Biomedical subjects

E J van den Oord

Publications and source records attributed to E J van den Oord.

18 recordsLinked to original sources

PubMed2006

Genomewide linkage study in the Irish affected sib pair study of alcohol dependence: evidence for a susceptibility region for symptoms of alcohol dependence on chromosome 4.

Alcoholism is a relatively common, chronic, disabling and often treatment-resistant disorder. Evidence from twin and adoption studies indicates a substantial genetic influence, with heritability estimates of 50-60%. We conducted a genome scan in the Irish Affected Sib Pair Study of Alcohol Dependence (IASPSAD). Most probands were ascertained through alcoholism treatment settings and were severely affected. Probands, affected siblings and parents were evaluated by structured interview. A 4 cM genome scan was conducted using 474 families of which most (96%) were comprised by affected sib pairs. Nonparametric and quantitative linkage analyses were conducted using DSM-IV alcohol dependence (AD) and number of DSM-IV AD symptoms (ADSX). Quantitative results indicate strong linkage for number of AD criteria to a broad region of chromosome 4, ranging from 4q22 to 4q32 (peak multipoint LOD=4.59, P=2.1 x 10(-6), at D4S1611). Follow-up analyses suggest that the linkage may be due to variation in the symptoms of tolerance and out of control drinking. There was evidence of weak linkage (LODs of 1.0-2.0) to several other regions, including 1q44, 13q31, and 22q11 for AD along with 2q37, 9q21, 9q34 and 18p11 for ADSX. The location of the chromosome 4 peak is consistent with results from prior linkage studies and includes the alcohol dehydrogenase gene cluster. The results of this study suggest the importance of genetic variation in chromosome 4 in the etiology and severity of alcoholism in Caucasian populations.

Aged↗

PubMed2001

Using parental ratings to study the etiology of 3-year-old twins' problem behaviors: different views or rater bias?

Child Behavior Checklist questionnaires (Achenbach, 1992), filled in separately by mothers and fathers, were collected for an effective sample of 3,501 Dutch 3-year-old twin pairs. To disentangle the child's phenotype from that of the rater, two contrasting models were fitted to the data. One model, called a Rater Bias model, is based on the assumption that both parents assess exactly the same behaviors in the child. A weaker alternative of this model, called a Psychometric model, assumes that apart from these common behavioral views, each parent also assesses a unique aspect of the child's behavior. A Psychometric model fitted the data of both Internalizing and Externalizing scales significantly better than a Rater Bias model. This implied that each parent provided unique information from his or her own perspective, apart from the common behavioral view. Using this best fitting model, the etiology of both the Internalizing and Externalizing scales was studied. Common factors (influencing behaviors similarly assessed by both parents) were more important than unique factors (influencing behaviors uniquely assessed by one parent). Common genetic factors explained about 50% of the variance of both scales, indicating a possible inborn vulnerability to childhood psychopathology. Common environmental factors not shared between twins (free of unreliability and error) explained around 14% of both scales, suggesting the importance of pure idiosyncratic experiences even for children as young as 3 years. Common environmental factors shared between twins (unconfounded by rater bias) were only found for the Externalizing scale, explaining 18% of the variance. Rater bias and unreliability, if present in the data, were included in the estimates of the unique factors. Unique genetic, shared, and nonshared environmental factors each explained around 8% of the variance for both scales. These small effects could be detected because of the large sample of twin pairs used.

Adult↗

PubMed2001

Estimating effects of latent and measured genotypes in multilevel models.

Multilevel modelling is a data analysis technique for analysing linear models in samples with a hierarchical or clustered structure. Clustered data are often present in genetic research where family members may either be required or serve a methodological purpose to study hereditary factors. These samples imply a natural hierarchy because genetically related individuals are grouped within families. We first demonstrate the use of multilevel modelling to study latent genetic and environmental components of variance in extended families where subjects may be related as twins, full siblings, half siblings, or cousins. Next, measured genotypes are included to estimate locus effects. Because the model accounts for the clustering of observations by estimating a random intercept at the family level, it tests for genotype effects on the phenotype within families so that possible population stratification effects cannot cause false positive results. Several extensions are discussed such as testing for genotype-environment interactions, analysing different types of response scales, or tailoring the model to other sample structures. To illustrate the approach we used birth weight data of 5562 children from 3643 fathers from 3186 mothers in 2873 extended families to which simulated genotypes of a hypothetical locus were added.

