Genotype-Temperature Interaction in the Regulation of Development, Growth, and Morphometrics in Wild-Type, and Growth-Hormone Transgenic Coho Salmon

The neuroendocrine system is an important modulator of phenotype, directing cellular genetic responses to external cues such as temperature. Behavioural and physiological processes in poikilothermic organisms (e.g. most fishes), are particularly influenced by surrounding temperatures.By comparing the development and growth of two genotypes of coho salmon (wild-type and transgenic with greatly enhanced growth hormone production) at six different temperatures, ranging between 8 degrees and 18 degrees C, we observed a genotype-temperature interaction and possible trend in directed neuroendocrine selection. Differences in growth patterns of the two genotypes were compared by using mathematical models, and morphometric analyses of juvenile salmon were performed to detect differences in body shape. The maximum hatching and alevin survival rates of both genotypes occurred at 12 degrees C. At lower temperatures, eggs containing embryos with enhanced GH production hatched after a shorter incubation period than wild-type eggs, but this difference was not apparent at and above 16 degrees C. GH transgenesis led to lower body weights at the time when the yolk sack was completely absorbed compared to the wild genotype. The growth of juvenile GH-enhanced salmon was to a greater extent stimulated by higher temperatures than the growth of the wild-type. Increased GH production significantly influenced the shape of the salmon growth curves.Growth hormone overexpression by transgenesis is able to stimulate the growth of coho salmon over a wide range of temperatures. Temperature was found to affect growth rate, survival, and body morphology between GH transgenic and wild genotype coho salmon, and differential responses to temperature observed between the genotypes suggests they would experience different selective forces should they ever enter natural ecosystems. Thus, GH transgenic fish would be expected to differentially respond and adapt to shifts in environmental conditions compared with wild type, influencing their ability to survive and interact in ecosystems. Understanding these relationships would assist environmental risk assessments evaluating potential ecological effects

Effect of population stratification analysis on false-positive rates for common and rare variants

Principal components analysis (PCA) has been successfully used to correct for population stratification in genome-wide association studies of common variants. However, rare variants also have a role in common disease etiology. Whether PCA successfully controls population stratification for rare variants has not been addressed. Thus we evaluate the effect of population stratification analysis on false-positive rates for common and rare variants at the single-nucleotide polymorphism (SNP) and gene level. We use the simulation data from Genetic Analysis Workshop 17 and compare false-positive rates with and without PCA at the SNP and gene level. We found that SNPs’ minor allele frequency (MAF) influenced the ability of PCA to effectively control false discovery. Specifically, PCA reduced false-positive rates more effectively in common SNPs (MAF > 0.05) than in rare SNPs (MAF < 0.01). Furthermore, at the gene level, although false-positive rates were reduced, power to detect true associations was also reduced using PCA. Taken together, these results suggest that sequence-level data should be interpreted with caution, because extremely rare SNPs may exhibit sporadic association that is not controlled using PCA

Population Substructure and Control Selection in Genome-Wide Association Studies

Determination of the relevance of both demanding classical epidemiologic criteria for control selection and robust handling of population stratification (PS) represents a major challenge in the design and analysis of genome-wide association studies (GWAS). Empirical data from two GWAS in European Americans of the Cancer Genetic Markers of Susceptibility (CGEMS) project were used to evaluate the impact of PS in studies with different control selection strategies. In each of the two original case-control studies nested in corresponding prospective cohorts, a minor confounding effect due to PS (inflation factor λ of 1.025 and 1.005) was observed. In contrast, when the control groups were exchanged to mimic a cost-effective but theoretically less desirable control selection strategy, the confounding effects were larger (λ of 1.090 and 1.062). A panel of 12,898 autosomal SNPs common to both the Illumina and Affymetrix commercial platforms and with low local background linkage disequilibrium (pair-wise r2<0.004) was selected to infer population substructure with principal component analysis. A novel permutation procedure was developed for the correction of PS that identified a smaller set of principal components and achieved a better control of type I error (to λ of 1.032 and 1.006, respectively) than currently used methods. The overlap between sets of SNPs in the bottom 5% of p-values based on the new test and the test without PS correction was about 80%, with the majority of discordant SNPs having both ranks close to the threshold. Thus, for the CGEMS GWAS of prostate and breast cancer conducted in European Americans, PS does not appear to be a major problem in well-designed studies. A study using suboptimal controls can have acceptable type I error when an effective strategy for the correction of PS is employed

Can Broader Diffusion of Value-Based Insurance Design Increase Benefits from US Health Care without Increasing Costs? Evidence from a Computer Simulation Model

Using a computer simulation based on US data, R. Scott Braithwaite and colleagues calculate the benefits of value-based insurance design, in which patients pay less for highly cost-effective services

Alcohol and risk of admission to hospital for unintentional cutting or piercing injuries at home: a population-based case-crossover study

