Drug-gene interactions of antihypertensive medications and risk of incident cardiovascular disease: a pharmacogenomics study from the CHARGE consortium

Background Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals. Methods Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases). Results Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10−8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction ≥ 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure medications and the risk of incident CVD

The Promises and Pitfalls of Genoeconomics

This article reviews existing research at the intersection of genetics and economics, presents some new findings that illustrate the state of genoeconomics research, and surveys the prospects of this emerging field. Twin studies suggest that economic outcomes and preferences, once corrected for measurement error, appear to be about as heritable as many medical conditions and personality traits. Consistent with this pattern, we present new evidence on the heritability of permanent income and wealth. Turning to genetic association studies, we survey the main ways that the direct measurement of genetic variation across individuals is likely to contribute to economics, and we outline the challenges that have slowed progress in making these contributions. The most urgent problem facing researchers in this field is that most existing efforts to find associations between genetic variation and economic behavior are based on samples that are too small to ensure adequate statistical power. This has led to many false positives in the literature. We suggest a number of possible strategies to improve and remedy this problem: (a) pooling data sets, (b) using statistical techniques that exploit the greater information content of many genes jointly, and (c) focusing on economically relevant traits that are most proximate to known biological mechanisms.EconomicsSociolog

Evidence for Neutrino Oscillations from Muon Decay at Rest

A search for nu_bar_mu to nu_bar_e oscillations has been conducted at the Los Alamos Meson Physics Facility using nu_bar_mu from mu+ decay at rest. The nu_bar_e are detected via the reaction (nu_bar_e,p) -> (e+,n), correlated with the 2.2 MeV gamma from (n,p) -> (d,gamma). The use of tight cuts to identify e+ events with correlated gamma rays yields 22 events with e+ energy between 36 and 60 MeV and only 4.6 (+/- 0.6) background events. The probability that this excess is due entirely to a statistical fluctuation is 4.1E-08. A chi^2 fit to the entire e+ sample results in a total excess of 51.8 (+18.7) (-16.9) (+/- 8.0) events with e+ energy between 20 and 60 MeV. If attributed to nu_bar_mu -> nu_bar_e oscillations, this corresponds to an oscillation probability (averaged over the experimental energy and spatial acceptance) of 0.0031 (+0.0011) (-0.0010) (+/- 0.0005).Comment: 57 pages, 34 figures, revtex, additional information available at http://nu1.lampf.lanl.gov/~lsnd

‘Caution! The Bread is Poisoned’: The Hong Kong Mass Poisoning of January 1857

This article examines the Hong Kong mass poisoning of 15 January 1857, in which bread from a Chinese bakery that supplied the colonial community was adulterated with arsenic. Even though there is a wealth of printed and manuscript documentation available many vital aspects of the poisoning remain unclear. What kind of incident was it: an act of terrorism and attempted mass murder, a war crime, a criminal conspiracy, an act of commercial sabotage, an accident or even an imagined or imaginary event? Throughout, our focus remains firmly fixed on the central act of the poisoning itself and on what it reveals about the precarious nature of early colonial Hong Kong. Interpretations have swarmed over the available ‘facts'. Equally ironic is what happened to the afterlife of how the event was understood. This article seeks to rescue the Hong Kong poisoning from being a freakish and isolated footnote of only local interest. Accepting this historical verdict would be a mistake as it is of significance not only at a local level, but geopolitically in Britain and across the empire

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition