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Comorbidity is pervasive among both adult and child psychiatric disorders; however, the etiological mechanisms underlying the majority of comorbidities are unknown. This study used genetic linkage analysis to assess the etiology of comorbidity between reading disability (RD) and attention-deficit hyperactivity disorder (ADHD), two common childhood disorders that frequently co-occur. Sibling pairs (N = 85) were ascertained initially because at least one individual in each pair exhibited a history of reading difficulties. Univariate linkage analyses in sibling pairs selected for ADHD from within this RD-ascertained sample suggested that a quantitative trait locus (QTL) on chromosome 6p is a susceptibility locus for ADHD. Because this QTL is in the same region as a well-replicated QTL for reading disability, subsequent bivariate analyses were conducted to test if this QTL contributed to comorbidity between the two disorders. Analyses of data from sib pairs selected for reading deficits revealed suggestive bivariate linkage for ADHD and three measures of reading difficulty, indicating that comorbidity between RD and ADHD may be due at least in part to pleiotropic effects of a QTL on chromosome 6p.
Comorbidity is pervasive among both adult and child psychiatric disorders; however, the etiological mechanisms underlying the majority of comorbidities are unknown. This study used genetic linkage analysis to assess the etiology of comorbidity between reading disability (RD) and attention-deficit hyperactivity disorder (ADHD), two common childhood disorders that frequently co-occur. Sibling pairs (N = 85) were ascertained initially because at least one individual in each pair exhibited a history of reading difficulties. Univariate linkage analyses in sibling pairs selected for ADHD from within this RD-ascertained sample suggested that a quantitative trait locus (QTL) on chromosome 6p is a susceptibility locus for ADHD. Because this QTL is in the same region as a well-replicated QTL for reading disability, subsequent bivariate analyses were conducted to test if this QTL contributed to comorbidity between the two disorders. Analyses of data from sib pairs selected for reading deficits revealed suggestive bivariate linkage for ADHD and three measures of reading difficulty, indicating that comorbidity between RD and ADHD may be due at least in part to pleiotropic effects of a QTL on chromosome 6p.
Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for members of families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). We describe an ACE model for binary family data; this structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. We then introduce our contribution, a likelihood-based approach to fitting the model to singly-ascertained case-control family data. The approach, which involves conditioning on the proband’s disease status and also setting prevalence equal to a pre-specified value that can be estimated from the data, makes it possible to obtain valid estimates of the A, C, and E variance components from case-control (rather than only from population-based) family data. In fact, simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly-made assumptions hold. Further, when our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.
In human genetics, twin studies are widely underwent for investigation of the genetic influence on diseases. There are several measures that had been proposed to evaluate the similarity between two twins for dichotomous traits when the twins are sampled at random. These measures include the correlations, odds ratios, and casewise and pairwise concordances. When data are sampled through an ascertainment procedure, truncated data are formed. Under this circumstance, odds ratio cannot be defined and correlations cannot be correctly estimated. However, concordance measures for dichotomous traits can still be estimated using the likelihood method. On the other hand, though theoretically concordance measures can be extended to trichotomous traits, how to define them and to derive their estimators for ascertained trichotomous traits data have not been thoroughly discussed. In this study, we aim to address several relevant issues for ascertained trichotomous traits data. We define two new (casewise and pairwise) concordance measures for trichotomous traits and demonstrate how to apply a so-called 'self-contained subsets method (SCSM)' to estimation of twin concordances for ascertained data. We show that this method can obtain the same estimates as the likelihood method in an easier way and derive the asymptotic variances of the SCSM estimates under ascertainment. We establish the testing procedure for test of the equality of concordance measures between monozygotic twin pairs and dizygotic twin pairs, and illustrate the methods with a real data set and conduct Monte Carlo simulation to investigate its power performance.
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