<p>Abstract</p> <p>Background</p> <p>Cutting and piercing injuries are among the leading causes of unintentional injury morbidity in developed countries. In New Zealand, cutting and piercing are second only to falls as the most frequent cause of unintentional home injuries resulting in admissions to hospital among people aged 20 to 64 years. Alcohol intake is known to be associated with many other types of injury. We used a case-crossover study to investigate the role of acute alcohol use (i.e., drinking during the previous 6 h) in unintentional cutting or piercing injuries at home.</p> <p>Methods</p> <p>A population-based case-crossover study was conducted. We identified all people aged 20 to 64 years, resident in one of three regions of the country (Greater Auckland, Waikato and Otago), who were admitted to public hospital within 48 h of an unintentional non-occupational cutting or piercing injury sustained at home (theirs or another's) from August 2008 to December 2009. The main exposure of interest was use of alcohol in the 6-hour period before the injury occurred and the corresponding time intervals 24 h before, and 1 week before, the injury. Other information was collected on known and potential confounders. Information was obtained during face-to-face interviews with cases, and through review of their medical charts.</p> <p>Results</p> <p>Of the 356 participants, 71% were male, and a third sustained injuries from contact with glass. After adjustment for other paired exposures, the odds ratio for injury after consuming 1 to 3 standard drinks of alcohol during the 6-hour period before the injury (compared to the day before), compared to none, was 1.77 (95% confidence interval 0.84 to 3.74), and for four or more drinks was 8.68 (95% confidence interval 3.11 to 24.3). Smokers had higher alcohol-related risks than non-smokers.</p> <p>Conclusions</p> <p>Alcohol consumption increases the odds of unintentional cutting or piercing injury occurring at home and this risk increases with higher levels of drinking.</p

A model to prioritize access to elective surgery on the basis of clinical urgency and waiting time

<p>Abstract</p> <p>Background</p> <p>Prioritization of waiting lists for elective surgery represents a major issue in public systems in view of the fact that patients often suffer from consequences of long waiting times. In addition, administrative and standardized data on waiting lists are generally lacking in Italy, where no detailed national reports are available. This is true although since 2002 the National Government has defined implicit Urgency-Related Groups (URGs) associated with Maximum Time Before Treatment (MTBT), similar to the Australian classification. The aim of this paper is to propose a model to manage waiting lists and prioritize admissions to elective surgery.</p> <p>Methods</p> <p>In 2001, the Italian Ministry of Health funded the Surgical Waiting List Info System (SWALIS) project, with the aim of experimenting solutions for managing elective surgery waiting lists. The project was split into two phases. In the first project phase, ten surgical units in the largest hospital of the Liguria Region were involved in the design of a pre-admission process model. The model was embedded in a Web based software, adopting Italian URGs with minor modifications. The SWALIS pre-admission process was based on the following steps: 1) urgency assessment into URGs; 2) correspondent assignment of a pre-set MTBT; 3) real time prioritization of every referral on the list, according to urgency and waiting time. In the second project phase a prospective descriptive study was performed, when a single general surgery unit was selected as the deployment and test bed, managing all registrations from March 2004 to March 2007 (1809 ordinary and 597 day cases). From August 2005, once the SWALIS model had been modified, waiting lists were monitored and analyzed, measuring the impact of the model by a set of performance indexes (average waiting time, length of the waiting list) and Appropriate Performance Index (API).</p> <p>Results</p> <p>The SWALIS pre-admission model was used for all registrations in the test period, fully covering the case mix of the patients referred to surgery. The software produced real time data and advanced parameters, providing patients and users useful tools to manage waiting lists and to schedule hospital admissions with ease and efficiency. The model protected patients from horizontal and vertical inequities, while positive changes in API were observed in the latest period, meaning that more patients were treated within their MTBT.</p> <p>Conclusion</p> <p>The SWALIS model achieves the purpose of providing useful data to monitor waiting lists appropriately. It allows homogeneous and standardized prioritization, enhancing transparency, efficiency and equity. Due to its applicability, it might represent a pragmatic approach towards surgical waiting lists, useful in both clinical practice and strategic resource management.</p

Balancing equity and efficiency in the Dutch basic benefits package using the principle of proportional shortfall

Economic evaluations are increasingly used to inform decisions regarding the allocation of scarce health care resources. To systematically incorporate societal preferences into these evaluations, quality-adjusted life year gains could be weighted according to some equity principle, the most suitable of which is a matter of frequent debate. While many countries still struggle with equity concerns for priority setting in health care, the Netherlands has reached a broad consensus to use the concept of proportional shortfall. Our study evaluates the concept and its support in the Dutch health care context. We discuss arguments in the Netherlands for using proportional shortfall and difficulties in transitioning from principle to practice. In doing so, we address universal issues leading to a systematic consideration of equity concerns for priority setting in health care. The article thus has relevance to all countries struggling with the formalization of equity concerns for priority setting

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenario