Enabling personalized cancer medicine through analysis of gene-expression patterns

Therapies for patients with cancer have changed gradually over the past decade, moving away from the administration of broadly acting cytotoxic drugs towards the use of more-specific therapies that are targeted to each tumour. To facilitate this shift, tests need to be developed to identify those individuals who require therapy and those who are most likely to benefit from certain therapies. In particular, tests that predict the clinical outcome for patients on the basis of the genes expressed by their tumours are likely to increasingly affect patient management, heralding a new era of personalized medicine.     

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).     

A second generation human haplotype map of over 3.1 million SNPs

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.     

Playing Dissymmetry in Action Research: The Role of Power and Differences in Promoting Participative Knowledge and Change

This article explores the importance of power and dissymmetry in promoting participative knowledge and change in action research. Based on the analysis of two action research cases, the paper builds its argument by analyzing two key aspects: the construction of the action research setting and its maintenance during the process. It does so by highlighting the decisions assumed with respect to the relationship between researchers and participants and with respect to power issues. The findings indicate that promoting a functional dissymmetry in internal relationships allows distribution of the necessary types of power that make the participants use their authority and knowledge to invest in change. Thus, the distributed leadership is essential every time an organization needs to create a realistic and workable change of roles and responsibilities inside its boundaries. The article discusses some key factors in employing dissymmetry for sustained learning and knowledge-sharing.     

Reaction of pyridoxal-5′-phosphate with γ-carboxyglutamic acid

Summary Pyridoxal-5-phosphate (PLP) reacts with -carboxyglutamic acid (Gla) to form a stable complex absorbing at 325 nm. It is suggested that a condensation occurs in which the formyl group of PLP reacts with the -amino group and the carbon atom of Gla to give a pyrrolidine derivative.     

Multi‐model Forecasts of the West Texas Intermediate Crude Oil Spot Price

We measure the performance of multi‐model inference (MMI) forecasts compared to predictions made from a single model for crude oil prices. We forecast the West Texas Intermediate (WTI) crude oil spot prices using total OECD petroleum inventory levels, surplus production capacity, the Chicago Board Options Exchange Volatility Index and an implementation of a subset autoregression with exogenous variables (SARX). Coefficient and standard error estimates obtained from SARX determined by conditioning on a single ‘best model’ ignore model uncertainty and result in underestimated standard errors and overestimated coefficients. We find that the MMI forecast outperforms a single‐model forecast for both in‐ and out‐of‐sample datasets over a variety of statistical performance measures, and further find that weighting models according to the Bayesian information criterion generally yields superior results both in and out of sample when compared to the Akaike information criterion. Copyright © 2016 John Wiley & Sons, Ltd.