Effects of marker information on sib-pair linkage analysis of a rare disease

Model-free sib-pair linkage analysis was used to screen the GAW9 - Problem 1 data set for evidence of linkage of a rare disease to any of 360 highly polymorphic marker loci. Negative regressions nominally significant at the α = 0.05 level were obtained for 44 markers; however all of these proved to be Type I errors. None of the four disease loci were detected by sib-pair linkage, which was not surprising, given the particular model and sampling scheme used to generate these data. Neither deleting parental marker genotypic information nor misspecifying marker allele frequency estimates substantially increased the Type I error rate. A two-stage testing procedure using a 10 or 20 cM map and a liberal first stage significance level gave the same overall results as a one-stage 2 cM map but required only about 42% or 22% as many markers, respectively. ©1995 Wiley-Liss, Inc.     

Misunderstandings about Q and ‘Cochran's Q test' in meta‐analysis

Many meta‐analyses report using ‘Cochran's Q test' to assess heterogeneity of effect‐size estimates from the individual studies. Some authors cite work by W. G. Cochran, without realizing that Cochran deliberately did not use Q itself to test for heterogeneity. Further, when heterogeneity is absent, the actual null distribution of Q is not the chi‐squared distribution assumed for ‘Cochran's Q test'. This paper reviews work by Cochran related to Q. It then discusses derivations of the asymptotic approximation for the null distribution of Q, as well as work that has derived finite‐sample moments and corresponding approximations for the cases of specific measures of effect size. Those results complicate implementation and interpretation of the popular heterogeneity index I². Also, it turns out that the test‐based confidence intervals used with I² are based on a fallacious approach. Software that outputs Q and I² should use the appropriate reference value of Q for the particular measure of effect size and the current meta‐analysis. Q is a key element of the popular DerSimonian–Laird procedure for random‐effects meta‐analysis, but the assumptions of that procedure and related procedures do not reflect the actual behavior of Q and may introduce bias. The DerSimonian–Laird procedure should be regarded as unreliable. Copyright © 2015 John Wiley & Sons, Ltd.     

基于小波变换的多分辨率CT/MRI图像配准

本文以仿射模型为基础将区域配准问题转化成优化问题,然后给出小波金字塔分解用于配准的一般框架,并与常规金字塔分解法进行了分析比较;针对配准过程的多变量非线性优化问题,结合混沌理论和遗传算法提出了改进的全局优化的混沌遗传算法。这种算法将混沌序列嵌入标准遗传算法,对标准遗传算法的个体进行混沌化处理,利用混沌的遍历特性来改进遗传算法的鲁棒性。     

A hierarchical Poisson mixture regression model to analyse maternity length of hospital stay

Inpatient length of stay (LOS) is often considered as a proxy of hospital resource consumption. Using statewide obstetrical delivery data, a two-component Poisson mixture model provides a reasonable fit to the heterogeneous LOS distribution. Adopting the generalized linear mixed model (GLMM) approach, random effects are introduced to the two-component Poisson mixture regression model to account for the inherent correlation of patients clustered within hospitals. An EM algorithm is developed for the joint estimation of regression coefficients and variance component parameters. Related diagnostic measures for assessing model adequacy are derived. When applying the method to analyse maternity LOS, appropriate risk factors for the short-stay and long-stay subgroups can be identified from the respective Poisson components. In addition, predicted random hospital effects enable the comparison of relative efficiencies among hospitals after adjustment for patient case-mix and health provision characteristics.     

几种图像分割技术在医学图像处理中的应用研究

叙述了基于特定理论的图像分割算法在医学图像处理中的应用,详细介绍了遗传算法、水平集分割法、模糊集分割法各自的基本理论以及优缺点,论述与讨论了医学图像分割效果评价方法,最后对各种算法的特点进行了总结。     

Ignoring temporal trends in genetic effects substantially reduces power of quantitative trait linkage analysis

Linkage analysis has been one of the most widely used methods for identifying regions of the human genome which contain genes responsible for human diseases. Evidence suggests that the effects of some of the trait causing genes may vary with the age of an individual, giving rise to temporal trends in genetic effects. Linkage analysis routinely tends to ignore such gene-by-age interactions. While linkage analysis methods have been proposed for analysis of longitudinal family data for exploring temporal trends, there are no models to characterize such trends nor methods for analysis of cross-sectional family data. We extend variance component linkage analysis methodology by modeling the variance components due to the quantitative trait locus (QTL) and that due to the polygenic effect to be age dependent. With this model, we investigate the power of linkage analysis in the presence of temporal trends. We show that modeling true temporal trends in QTL effects can substantially increase the power of linkage analysis even when the average locus-specific heritabilities (when trends are ignored) are quite low, thereby demonstrating that, ignoring the gene-by-age interactions, when present, could jeopardize gene discovery.     

基于MATLAB的基因芯片图像预处理

目的：基于MATLAB设计低密度基因芯片图像预处理系统，对经过Scan Array扫描系统获得的用cy3和cy5染色的低密度芯片的荧光图像进行处理，以滤除噪声。增强图像的对比度，提高图像的质量，实现图像的边缘检测与分割，进行区域的识别。方法：采用小波中值滤波方法，滤除噪声，提高图像的质量；基于小波方法，结合边缘算子进行图像边缘检测；使用遗传算法实现图像的分割。结果：该系统对基因芯片图像进行处理，减少了污点、噪声及其他各种因素的影响，提高了图像的质量，可以更好地检测出图像的边缘，比较准确地分割出样点区域，能有效地分离有价值的弱信号点和背景点或者噪声。结论：该系统可以基本实现低密度基因芯片图像预处理功能．采用的图像处理方法是可行的，能为以后的分析提供较为准确的数据信息。     

