Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10−8) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10−8). The top IBC association for SBP was rs2012318 (P= 6.4 × 10−6) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10−6) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexit

Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study.

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10(-8)) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10(-8)). The top IBC association for SBP was rs2012318 (P= 6.4 × 10(-6)) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10(-6)) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity

Experimental UK Regional Consumer Price Inflation with Model-Based Expenditure Weights

Like many other countries, the United Kingdom (UK) produces a national consumer priceindex (CPI) to measure inflation. Presently, CPI measures are not produced for regions withinthe UK. It is believed that, using only available data sources, a regional CPI would not beprecise or reliable enough as an official statistic, primarily because the regional partitioning ofthe data makes sample sizes too small. We investigate this claim by producing experimentalregional CPIs using publicly available price data, and deriving expenditure weights from theLiving Costs and Food survey. We detail the methods and challenges of developing a regional CPI and evaluate its reliability. We then assess whether model-based methods such assmoothing and small area estimation significantly improve the measures. We find that a regional CPI can be produced with available data sources, however it appears to beexcessively volatile over time, mainly due to the weights. Smoothing and small areaestimation improve the reliability of the regional CPI series to some extent but they remain toovolatile for regional policy use. This research provides a valuable framework for thedevelopment of a more viable regional CPI measure for the UK in the future

Construction of regional consumer price indices using small area estimation

Consumer Price Indices (CPI) are used in many ways by the government, businesses, and society in general. They can affect interest rates, tax allowances, wages, state benefits, and many other payments. The CPI is a fixed (national) basket index, where a range of goods and services is priced each month, and the expenditure shares on items in the basket are used to weight the price information together. The starting point for a regional price index should be a regional basket of goods and services. In the current poster, we derive regional baskets from the UK Living Costs and Food Survey (LCF), taking the products (COICOP classification) with the largest proportion of expenditures. As the sample size is naturally much smaller for regions, the accuracy of the direct estimates on the basket will be reduced. In order to overcome this problem one possibility - discussed in the poster - is to pool multiple years of LCF data to increase the sample size. Another is to consider small area estimation approaches for the regional basket. Ideally, the small area estimates would be constrained to the overall expenditure total. Therefore, we assess some benchmarking approaches. Since the conceptual framework of CPI-calculationfor the UK and Germany do not differ too much the presented methodology can also be adapted for the calculation of regional CPIs for Germany.<br/

Deliverable 3.2 - Guidelines for best practices implementation for transferring methodology

Today, big data is a buzz word. Although there have been attempts to properly define the term, a really universally accepted definition has not yet been given. Accordingly, many different types of data may be classified as big data or new data. These range from scanner data collected at retail outlets, through remote sensing data to mobile phone data. As the availability of such data increases, researchers try to make use of them by incorporating them into existing methods and developing new methods. These developments are also highly relevant for the estimation of well-being indicators, a core focus of the MAKSWELL project. The combination of new data sources and new or modified methods are promising especially where the estimation of well-being at a fine spatial resolution is concerned. While a comprehensive survey of the related literature and available data sets is out of the scope of this project, this deliverable collects a few (experimental) applications that shed a light on the potential benefits of these new approaches. Some drawbacks and practical implementation problems are addressed as well. Taken as a whole, the presented set of applications points to future research needs in the area and allows the derivation of some general best practice guidelines that can also inform other subject matter areas beside the measurement of poverty and well-being

Thermal issues in machine tools

ISSN:0007-8506ISSN:1660-277