LISREL analysis of twin data with structured means

Notes on Three Decades of Methodology Workshops

Since 1987, a group of behavior geneticists have been teaching an annual methodology workshop on how to use state-of-the-art methods to analyze genetically informative data. In the early years, the focus was on analyzing twin and family data, using information of their known genetic relatedness to infer the role of genetic and environmental factors on phenotypic variation. With the rapid evolution of genotyping and sequencing technology and availability of measured genetic data, new methods to detect genetic variants associated with human traits were developed and became the focus of workshop teaching in alternate years. Over the years, many of the methodological advances in the field of statistical genetics have been direct outgrowths of the workshop, as evidence by the software and methodological publications authored by workshop faculty. We provide data and demographics of workshop attendees and evaluate the impact of the methodology workshops on scientific output in the field by evaluating the number of papers applying specific statistical genetic methodologies authored by individuals who have attended workshops.

Long-term effect of placental type on anthropometrical and psychological traits among monozygotic twins: a follow up study

The long-term effect of differences in placentation in MZ twins is a controversial subject. An effect has been clearly established for birth weight but data on psychological traits are still under debate. We studied 20 pairs of monochorionic MZ (MCMZ) and 24 pairs of dichorionic MZ (DCMZ) twins. A chorion effect was observed for Block Design (WISC-R) confirming a previous report: MC MZ co-twins were more similar that DC MZ co-twins. For anthropometrical measures, an expected effect in the opposite direction was found. No chorion effect was significant for the other variables. A follow up was undertaken 3 years later using cognitive, national academic evaluations, and personality variables. The sample included 16 pairs of MC MZ and 22 pairs of DC MZ twins. Again a chorion effect was observed on anthropometrical variables but results on the Block Design were not replicated. However, the MC MZ co-twins were more similar than the DC MZ co-twins for two other cognitive variables: Perceptual Organization Index from the WISC-R and Global Visualisation from a Belgian reasoning test. Among the personality variables only one was sensitive to a chorion effect. The discussion focuses on the need for larger samples to achieve adequate power in statistical comparisons.

Quantitative Association Tests of Immune Responses to Antigens ofMycobacterium Tuberculosis: A Study of Twins in West Africa

There is now considerable evidence that host genetic factors are important in determining the outcome of infection withMycobacterium tuberculosis(MTB). The aim of this study was to assess the role of several candidate genes in the variation observed in the immune responses to MTB antigens. In-vitro assays of T-cell proliferation, an in-vivo intradermal delayed hypersensitivity response; cytokine and antibody secretions to several mycobacterial peptide antigens were assessed in healthy, but exposed, West African twins. Candidate gene polymorphisms were typed in theNRAMP1,Vitamin D receptor,IL10,IL4,IL4 receptorandCTLA-4genes. Variants of the lociIL10(−1082 G/A),CTLA-4(49 A/G) and theIL4 receptor(128 A/G) showed significant associations with immune responses to several antigens. T-cell proliferative responses and antibody responses were reduced, TNF-α responses were increased for subjects with theCTLA-4G allele. The T-cell proliferative responses of subjects withIL10GA and GG genotypes differed significantly.IL4 receptorAG and GG genotypes also showed significant differences in their T-cell proliferative responses to MTB antigens. These results yield a greater understanding of the genetic mechanisms that underlie the immune responses in tuberculosis and have implications for the design of therapeutic interventions.

Decomposing group differences of latent means of ordered categorical variables within a genetic factor model

A genetic factor model is introduced for decomposition of group differences of the means of phenotypic behavior as well as individual differences when the research variables under consideration are ordered categorical. The model employs the general Genetic Factor Model proposed by Neale and Cardon (Methodology for genetic studies of twins and families, 1992) and, more specifically, the extension proposed by Dolan et al. (Behav Genet 22: 319-335, 1992) which enables decomposition of group differences of the means associated with genetic and environmental factors. Using a latent response variable (LRV) formulation (Muthén and Asparouhov, Latent variable analysis with categorical outcomes: multiple-group and growth modeling in Mplus. Mplus web notes: No. 4, Version 5, 2002), proportional differences of response categories between groups are modeled within the genetic factor model in terms of the distributional differences of latent response variables assumed to underlie the observed ordered categorical variables. Use of the proposed model is illustrated using a measure of conservatism in the data collected from the Australian Twin Registry.

Stability of genetic influence on morningness?eveningness: a cross-sectional examination of South Korean twins from preadolescence to young adulthood

A cross-sectional twin design was used to study the developmental nature of genetic and environmental influences on morningness-eveningness (M-E). A total of 977 South Korean twin pairs aged 9-23 years completed 13 items of a Korean version of the Composite Scale through the telephone interview. The total sample was split into three age groups: preadolescents, adolescents, and young adults. Twin correlations did not vary significantly with age, suggesting that genetic influences on M-E are stable throughout the developmental span. Results of model-fitting analyses indicated that genetic and environmental factors explained, respectively, 45% and 55% of the variance in all three age groups. Environmental factors were primarily those factors that twins did not share as a consequence of their common rearing; family environmental factors in M-E were consistently near zero in all three age groups. The present study is the first to demonstrate genetic influences on M-E in preadolescent children as young as 9 years old. In spite of differences in culture and frequencies of genes between South Koreans and Caucasians, genetic and environmental influences on M-E found in the present sample were remarkably similar to those reported by previous studies on the basis of late adolescent and adult Caucasian twins.

