Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.     

In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.     

Caution! Overestimation of treatment effects of corticosteroid therapy for community-acquired pneumonia in a meta-analysis of randomized controlled trials

It is well known that a meta-analysis of randomized controlled trials aims to increase the power and precision of the estimated intervention effects. However, when a meta-analysis includes a limited number of patients and a small number of events, overestimation of intervention effect estimates may occur and could cause spurious results. Although many biases can cause the overestimation, random error may be the most common cause. Trial sequential analysis (TSA) can explore the independent effect of random error on intervention effect estimates in meta-analyses and protect meta-analyses against overestimation due to random error.     

白内障超声乳化术后角膜地形图和屈光状态的观察

目的　观察白内障超声乳化人工晶状体植入术后 ,患者角膜地形图和屈光状态的变化。方法　对2 0 0 0年～ 2 0 0 1年行白内障超声乳化的 136例 (15 1只眼 )患者术前、术后随访一年的资料进行回顾性分析。结果　术后 1、3、6、12月术眼的球镜、散光、角膜地形图曲率和散光 sim k值统计结果无明显差异 ;术后 1～ 12月视力和屈光状态稳定 (方差分析 P >0 .0 5 )。术后 6月平均视力为 0 .5 9± 0 .2 3,矫正视力为 0 .82± 0 .19,术后 6月 ,球镜为 (-0 .2 4± 0 .88) D,散光为 (- 0 .5 6± 1.0 1) D,在± 1.0 D之内球镜和散光眼分别为 84.0 %和 6 4.0 % ,≥ 0 .5和≥ 0 .8的视力分别为 70 .0 %和 2 8.0 % ,≥ 0 .5和≥ 0 .8的矫正视力分别为 94.0 %和 72 .0 %。≥ 0 .5和≥ 0 .8裸眼视力和矫正视力的平均百分率分别由 6 1.96 %和 2 3.49%提高到配镜矫正后的 92 .35 %和 6 3.0 9%。结论　超声乳化术后角膜形态和屈光状态保持良好的稳定性 ,患者可以达到良好的视力。由于植入的人工晶状体无调节功能和测量误差 ,目前部分患者需要配戴较低度数的眼镜矫正远或近视力 ,达到更好的视觉效果。     

ITERATIVE DYNAMIC PROGRAMMING FOR MINIMUM ENERGY CONTROL PROBLEMS WITH TIME DELAY

This paper presents the use of iterative dynamic programming employing exact penalty functions for minimum energy control problems. We show that exact continuously non-differentiable penalty functions are superior to continuously differentiable penalty functions in terms of satisfying final state constraints. We also demonstrate that the choice of an appropriate penalty function factor depends on the relative size of the time delay with respect to the final time and on the expected value of the energy consumption. A quadratic approximation (QA) of the delayed variables is much better than a linear approximation (LA) of the same for relatively large time delays. The QA improves the rate of convergence and avoids the formation of ‘kinks‘. A more general way of selecting appropriate penalty function factors is given and the results obtained using four illustrative examples of varying complexity corroborate the efficacy of the method.