Adult↗

PubMed2001

An evaluation of nonresponse bias in peer, self, and teacher ratings of children's psychosocial adjustment.

The last decades have shown a rapid increase in nonresponse rates. For this reason it is important to study nonresponse and think about it critically. In this article we investigated whether nonresponse affected estimates of the levels of adjustment problems in children and the correlations between these outcomes. The nonresponse was caused by parents who refused permission to interview their children at school, parents who did not return a questionnaire, teachers who did not complete the questionnaire, and parents who refused to participate in an in-depth study, with nonresponse rates of 9%, 69%, 25%, and 46% respectively. The sample consisted of 1282 children aged 4 5 years and the dependent measures were peer-rated sociometric status, self-rated wellbeing at school, and teacher-rated behaviour problems. Despite considerable nonresponse in some conditions our results showed hardly any evidence for bias. This suggested that bias cannot simply be inferred from the amount of nonresponse and that standard rules such as "nonresponse rates higher than 50% are not acceptable" lack a scientific basis. Instead, we argue that to assess nonresponse bias the specific conditions and analyses of the study will need to be considered and special measures may be required.

Child, Preschool↗

PubMed2000

Framework for identifying quantitative trait loci in association studies using structural equation modeling.

In this article, we suggest a framework for identifying quantitative trait loci (QTL) in association studies using structural equation modeling. Two tests to detect QTLs and estimate the proportion of variance they explain are discussed. The first test assumes that there is no population admixture and only requires that the subjects are genotyped. The second one is a TDT-like test that cannot give false-positive results due to population admixture but requires that the parents of the subjects are genotyped as well and that subjects have at least one heterozygous parent. Power calculations showed that with the first test, 100 subjects were generally sufficient to detect a locus that explained 10% and less than 1,000 subject to detect a locus that explained 1% of the total variance. To obtain the same power, the TDT-like test required an initial sample that was on average 1.7 times larger. Calculations showed that the first test was quite robust against population admixture and that the power of tests to detect admixture was good. This suggested that in the extreme and very specific conditions in which population admixture may cause false-positive findings, admixture can often be detected.

Alleles↗

PubMed2000

An evaluation of different approaches for behavior genetic analyses with psychiatric symptom scores.

We used a simulation study to evaluate six approaches for behavior genetic analyses of psychiatric symptom scores. For the selection of the correct model, the best results were obtained with approaches using transformed scores in combination with a procedure involving p-values. With normalizing transformations, the chi 2 test statistic gave a reasonable impression of the overall fit of the model but was less accurate when used as a difference test. The asymptotic distribution free estimation methods yielded chi 2s that were much too large. All data analysis techniques yielded substantially biased parameter estimates. The most biased results were obtained with normalizing transformations. The least biased results were obtained with tobit correlations, but because of its large standard errors the most precise estimates were obtained with polychoric correlations and optimal scale scores. An empirical study showed that a recognition of the role of methodological factors was helpful to understand part of the differences between assessment instruments, raters, and data analysis techniques that were found in the real data.

Adolescent↗

PubMed2000

The use of mixture models to perform quantitative tests for linkage disequilibrium, maternal effects, and parent-of-origin effects with incomplete subject-parent triads.

The transmission disequilibrium test and related methods require complete triads of genotyped cases plus both parents to test for linkage disequilibrium in the presence of population stratification. Based on the work of Weinberg (1999ab) and Schaid and Li (1997) with dichotomous outcomes, we discuss how additional tests for maternal or parent-of-origin effects can be performed and incomplete subject-parent triads can be used in situations where the outcome is a quantitative variable. This is achieved using a finite mixture model where class membership is known for complete triads and unknown for incomplete triads. Simulations were performed assuming different genetic models and various amounts of missing parental genotypes. Results showed that the tests resulted in accurate Type I errors, recovered much of the loss in information caused by missing genotypes, and yielded unbiased parameter estimates. Extensions of the model to nuclear families, multiple loci, and multiple outcome variables are briefly discussed.