Joint tests of linkage and association for quantitative traits

In this paper we describe various study designs and analytic techniques for testing the joint hypothesis that a genetic marker is both linked to and associated with a quantitative phenotype. Issues of power and sampling are addressed. The distinction between methods that explicitly examine association and those that infer association by examining the distribution of allelic transmissions from a heterozygous parent is examined. Extensions to multivariate, multiallelic, and multilocus situations are addressed. Recent approaches that combine variance-components-based linkage analyses with joint tests of linkage in the presence of association for disentanglement of the linkage and association and the application of such methods to fine mapping are discussed. Finally, new classes of joint tests of linkage and association that do not require samples of related individuals are described.

Racial differences in birth health risk: A quantitative genetic approach

In the United States the gap between black and white babies’ birth weights has remained largely unexplained. Rather than trying to measure all relevant variables, we used a genetically informative design to study the relative importance of genetic and environmental factors. Employing multiple indicators of “birth health risk,” we found that the racial differences increased with the magnitude of the shared environmental effects. This suggested that possible genetic effects would not pertain to fetal genes, although genes affecting the mother’s physical or physiological characteristics could be important because they contribute to shared environment in our analysis.

Twin studies in metabolic diseases

The traditional role of twin studies has been to assess the relative role of genetic factors as a first step in defining the genetic architecture of complex traits. This has been based on the realization that monozygotic pairs (MZ) share all their genes, while dizygotic pairs (DZ) share 50% of their genes on average. Thus, greater similarity of MZ pairs compared to DZ pairs has been taken as prima facie evidence of the role of genetic factors. This is true provided the environmental similarity of MZ pairs is not greater than for DZ pairs for effects relevant to the trait in question. This first step in genetic studies was carried out long ago in many research areas, but not in others. More detailed knowledge of the genetic architecture of traits is then obtained by other means. In this paper, we give a brief overview of some results for metabolic diseases (ischaemic heart disease, hypertension, subarachnoid haemorrhage, NIDDM and IDDM) using the classical twin approach in a large, unselected population-based twin cohort. We also outline approaches to using twins that we believe will continue to be useful, particularly for the study of environmental effects.

Decomposition of multivariate phenotypic means in multigroup genetic covariance structure analysis

Observed differences in phenotypic means between groups such as parents and their offspring or male and female twins can be decomposed into genetic and environmental components. The decomposition is based on the assumption that the difference in phenotypic means is due to a difference in the location of the normal genetic and environmental distributions underlying the phenotypic individual differences. Differences between the groups in variance can be accommodated insofar as they are due to differences in unique variance or can be modeled using a scale parameter. The decomposition may be carried out in the standard analysis of genetic covariance structure using, for instance, LISREL. Illustrations are given using simulated data and twin data relating to blood pressure. Other possible applications are mentioned.

Simultaneous genetic analysis of longitudinal means and covariance structure in the simplex model using twin data

A longitudinal model based on the simplex model is presented to analyze simultaneously means and covariance structure using univariate longitudinal twin data. The objective of the model is to decompose the mean trend into components which can be attributed to those genetic and environmental factors which give rise to phenotypic individual differences and a component of unknown constitution which does not involve individual differences. Illustrations are given using simulated data and repeatedly measured weight obtained in a sample of 82 female twin pairs on six occasions.

Simultaneous genetic analysis of longitudinal means and covariance structure using the simplex model: application to repeatedly measured weight in a sample of 164 female twins

The simultaneous analysis of means and covariance structures is applied to longitudinal twin data. Body weight was measured on six occasions in a sample of young female MZ and DZ twins. When average body weight at the first measurement occasion, as well as the increments in weight at later occasions, are specified in the genetic part of the model that also adequately explains the covariance structure, a good fit is obtained. In this application the increase in body weight at each occasion is weighted by the square root of the genetic variance innovation terms that represent the new genetic variance entering into the process.

Estimation of individual genetic and environmental factor scores

Implicit in the application of the common-factor model as a method for decomposing trait covariance into a genetic and environmental part is the use of factor scores. In multivariate analyses, it is possible to estimate these factor scores for the communal part of the model. Estimation of scores on latent factors in terms of individual observations within the context of a twin/family study amounts to estimation of individual genetic and environmental scores. Such estimates may be of both theoretical and practical interest and may be provided with confidence intervals around the individual estimates. The method is first illustrated with stimulated twin data and next is applied to blood pressure data obtained in a Dutch sample of 59 male adolescent twin pairs. Subjects with high blood pressure can be distinguished into groups with high genetic or high environmental scores.

Fitting genetic models with LISREL: hypothesis testing

A brief introduction to the mathematical theory involved in model fitting is provided. The properties of maximum-likelihood estimates are described, and their advantages in fitting structural models are given. Identification of models is considered. Standard errors of parameter estimates are compared with the use of likelihood-ratio (L-R) statistics. For structural modeling, L-R tests are invariant to parameter transformation and give robust tests of significance. Some guidelines for fitting models to data collected from twins are given, with discussion of the relative merits of parsimony and data description.