Problems in genetic counseling in a family with an "atypical" centronuclear myopathy

Genetic counseling often deals with a rare disease the inheritance of which is not clearly established or in which genetic heterogeneity is reported. In addition, relevant parameters such as penetrance, gene frequency, and mutation rate may not be available. In this situation, establishing the risk may be very difficult. An example is presented in which Bayesian risk calculation proved to be of great help in providing precise risk estimates in a family in which an "atypical" centronuclear myopathy was segregating.     

Problems in genetic counseling in a family with an “atypical” centronuclear myopathy

Genetic counseling often deals with a rare disease the inheritance of which is not clearly established or in which genetic heterogeneity is reported. In addition, relevant parameters such as penetrance, gene frequency, and mutation rate may not be available. In this situation, establishing the risk may be very difficult. An example is presented in which Bayesian risk calculation proved to be of great help in providing precise risk estimates in a family in which an “atypical” centronuclear myopathy was segregating.     

Incorporating Hardy–Weinberg Equilibrium Law to Enhance the Association Strength for Ordinal Trait Genetic Study

The conventional method to examine whether genetic variants are associated with the ordinal traits is the proportional odds model. Such analyses are often conducted by assuming an additive genetic mode of inheritance. However, how the genetic variants influence the risk of occurrence of a disease is impossible to know in practice. Using an improper model might result in a low‐power test, thus it reduces the probability of detecting the deleterious genetic markers. To address these concerns, we propose a two‐phase procedure (TPP) for ordinal trait genetic studies. In the first phase, we used a linear combination to weight the Hardy–Weinberg equilibrium tests in case groups and formed an omnibus test to classify the genetic models. Then based on the chosen model, the corresponding score test was used to identify the associations. To control the false positive rate, we derived the joint distribution of the test used for selecting the genetic model and that used for identifying the associations. We also obtained the closed forms of two other robust tests, MAX3 and CHI2. Extensive computer simulations were carried out and the results showed that the true selection rates of genetic models are satisfactory and the proposed TPP is more robust than MAX3 and CHI2. Finally, we demonstrated the advantage of our proposed method by applying it to analyse the antibody reactivity to cyclic citrullinated peptides data.     

利用Bayes公式计算单基因遗传病再发风险的教学策略

目的讨论单基因遗传病再发风险的Bayes分析方法的教学技巧。方法通过系统讲述单基因遗传病的发病风险估计,引入在夫妻双方或一方基因型不确定的情况下,通过Bayes公式精确计算各种单基因遗传病再发风险。结果利用Bayes公式精确计算遗传病再发风险,可以为患者家庭提供可靠的遗传咨询信息及正确的婚姻、生育指导,作为是医学遗传学教学的难点和重点,利用Bayes公式精确计算遗传病再发风险的教学需要必要的教学技巧,其中讲述条件概率的计算是教学成功与否的关键。结论利用Bayes公式精确计算遗传病再发风险比单纯利用孟德尔分离定律更能准确地计算遗传病的再发风险,在教学过程中,通过各种手段强化教学内容是教学成功的关键。     

Familial recurrence rates and genetic models of multiple sclerosis

Susceptibility to multiple sclerosis (MS) is determined by both inherited and non-inherited factors. The importance of genetic factors is demonstrated by the increased risk of disease in relatives of MS patients. Our objective was to determine the implications of the observed familial recurrence risks for the genetic basis of MS. We developed a computer program which calculates recurrence risks for monozygotic (MZ) twins, siblings, and second degree relatives, and used it to calculate recurrence risks for a wide variety of genetic models. We investigated models with different numbers of genes, different patterns of interaction between the genes, and dominant or recessive inheritance. The models that best reproduced the observed values had multiple loci with strongly synergistic interaction and autosomal dominant (AD) inheritance. At least six loci were required, and we found no upper limit on the number of loci. Models with genetic heterogeneity, where only a fraction of the risk loci are required for disease, are possible. In models with large numbers of loci the "abnormal" alleles conferring risk of disease are the most common allele. We conclude that a variety of genetic models with multiple genes, dominant inheritance, and synergistic interaction between risk genes are consistent with the observed familial recurrence rates in MS.     