Meta-analysis of sex-specific genome-wide association studies

Despite the success of genome-wide association studies, much of the genetic contribution to complex human traits is still unexplained. One potential source of genetic variation that may contribute to this "missing heritability" is that which differs in magnitude and/or direction between males and females, which could result from sexual dimorphism in gene expression. Such sex-differentiated effects are common in model organisms, and are becoming increasingly evident in human complex traits through large-scale male- and female-specific meta-analyses. In this article, we review the methodology for meta-analysis of sex-specific genome-wide association studies, and propose a sex-differentiated test of association with quantitative or dichotomous traits, which allows for heterogeneity of allelic effects between males and females. We perform detailed simulations to compare the power of the proposed sex-differentiated meta-analysis with the more traditional "sex-combined" approach, which is ambivalent to gender. The results of this study highlight only a small loss in power for the sex-differentiated meta-analysis when the allelic effects of the causal variant are the same in males and females. However, over a range of models of heterogeneity in allelic effects between genders, our sex-differentiated meta-analysis strategy offers substantial gains in power, and thus has the potential to discover novel loci contributing effects to complex human traits with existing genome-wide association data.     

A robust and powerful two‐step testing procedure for local ancestry adjusted allelic association analysis in admixed populations

Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two‐step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry‐specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups’ previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA .     

Latent variable models with nonparametric interaction effects of latent variables

Renal disease is one of the common complications of diabetes, especially for Asian populations. Moreover, cardiovascular and renal diseases share common risk factors. This paper proposes a latent variable model with nonparametric interaction effects of latent variables for a study based on the Hong Kong Diabetes Registry, which was established in 1995 as part of a continuous quality improvement program at the Prince of Wales Hospital in Hong Kong. Renal outcome (outcome latent variable) is regressed in terms of cardiac function and diabetes (explanatory latent variables) through an additive structural equation formulated using a series of unspecified univariate and bivariate smooth functions. The Bayesian P‐splines approach, along with a Markov chain Monte Carlo algorithm, is proposed to estimate smooth functions, unknown parameters, and latent variables in the model. The performance of the developed methodology is demonstrated via a simulation study. The effect of the nonparametric interaction of cardiac function and diabetes on renal outcome is investigated using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.     

A comparison of analytical methods for genetic association studies

The explosion of genetic information over the last decade presents an analytical challenge for genetic association studies. As the number of genetic variables examined per individual increases, both variable selection and statistical modeling tasks must be performed during analysis. While these tasks could be performed separately, coupling them is necessary to select meaningful variables that effectively model the data. This challenge is heightened due to the complex nature of the phenotypes under study and the complex underlying genetic etiologies. To address this problem, a number of novel methods have been developed. In the current study, we compare the performance of six analytical approaches to detect both main effects and gene-gene interactions in a range of genetic models. Multifactor dimensionality reduction, grammatical evolution neural networks, random forests, focused interaction testing framework, step-wise logistic regression, and explicit logistic regression were compared. As one might expect, the relative success of each method is context dependent. This study demonstrates the strengths and weaknesses of each method and illustrates the importance of continued methods development.     

Multipoint linkage disequilibrium mapping for complex diseases

Linkage disequilibrium (LD) or association studies using case-parent trios have become a common approach to locate unobserved susceptibility genes underlying complex diseases. With the availability of ever more dense marker maps, how to utilize the information carried by multiple markers simultaneously remains challenging. Recently, Liang et al. ([2001a] Am. J. Hum. Genet. 68: 937-950) proposed a multipoint LD method to estimate the location of a susceptibility gene within a framework map along with its sampling uncertainty. Two important features of this method are that 1) it uses all trios whether parents are heterozygous for a given marker or not, and 2) it provides a single test statistic for the null hypothesis of no linkage or no LD to the region, avoiding the multiple testing problem encountered when performing individual transmission disequilibrium tests (TDT) for each marker individually. In this paper, we discuss how this method can be expanded to address important issues pertaining to complex diseases in a unified fashion. These issues include, among others, gene-gene and gene-environment interactions, genetic heterogeneity, phenotypic refinement, and paternal vs. maternal transmission. We applied this method to asthmatic case-parent trios from the Collaborative Study on the Genetics of Asthma (CSGA), and found that the previous evidence for linkage and LD in a 13.6 cM region of chromosome 11 can be attributed to maternal transmission, while there was no evidence of excess paternal transmission. Furthermore, such discrepancy in preferential transmission was most evident among probands with early onset age (6 years old or younger).     

The Effects of Violating Standard Item Writing Principles on Tests and Students: The Consequences of Using Flawed Test Items on Achievement Examinations in Medical Education

The purpose of this research was to study the effects of violations of standard multiple-choice item writing principles on test characteristics, student scores, and pass–fail outcomes. Four basic science examinations, administered to year-one and year-two medical students, were randomly selected for study. Test items were classified as either standard or flawed by three independent raters, blinded to all item performance data. Flawed test questions violated one or more standard principles of effective item writing. Thirty-six to sixty-five percent of the items on the four tests were flawed. Flawed items were 0–15 percentage points more difficult than standard items measuring the same construct. Over all four examinations, 646 (53%) students passed the standard items while 575 (47%) passed the flawed items. The median passing rate difference between flawed and standard items was 3.5 percentage points, but ranged from −1 to 35 percentage points. Item flaws had little effect on test score reliability or other psychometric quality indices. Results showed that flawed multiple-choice test items, which violate well established and evidence-based principles of effective item writing, disadvantage some medical students. Item flaws introduce the systematic error of construct-irrelevant variance to assessments, thereby reducing the validity evidence for examinations and penalizing some examinees.