Genetic Loci implicated in erythroid differentiation and cell cycle regulation are associated with red blood cell traits

ObjectiveTo identify common genetic variants influencing red blood cell (RBC) traits.Patients and MethodsWe performed a genomewide association study from June 2008 through July 2011 of hemoglobin, hematocrit, RBC count, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration in 12,486 patients of European ancestry from the electronic MEdical Records and Genomics (eMERGE) network. We developed an electronic medical record–based algorithm that included individuals who had RBC measurements obtained for clinical care and excluded values measured in the setting of hematopoietic disorders, comorbid conditions, or medications known to affect RBC production or a recent history of blood loss.ResultsWe identified 4 new genetic loci and replicated 11 loci previously reported to be associated with one or more RBC traits in individuals of European ancestry. Notably, genes present in 3 of the 4 newly identified loci (THRB, PTPLAD1, CDT1) and in 6 of the 11 replicated loci (KLF1, ALDH8A1, CCND3, SPTA1, FBXO7, TFR2/EPO) are implicated in erythroid differentiation and regulation of cell cycle in hematopoietic stem cells.ConclusionGenes in the erythroid differentiation and cell cycle regulation pathways influence interindividual variation in RBC indices. Our results provide insights into the molecular basis underlying variation in RBC traits.     

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.     

Arterial ultrasonography and tonometry as adjuncts to cardiovascular risk stratification

Myocardial infarction and stroke often occur without prior warning in asymptomatic individuals. Identifying individuals at risk is important for cost-effective use of preventive therapies. Algorithms based on risk factors statistically associated with cardiovascular events classify individuals into high-risk, intermediate-risk, or low-risk categories. However, more than one-third of adults in the U.S. are in the intermediate-risk category, and decisions regarding therapy are challenging in this subset. Testing for alterations in arterial function and structure that predate cardiovascular events may help refine cardiovascular risk assessment in the intermediate-risk group and identify candidates for aggressive therapy. Vascular ultrasonography and tonometry are promising test modalities for assessment of arterial function and structure in asymptomatic subjects. Several prospective studies have shown that measures of arterial function and structure provide prognostic information incremental to conventional risk factors. Standardization of methodology and establishment of quality control standards in the performance of these tests could facilitate their integration into clinical practice as adjuncts to existing cardiovascular risk stratification algorithms.     

Associations of serum uric acid with markers of inflammation, metabolic syndrome, and subclinical coronary atherosclerosis

BACKGROUND: We examined the associations of serum uric acid (UA) with indices of coronary heart disease (CHD) risk, including the 10-year probability of CHD (10y-CHDr), metabolic syndrome (MS), inflammation (C-reactive protein [CRP] and fibrinogen), and the presence and quantity of coronary artery calcium (CAC). METHODS: Subjects (n = 1107, mean age 58 years, 59% women) belonged to sibships with > or =2 individuals with hypertension diagnosed before age 60 years. UA was measured by a colorimetric method, CAC by electron beam computed tomography, and CAC score calculated using the method of Agatston. The correlation of UA with 10y-CHDr, MS components, log CRP, and fibrinogen was assessed after adjustment for age and gender. Multivariable regression was used to assess whether UA was associated with CAC presence and quantity after (1) adjustment for age and gender, and (2) additional adjustment for CHD risk factors. RESULTS: Most subjects (71%) had hypertension and 14% had diabetes. Mean (+/- SD) UA level was 5.97 +/- 1.6 mg/dL, and CAC was detectable in 63% of patients. After adjustment for age and gender, UA was significantly correlated with 10y-CHDr, number of MS components, log CRP, and fibrinogen. UA was associated with CAC presence and quantity after adjustment for age and gender but not after further adjustment for systolic blood pressure (BP), diabetes, total and HDL-cholesterol, smoking, and body mass index (BMI). CONCLUSIONS: Serum UA was significantly correlated with several indices of CHD risk. UA was associated with presence and quantity of CAC, but not independently of conventional risk factors.     

Geographic differences in allele frequencies of susceptibility SNPs for cardiovascular disease

Background  

We hypothesized that the frequencies of risk alleles of SNPs mediating susceptibility to cardiovascular diseases differ among populations of varying geographic origin and that population-specific selection has operated on some of these variants.     

Pleiotropic genetic effects contribute to the correlation between HDL cholesterol, triglycerides, and LDL particle size in hypertensive sibships

OBJECTIVE: Abnormalities of HDL cholesterol (HDL-C), triglycerides, and LDL particle size are present in familial dyslipidemic hypertension. We investigated heritability of these three lipid traits and the extent to which shared effects of genes (pleiotropy) contribute to the additive genetic variation in each trait in hypertensive sibships. METHODS: Subjects included 788 individuals (60% women) ascertained through sibships with >/=2 members diagnosed with hypertension before age 60 years. The LDL particle size was measured by polyacrylamide gel electrophoresis. Triglycerides were log transformed to reduce skewness, and age- and sex-adjusted lipid traits were used in the analyses. Heritability and pairwise genetic correlations were computed using a variance components approach. The genetic correlation between a pair of traits was squared to yield genetic covariance, a measure of pleiotropic effects of genes influencing both traits concomitantly. RESULTS: Heritability estimates indicated significant genetic effects on HDL-C (0.58), log triglycerides (0.47), and LDL particle size (0.71). Genetic correlation was strongest between HDL-C and log triglycerides (-0.642), followed by log triglycerides and LDL particle size (-0.493), and HDL-C and LDL particle size (0.334). HDL-C and log triglycerides showed the strongest genetic covariance (41%), followed by LDL particle size and log triglycerides (24%), and HDL-C and LDL particle size (11%). CONCLUSIONS: Multivariate quantitative genetic analyses in hypertensive sibships reveal that pleiotropy contributes to the additive genetic variation in HDL-C, triglycerides, and LDL particle size. These findings provide the rationale for multivariate linkage analyses to identify novel genetic loci with pleiotropic effects on the traits.