Risk analysis of a patient monitoring system using Bayesian Network modeling

In a modern technological environment where information systems are characterized by complexity, situations of non-effective operation should be anticipated. Often system failures are a result of insufficient planning or equipment malfunction, indicating that it is essential to develop techniques for predicting and addressing a system failure. Particularly for safety–critical applications such as the healthcare information systems, which are dealing with human health, risk analysis should be considered a necessity. This paper presents a new method for performing a risk analysis study of health information systems. Specifically, the CCTA Risk Analysis and Management Methodology (CRAMM) has been utilized for identifying and valuating the assets, threats, and vulnerabilities of the information system, followed by a graphical modeling of their interrelationships using Bayesian Networks. The proposed method exploits the results of the CRAMM-based risk analysis for developing a Bayesian Network model, which presents concisely all the interactions of the undesirable events for the system. Based on “what–if” studies of system operation, the Bayesian Network model identifies and prioritizes the most critical events. The proposed risk analysis framework has been applied to a vital signs monitoring information system for homecare telemedicine, namely the VITAL-Home System, developed and maintained for a private medical center (Medical Diagnosis and Treatment S.A.).     

Mapping Multiple Quantitative Trait Loci for Ordinal Traits

Many complex traits in humans and other organisms show ordinal phenotypic variation but do not follow a simple Mendelian pattern of inheritance. These ordinal traits are presumably determined by many factors, including genetic and environmental components. Several statistical approaches to mapping quantitative trait loci (QTL) for such traits have been developed based on a single-QTL model. However, statistical methods for mapping multiple QTL are not well studied as continuous traits. In this paper, we propose a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm to map multiple QTL for ordinal traits in experimental crosses. We model the ordinal traits under the multiple threshold model, which assumes a latent continuous variable underlying the ordinal phenotypes. The ordinal phenotype and the latent continuous variable are linked through some fixed but unknown thresholds. We adopt a standardized threshold model, which has several attractive features. An efficient sampling scheme is developed to jointly generate the threshold values and the values of latent variable. With the simulated latent variable, the posterior distributions of other unknowns, for example, the number, locations, genetic effects, and genotypes of QTL, can be computed using existing algorithms for normally distributed traits. To this end, we provide a unified approach to mapping multiple QTL for continuous, binary, and ordinal traits. Utility and flexibility of the method are demonstrated using simulated data.     

Discontinuity and quasi-continuity: Alternative hypotheses of multifactorial inheritance

Consequences of three models of multifactorial inheritance are deduced for inbreeding effects and recurrence risks, and methods are developed to test goodness of fit. Applying the computer program DISQUAC to several bodies of data, we find that every one which fits a quasi-continuous modcl also fits a discontinuous one.
Our results show that it is exceedingly difficult, and may be practically impossible, to infer the genetic basis of traits which do not give regular mendelian ratios. The only consolation is that predicted recurrence risks are esscntially the same for similar models. Thus the two quasi-continuous models of Falconer and Edwards, which assume cumulative gene action, seem in practice indistinguishable, while the genetic load model can be differentiated only if there is significant dominancc. Useful predictions for genetic counseling may be made from a well-fitting model even when the mode of inheritance is unclear. In particular, it is shown how to estimate the probability of affection in the next child after s sibs have been born, of whom r were affected, when there is no birth order effect.     

寻常型银屑病遗传模式分析

目的 探讨寻常型银屑病可能的遗传模式。方法 应用Penrose法、Falconer回归法及SAGE－REGTL软件对1043例银屑病患者及家系进行复合分离分析和遗传度计算，以探讨银屑病的可能遗传模式。结果 1043例寻常型银屑病患者中，有家族史者305例（29.24%)，无家族史者738例（70.76%)；Penrose法计算，s/q接近1／q；一级、二级亲属的遗传度分别是0.670和0.466；通过复合分离分析不符合单基因显性、隐性、共显性遗传模式及环境模式和非主基因模式。结论 遗传分析提示寻常型银屑病不符合单基因遗传，而属于多基因遗传模式或多因子遗传模式。     

A transmission disequilibrium test for general pedigrees that is robust to the presence of random genotyping errors and any number of untyped parents

