Fitting ACE structural equation models to case-control family data

Differences in the heritability of craniofacial skeletal and dental characteristics between twin pairs with skeletal Class I and II malocclusions

Objective

To investigate differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and Class II malocclusions.

Methods

Forty Korean adult twin pairs were divided into Class I (C-I) group (0° ≤ angle between point A, nasion, and point B [ANB]) ≤ 4°; mean age, 40.7 years) and Class II (C-II) group (ANB > 4°; mean age, 43.0 years). Each group comprised 14 monozygotic and 6 dizygotic twin pairs. Thirty-three cephalometric variables were measured using lateral cephalograms and were categorized as the anteroposterior, vertical, dental, mandible, and cranial base characteristics. The ACE model was used to calculate heritability (A > 0.7, high heritability). Thereafter, principal component analysis (PCA) was performed.

Results

Twin pairs in C-I group exhibited high heritability values in the facial anteroposterior characteristics, inclination of the maxillary and mandibular incisors, mandibular body length, and cranial base angles. Twin pairs in C-II group showed high heritability values in vertical facial height, ramus height, effective mandibular length, and cranial base length. PCA extracted eight components with 88.3% in the C-I group and seven components with 91.0% cumulative explanation in the C-II group.

Conclusions

Differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and II malocclusions might provide valuable information for growth prediction and treatment planning.

strum: an R package for structural modeling of latent variables for general pedigrees

BACKGROUND

Structural equation modeling (SEM) is an extremely general and powerful approach to account for measurement error and causal pathways when analyzing data, and it has been used in wide range of applied sciences. There are many commercial and freely available software packages for SEM. However, it is difficult to use any of the packages to analyze general pedigree data, and SEM packages for genetics are limited in their application.

RESULTS

We present the new R package strum to serve the need of a suitable SEM software tool for genetic analysis. It implements a general framework for SEM within the context of general pedigree data. This context requires specialized considerations such as familial correlations and ascertainment. Our package is an extraordinarily flexible tool capable of modeling genetic association, linkage analysis, polygenic effects, shared environment, and ascertainment combined with confirmatory factor analysis and general SEM. It also provides a convenient tool for model visualization, and integrates tools for simulating pedigree data. The various features of this package are tested through a simulation study to evaluate performance, and our results show that strum is very reliable and robust in terms of the accuracy and coverage of parameter estimates.

CONCLUSIONS

strum is a valuable new tool for genetic analysis. It can be easily used with general pedigree data, incorporating both measurement and structural models, giving it some significant advantages over other software packages. It also includes a built-in approach for handling ascertainment, a helpful integrated tool for genetic data simulation, and built-in tools for model visualization, providing a significant addition to biomedical research.

Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures

Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene-environment interaction (G × E). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for G × E effects within the LV framework to examine effect modification of outcome-exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data-adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data-adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time-varying gene-environment association.

The contribution of familial internalizing and externalizing liability factors to borderline personality disorder

BACKGROUND

Individuals with borderline personality disorder (BPD) frequently display co-morbid mental disorders. These disorders include 'internalizing' disorders (such as major depressive disorder and anxiety disorders) and 'externalizing' disorders (such as substance use disorders and antisocial personality disorder). It is hypothesized that these disorders may arise from latent 'internalizing' and 'externalizing' liability factors. Factor analytic studies suggest that internalizing and externalizing factors both contribute to BPD, but the extent to which such contributions are familial is unknown.

METHOD

Participants were 368 probands (132 with BPD; 134 without BPD; and 102 with major depressive disorder) and 885 siblings and parents of probands. Participants were administered the Diagnostic Interview for DSM-IV Personality Disorders, the Revised Diagnostic Interview for Borderlines, and the Structured Clinical Interview for DSM-IV.

RESULTS

On confirmatory factor analysis of within-person associations of disorders, BPD loaded moderately on internalizing (factor loading 0.53, S.E. = 0.10, p < 0.001) and externalizing latent variables (0.48, S.E. = 0.10, p < 0.001). Within-family associations were assessed using structural equation models of familial and non-familial factors for BPD, internalizing disorders, and externalizing disorders. In a Cholesky decomposition model, 84% (S.E. = 17%, p < 0.001) of the association of BPD with internalizing and externalizing factors was accounted for by familial contributions.

CONCLUSIONS

Familial internalizing and externalizing liability factors are both associated with, and therefore may mutually contribute to, BPD. These familial contributions account largely for the pattern of co-morbidity between BPD and internalizing and externalizing disorders.

A latent variable approach to study gene-environment interactions in the presence of multiple correlated exposures

Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G × E) effects. In environmental epidemiology, interest in G × E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G × E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G × E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G × E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron metabolism genes, and birth weight.

Family study of borderline personality disorder and its sectors of psychopathology

CONTEXT

The familiality of borderline personality disorder (BPD) and its sectors of psychopathology are incompletely understood.

OBJECTIVES

To assess the familial aggregation of BPD and its 4 major sectors (affective, interpersonal, behavioral, and cognitive) and test whether the relationship of the familial and nonfamilial associations among these sectors can be accounted for by a latent BPD construct.

DESIGN

Family study, with direct interviews of probands and relatives.

SETTING

A psychiatric hospital (McLean Hospital) and the Boston-area community.

PARTICIPANTS

A total of 368 probands (132 with BPD, 134 without BPD, and 102 with major depressive disorder) and 885 siblings and parents of probands. MAIN ASSESSMENTS: The Diagnostic Interview for DSM-IV Personality Disorders and the Revised Diagnostic Interview for Borderlines (DIB-R) were used to assess borderline psychopathology, and the Structured Clinical Interview for DSM-IV was used to assess major depressive disorder.

RESULTS

Borderline personality disorder meeting both DSM-IV and DIB-R criteria showed substantial familial aggregation for BPD in individuals with a family member with BPD vs those without a family member with BPD, using proband-relative pairs (risk ratio, 2.9; 95% confidence interval, 1.5-5.5) as well as using all pairs of family members (3.9; 1.7-9.0). All 4 sectors of BPD psychopathology aggregated significantly in families, using both DSM-IV and DIB-R definitions (correlation of traits among all pairs of family members ranged from 0.07 to 0.27), with the affective and interpersonal sectors showing the highest levels; however, the level of familial aggregation of BPD was higher than that of the individual sectors. The relationship among the sectors was best explained by a common pathway model in which the sectors represent manifestations of a latent BPD construct.

CONCLUSIONS

Familial factors contribute to BPD and its sectors of psychopathology. Borderline personality disorder may arise from a unitary liability that finds expression in its sectors of psychopathology.

Estimating disease prevalence using relatives of case and control probands

We introduce a method of estimating disease prevalence from case-control family study data. Case-control family studies are performed to investigate the familial aggregation of disease; families are sampled via either a case or a control proband, and the resulting data contain information on disease status and covariates for the probands and their relatives. Here, we introduce estimators for overall prevalence and for covariate-stratum-specific (e.g., sex-specific) prevalence. These estimators combine the proportion of affected relatives of control probands with the proportion of affected relatives of case probands and are designed to yield approximately unbiased estimates of their population counterparts under certain commonly made assumptions. We also introduce corresponding confidence intervals designed to have good coverage properties even for small prevalences. Next, we describe simulation experiments where our estimators and intervals were applied to case-control family data sampled from fictional populations with various levels of familial aggregation. At all aggregation levels, the resulting estimates varied closely and symmetrically around their population counterparts, and the resulting intervals had good coverage properties, even for small sample sizes. Finally, we discuss the assumptions required for our estimators to be approximately unbiased, highlighting situations where an alternative estimator based only on relatives of control probands may perform better.