Acculturation Is Associated With Hypertension in a Multiethnic Sample

Background: Hypertension varies in prevalence among race/ethnic groups in the United States. Within-ethnic group differences associated with acculturation have been less frequently examined. We studied the association of three measures of acculturation (language spoken at home, place of birth, and years living in the US) with hypertension in a population sample of 2619 white, 1898 African American, 1,494 Hispanic, and 803 Chinese participants in the Multiethnic Study of Atherosclerosis. Methods: Multivariate Poisson regression was used to estimate the association between the acculturation variables and hypertension. Results: Birthplace outside the US and speaking a non-English language at home were each associated with a lower prevalence of hypertension after adjustment for age, gender, and socioeconomic status (prevalence ratio [95% confidence intervals] 0.82 (0.77–0.87) for non-US born versus US born and 0.80 (0.74–0.85) for those not speaking English at home versus speakers of English at home, both P < .001). For participants born outside of the US, each 10-year increment of years in the US was associated with a higher prevalence of hypertension after adjustment for age, gender, and socioeconomic status (P for trend < .01). The associations between acculturation variables and hypertension were weakened after adjustment for race/ethnic category and risk factors for hypertension. Compared to US-born Hispanics, those born in Mexico or South America had lower prevalence of hypertension, but those born in the Caribbean and Central America had higher prevalence of hypertension. Conclusions: Acculturation and place of birth are associated with hypertension in a multiethnic sample.http://deepblue.lib.umich.edu/bitstream/2027.42/57776/1/Acculturation is associated with hypertension in a multiethnic sample.pd

A LASSO-based approach to analyzing rare variants in genetic association studies

Genetic markers with rare variants are spread out in the genome, making it necessary and difficult to consider them in genetic association studies. Consequently, wisely combining rare variants into “composite” markers may facilitate meaningful analyses. In this paper, we propose a novel approach of analyzing rare variant data by incorporating the least absolute shrinkage and selection operator technique. We applied this method to the Genetic Analysis Workshop 17 data, and our results suggest that this new approach is promising. In addition, we took advantage of having 200 phenotype replications and assessed the performance of our approach by means of repeated classification tree analyses. Our method and analyses were performed without knowledge of the underlying simulating model. Our method identified 38 markers (in 65 genes) that are significantly associated with the phenotype Affected and correctly identified two causal genes, SIRT1 and PDGFD

The PhenX Toolkit: Get the Most From Your Measures

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

Misperceptions About β-Blockers and Diuretics

Based on a series of clinical trials showing no difference in the effectiveness or tolerability of most major classes of antihypertensive medications, the Joint National Commission on High Blood Pressure Treatment recommends that physicians prescribe β-blockers or diuretics as initial hypertensive therapy unless there are compelling indications for another type of medication. Nevertheless, many physicians continue to favor more expensive medications like angiotensin-converting enzyme (ACE) inhibitors and calcium channel blockers as first line agents. The persistent use of these agents raises questions as to whether physicians perceive ACE inhibitors and calcium channel blockers to be better than β-blockers and diuretics. METHODS:  We surveyed 1,200 primary care physicians in 1997, and another 500 primary care physicians in 2000, and asked them to estimate the relative effectiveness and side effects of 4 classes of medication in treating a hypothetical patient with uncomplicated hypertension: ACE inhibitors, β-blockers, calcium channel blockers, and diuretics. In addition, we asked them to indicate whether they ever provided free samples of hypertension medications to their patients. RESULTS:  Perceptions of the relative effectiveness and side effects of the 4 classes of hypertension medications did not significantly change over the 3 years, nor did prescription recommendations. Physicians perceive that diuretics are less effective at lowering blood pressure than the other 3 classes ( P  < .001). They also perceive that β-blockers are less tolerated than the other 3 classes ( P  < .001). In a multivariate model, perceptions of effectiveness and tolerability displayed significant associations with prescription preference independent of background variables. The only other variable to contribute significantly to the model was provision of free medication samples to patients. CONCLUSIONS:  Despite numerous clinical trials showing no difference in the effectiveness or side-effect profiles of these 4 classes of drugs, most physicians believed that diuretics were less effective and β-blockers were less tolerated than other medications. Moreover, their prescription practices were associated with their provision of free samples provided by pharmaceutical representatives, even after adjusting for other demographic characteristics. Efforts to increase physicians’ prescribing of β-blockers and diuretics may need to be directed at overcoming misunderstandings about the effectiveness and tolerability of these medicines. J GEN INTERN MED 2003;18:977–983.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75385/1/j.1525-1497.2003.20414.x.pd

Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model

Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype

Another tool in the genome-wide association study arsenal: population-based detection of somatic gene conversion

The hunt for the genetic contributors to complex disease has used a number of strategies, resulting in the identification of variants associated with many of the common diseases affecting society. However most of the genetic variants detected to date are single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) and fall far short of explaining the full genetic component of any given disease. An as yet untapped genomic mechanism is somatic gene conversion and deletion, which could be complicit in disease risk but has been challenging to detect in genome-wide datasets. In a recent publication in BMC Medicine by Kenneth Ross, the author uses existing datasets to look at somatic gene conversion and deletion in human disease. Here, we describe how Ross's recent efforts to detect such occurrences could impact the field going forward

Database resources of the National Center for Biotechnology Information

In addition to maintaining the GenBank(®) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link(BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace and Assembly Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Viral Genotyping Tools, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at