Two issues regarding the robustness of the original transmission disequilibrium test (TDT) developed by Spielman et al are: (i) missing parental genotype data and (ii) the presence of undetected genotype errors. While extensions of the TDT that are robust to items (i) and (ii) have been developed, there is to date no single TDT statistic that is robust to both for general pedigrees. We present here a likelihood method, the TDT(ae), which is robust to these issues in general pedigrees. The TDT(ae) assumes a more general disease model than the traditional TDT, which assumes a multiplicative inheritance model for genotypic relative risk. Our model is based on Weinberg's work. To assess robustness, we perform simulations. Also, we apply our method to two data sets from actual diseases: psoriasis and sitosterolemia. Maximization under alternative and null hypotheses is performed using Powell's method. Results of our simulations indicate that our method maintains correct type I error rates at the 1, 5, and 10% levels of significance. Furthermore, a Kolmorogov-Smirnoff Goodness of Fit test suggests that the data are drawn from a central chi2 with 2 df, the correct asymptotic null distribution. The psoriasis results suggest two loci as being significantly linked to the disease, even in the presence of genotyping errors and missing data, and the sitosterolemia results show a P-value of 1.5 x 10(-9) for the marker locus nearest to the sitosterolemia disease genes. We have developed software to perform TDT(ae) calculations, which may be accessed from our ftp site.     

Bayesian analysis for cystic fibrosis risks in prenatal and carrier screening.

PURPOSE: Risk assessment is an essential component of genetic counseling and testing, and Bayesian analysis plays a central role in complex risk calculations. We previously developed generalizable Bayesian methods to calculate the autosomal recessive disease risk of a fetus when one or no mutation is detected, and another, independent risk factor is present. Our methods are particularly useful for calculating the CF disease risk for a fetus with echogenic bowel. In genetics practice, however, there are other scenarios for which our previous methods are inadequate. METHODS AND RESULTS: We provide herein methods for calculating genetic risks in a variety of common clinical scenarios. These scenarios include the following: (1) different mutation panels that have been used for the parents and for a fetus; (2) genetic testing results available on the proband or other relatives, in addition to the consultand; (3) fetal ultrasound negative for echogenic bowel with a positive family history; and (4) a consultand with a mixed ethnic background. CONCLUSION: Our Bayesian methods have proven their versatility through application to many different common genetic counseling scenarios. These methods permit autosomal recessive disease and carrier probabilities to be calculated accurately, taking into account all relevant information. Our methods allow accurate genetic risk estimates for patients and their family members for CF or other autosomal recessive disorders.     

RISCALW: A Windows Program for Risk Calculation in Families with Duchenne Muscular Dystrophy

RISCALW is a novel convenient Windows program for risk calculation in families with Duchenne muscular dystrophy. It is based on an extended genetic model which includes germline mosaicism and different new mutation rates depending on sex and mutation type. Arbitrary family structures and additional diagnostic information such as genotypes from intragenic and flanking genetic markers of the dystrophin gene, creatin kinase values, and female deletion test results can be taken into account. Comprehensive online help is available. RISCALW has been extensively tested in more than 100 families. The user is able to perform risk calculations in large families in an easy way, and thus explore the dependence of the results on different model assumptions, for example different mutation rates in males and females. A detailed handbook and example files are distributed with the program. RISCALW is freely available for scientific use at http://www.riscalw.uni‐hd.de .     

Tests for Candidate-Gene Association Using Case-Parents Design

In the case‐parents design for testing candidate‐gene association, the conditional likelihood method based on genotype relative risks has been developed recently. A specific relation of the genotype relative risks is referred to as a genetic model. The efficient score tests have been used when the genetic model is correctly specified under the alternative hypothesis. In practice, however, it is usually not able to specify the genetic model correctly. In the latter situation, tests such as the likelihood ratio test (LRT) and the MAX3 (the maximum of the three score statistics for dominant, additive, and recessive models) have been used. In this paper, we consider the restricted likelihood ratio test (RLRT). For a specific genetic model, simulation results demonstrate that RLRT is asymptotically equivalent to the score test, and both are more powerful than the LRT. When the genetic model cannot be correctly specified, the simulation results show that RLRT is most robust and powerful in the situations we studied. MAX3 is the next most robust and powerful test. The TDT is the easiest statistic to compute, compared to MAX3 and RLRT. When the recessive model can be eliminated, it is also as robust and powerful as RLRT for other genetic models.     

Fitting Genetic Models to Twin Data with Binary and Ordered Categorical Responses: A Comparison of Structural Equation Modelling and Bayesian Hierarchical Models

We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a correct model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.     

