjudicated events by using medical record review. Morbidity data were available on nearly all the women through 8 years of follow-up. Risk Prediction Models To assess the effect of variation at rs10757274 on global cardiovascular disease risk prediction, we considered covariates from 2 nongenetic risk prediction models. The first included the covariates from the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) risk score, as well as history of diabetes (noted as a high-risk equivalent) (13). The second used the covariates from the Reynolds Risk Score (1), a model that includes additional biomarker information, as well as data on family history. To provide direct comparability and the highest level of internal validity, we elected on an a priori basis to model all covariates, including rs10757274, in the same base population rather than use -coefficients for the nongenetic covariates derived from previously published models. In so doing, we avoided the potential for bias that might occur by modeling the effect of the genetic data within the test cohort but using estimates of effect for the other covariates from a second, unrelated cohort. Statistical Analysis We used Cox proportional hazard models to generate crude and adjusted hazard ratios across genotypes and to test for trend. We used models with separate effects for each genotype in all analyses and generated adjusted survival curves by stratifying Cox proportional hazard models by genotype. We also used Cox models to generate estimates of predicted risk with and without genotype information, which we then assessed for accuracy. We saw no evidence of departures from proportionality in any of the models used. Our primary measure of discrimination was the Harrell c-index (14), a generalization of the area under the receiver-operating characteristic curve that allows for censored data. The c-index assesses the ability of the risk score to rank women who develop incident cardiovascular disease higher than women who do not. We assessed general calibration across deciles of predicted risk by using the HosmerLemeshow goodness-of-fit test (15) to compare the average predicted risk with the KaplanMeier risk estimate within each decile and considered a chi-square value of 20 or higher (P< 0.01) to be poor calibration (16). We assessed risk reclassification (1, 17) by sorting the predicted 10-year risk for each model into 4 categories (<5%, 5% to <10%, 10% to <20%, and 20%). We then compared the assigned categories for a pair of models. For each pair, we calculated the proportion of participants who were reclassified by the comparison model versus the reference model; we considered reclassification to be correct if the KaplanMeier risk estimate for the reclassified group was closer to the comparison category than the reference. We computed the HosmerLemeshow statistic for the reclassification tables (18), which assesses agreement between the KaplanMeier risk estimate and predicted risk within the reclassified categories. We also computed the Net Reclassification Improvement (19), which compares the shifts in reclassified categories by observed outcome, and the Integrated Discrimination Improvement (19), which directly compares the average difference in predicted risk for women who go on to develop cardiovascular disease with women who do not for the 2 models, on the women who were not censored before 8 years. Role of the Funding Source This study was supported by grants from the National Heart, Lung, and Blood Institute and National Cancer Institue, National Institutes of Health; the Donald W. Reynolds Foundation; and the Leducq Foundation. Additional support for DNA extraction, reagents, and data analysis was provided by Roche Diagnostics and Amgen. Genotyping of the 9p21.3 variant was performed by Celera. The funding sources had no role in the design, conduct, or reporting of this study or the decision to submit the manuscript for publication. Results Of the 22129 white women genotyped for this analysis, 5793 (26.2%) had no risk (G) alleles at rs10757374, 10952 (49.5%) had 1 risk allele, and 5384 (24.3%) had 2 risk alleles. The number of risk alleles had a significant association with family history of premature myocardial

Genome-wide association study reveals three susceptibility loci for common migraine in the general population

Migraine is a common, heterogeneous and heritable neurological disorder. Its pathophysiology is incompletely understood, and its genetic influences at the population level are unknown. In a population-based genome-wide analysis including 5,122 migraineurs and 18,108 non-migraineurs, rs2651899 (1p36.32, PRDM16), rs10166942 (2q37.1, TRPM8) and rs11172113 (12q13.3, LRP1) were among the top seven associations (P < 5 × 10−6) with migraine. These SNPs were significant in a meta-analysis among three replication cohorts and met genome-wide significance in a meta-analysis combining the discovery and replication cohorts (rs2651899, odds ratio (OR) = 1.11, P = 3.8 × 10−9; rs10166942, OR = 0.85, P = 5.5 × 10−12; and rs11172113, OR = 0.90, P = 4.3 × 10−9). The associations at rs2651899 and rs10166942 were specific for migraine compared with non-migraine headache. None of the three SNP associations was preferential for migraine with aura or without aura, nor were any associations specific for migraine features. TRPM8 has been the focus of neuropathic pain models, whereas LRP1 modulates neuronal glutamate signaling, plausibly linking both genes to migraine pathophysiology.

Inactivating mutations in NPC1L1 and protection from coronary heart disease

BACKGROUND Ezetimibe lowers plasma levels of low-density lipoprotein (LDL) cholesterol by inhibiting the activity of the Niemann-Pick C1-like 1 (NPC1L1) protein. However, whether such inhibition reduces the risk of coronary heart disease is not known. Human mutations that inactivate a gene encoding a drug target can mimic the action of an inhibitory drug and thus can be used to infer potential effects of that drug. METHODS We sequenced the exons of NPC1L1 in 7364 patients with coronary heart disease and in 14,728 controls without such disease who were of European, African, or South Asian ancestry. We identified carriers of inactivating mutations (nonsense, splice-site, or frameshift mutations). In addition, we genotyped a specific inactivating mutation (p.Arg406X) in 22,590 patients with coronary heart disease and in 68,412 controls. We tested the association between the presence of an inactivating mutation and both plasma lipid levels and the risk of coronary heart disease. RESULTS With sequencing, we identified 15 distinct NPC1L1 inactivating mutations; approximately 1 in every 650 persons was a heterozygous carrier for 1 of these mutations. Heterozygous carriers of NPC1L1 inactivating mutations had a mean LDL cholesterol level that was 12 mg per deciliter (0.31 mmol per liter) lower than that in noncarriers (P=0.04). Carrier status was associated with a relative reduction of 53% in the risk of coronary heart disease (odds ratio for carriers, 0.47; 95% confidence interval, 0.25 to 0.87; P=0.008). In total, only 11 of 29,954 patients with coronary heart disease had an inactivating mutation (carrier frequency, 0.04%) in contrast to 71 of 83,140 controls (carrier frequency, 0.09%). CONCLUSIONS Naturally occurring mutations that disrupt NPC1L1 function were found to be associated with reduced plasma LDL cholesterol levels and a reduced risk of coronary heart disease. (Funded by the National Institutes of Health and others.).

Understanding multicellular function and disease with human tissue-specific networks

Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.