人类与医学遗传学群体与家系资料分析计算机系统的功能与菜单结构（Ⅰ）

本文介绍我们用Ｃ语言编制的人类与医学遗传学群体与家系资料分析计算机系统的菜单结构与各子系统的功能。该系统可用于基因频率与群体分化分析；相关与遗传度分析；疾病关联分析；遗传异质性分析；ＡＧＦＡＰ遗传方式分析。尚可用于通径分析；数量性状的混合分布分析；分离分析与连锁分析等。对人类与医学遗传学及遗传流行病学研究都很有实用价值。     

Risk management in pathology and laboratory medicine

An effective risk-management program focuses attention on and minimizes events that ideally should not occur. In the laboratory, risk-management issues can be divided along two major lines: (1) patient-care-related issues and (2) employee-related issues (safety). The Joint Commission on Accreditation of Healthcare Organizations, Chicago, Ill, has established formal requirements for risk-management programs for hospitals and clinical departments. These requirements include patient-care-related activities and management functions related to the safety-management functions. The concept of a formal risk-management program may be new to pathologists and laboratory professionals; the components of the program are traditional elements of laboratory policies and procedures. Key items identified as building blocks for risk management are these: (1) risk prevention, (2) risk identification, (3) risk assessment, (4) corrective actions, and (5) loss control. Through the systematic implementation of a comprehensive risk-management program, laboratories have the opportunity to reduce liability exposure and improve the quality of care and services.     

Power of Selective Genotyping in Genetic Association Analyses of Quantitative Traits

A power calculation is described in which the power of selective genotyping in genetic association studies of quantitative traits is evaluated. The method of selective genotyping implies the selection of the extremely high and low scoring individuals from the continuous distribution of a quantitative trait. The selected individuals are genotyped and association is tested. In the power calculation the following parameters are varied: total sample size of the phenotyped group (1000, 1500, 2000); selection of extremely high and low scoring individuals (2.5%, 5%, 10%); allele frequency of the risk increasing allele (0.10 to 0.90); mode of inheritance and proportion of variance explained by the quantitative trait locus = 0.01, 0.05, 0.10). We conclude that the method of selective genotyping is a powerful method to detect association for a quantitative trait.     

Analytic power calculation for QTL linkage analysis of small pedigrees

Power calculation for QTL linkage analysis can be performed via simple algebraic formulas for small pedigrees, but requires intensive computation for large pedigrees, in order to evaluate the expectation of the test statistic over all possible inheritance vectors at the test position. In this report, we show that the non-centrality parameter for an arbitrary pedigree can be approximated by the sum of the variances of the correlations between all pairs of relatives, each variance being weighted by a factor that is determined by the mean correlation of the pair. We show that this approximation is sufficiently accurate for practical purposes in small to moderately large pedigrees, and that large sibships are more efficient than other family structures under a range of genetic models.     

Computational Discrimination of Breast Cancer for Korean Women Based on Epidemiologic Data Only

Breast cancer is the second leading cancer for Korean women and its incidence rate has been increasing annually. If early diagnosis were implemented with epidemiologic data, the women could easily assess breast cancer risk using internet. National Cancer Institute in the United States has released a Web-based Breast Cancer Risk Assessment Tool based on Gail model. However, it is inapplicable directly to Korean women since breast cancer risk is dependent on race. Also, it shows low accuracy (58%-59%). In this study, breast cancer discrimination models for Korean women are developed using only epidemiological case-control data (n = 4,574). The models are configured by different classification techniques: support vector machine, artificial neural network, and Bayesian network. A 1,000-time repeated random sub-sampling validation is performed for diverse parameter conditions, respectively. The performance is evaluated and compared as an area under the receiver operating characteristic curve (AUC). According to age group and classification techniques, AUC, accuracy, sensitivity, specificity, and calculation time of all models were calculated and compared. Although the support vector machine took the longest calculation time, the highest classification performance has been achieved in the case of women older than 50 yr (AUC = 64%). The proposed model is dependent on demographic characteristics, reproductive factors, and lifestyle habits without using any clinical or genetic test. It is expected that the model could be implemented as a web-based discrimination tool for breast cancer. This tool can encourage potential breast cancer prone women to go the hospital for diagnostic tests.

Graphical Abstract

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The value of isolated populations in genetic studies of allergic diseases

Asthma and other IgE-mediated atopic disorders are extremely common in the population, with a consistent, but rather small, genetic component (lambda 3-5) in the inheritance of the disease. So far, genome-wide gene mapping studies in asthma-related traits have not achieved the same success as the positional cloning of genes for Mendelian diseases. Many disease predisposing alleles are believed to be involved in the development of these phenotypes, but at present we do not understand their genetic architecture. As a result of reduced allelic diversity and longer-range linkage disequilibrium compared with more mixed populations, isolated populations may offer a unique opportunity to model a simplified picture of these complex genetic events predisposing to common diseases, such as asthma, which demonstrate familial clustering, but do not follow a simple Mendelian mode of inheritance.(sib)