Alleles↗

PubMed2000

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.

Adolescent↗

PubMed2000

Genetics of testosterone and the aggression-hostility-anger (AHA) syndrome: a study of middle-aged male twins.

The aim of this study was to determine the genetic contribution to the variation in testosterone and the aggression-hostility-anger (AHA) syndrome in middle-aged twins. Moreover, the relation between testosterone and this syndrome, and possible common genetic mechanisms were investigated. Towards this end, blood samples were collected at two time points; the AHA syndrome was measured using three questionnaires: the Buss-Durkee Hostility Inventory with seven subscales, the Jenkins Activity Survey and the Spielberger State-Trait Anger Scale. The results showed substantial heritabilities for testosterone (approximately 60%) and moderate to fair heritabilities for the nine measures of the AHA syndrome (23-53%). The best fitting model for testosterone at two time points included a small age component and additive genetic and unique environmental factors, while a multivariate analysis of the nine AHA subscales resulted in an independent pathway model with two common additive genetic and two common unique environmental factors. No correlation between the common genetic factor influencing testosterone and the AHA subscales was found. We did, however, detect a negative correlation between the common environmental factor underlying testosterone and both common environmental factors influencing the nine AHA subscales, which may reflect a tendency for testosterone levels to rise and hostility to drop (or vice versa) after repeatedly experiencing success (or failure).

Adult↗

PubMed1999

Method to detect genotype-environment interactions for quantitative trait loci in association studies.

Khoury et al. (Am J Hum Genet 1988;42:89-95 and Am J Epidemiol 1993;137:1241-50) presented an epidemiologic approach to examine genotype-environment interaction in situations where the disease is either present or absent. In this article, the author extends the approach of Khoury et al. to quantitative outcome variables. This extension is relevant for diseases that are extremes on a continuum or when continuous risk factors are studied. To account for a possible admixture of subgroups in the sample, tests for genotype-environment interaction are discussed for designs with parents as controls as well as without parents as controls. Assuming two environmental conditions, the author demonstrates how the power of these tests can be calculated and used to estimate the sample sizes needed to detect genotype-environment interaction in a variety of conditions. In addition, he analyzes simulated data to demonstrate the detection of different mechanisms of genotype-environment interaction and to study the effectiveness of this approach to identify the correct mechanism. Finally, extensions to multiple environmental conditions and designs with multiple subjects per family are discussed.

Analysis of Variance↗

PubMed1999

Differences between school classes in preschoolers' psychosocial adjustment: evidence for the importance of children's interpersonal relations.

We examined differences between school classes with respect to three aspects of psychosocial adjustment at school, namely the extent that children in the class liked to play with each other, the number of teacher-reported behaviour problems, and children's feelings of wellbeing at school. The sample consisted of 1282 4- to 5-year-olds from 94 school classes and 51 schools, but due to nonresponse actual sample sizes were somewhat smaller for most analyses. Multilevel analyses showed that on average 87% of the variance was at the child level, 11% at the class level, and 3% at the school level. This indicated that a non-negligible amount of variance could not be accounted for by factors at the child level. Furthermore, this variance was mainly associated with differences between classes instead of differences between schools. A set of variables that pertained to sociodemographic characteristics of schools, school facilities, organisational aspects of classrooms, and the teacher did not provide an adequate explanation for the differences in adjustment levels. In contrast to these traditional variables, social network indices yielded substantial correlations, showed consistent trends across the different adjustment measures, and fulfilled the necessary requirement that to explain differences between school classes the predictor variables themselves should differ for classes within the same school. These results suggested that aspects of the interpersonal relations of children in the classroom such as proximity, integration, and the amount of contact could be determinants of differences between school classes in psychosocial adjustment.

Adaptation, Psychological↗

PubMed1997

WISC profiles in child psychiatric diagnosis: sense or nonsense?

OBJECTIVE: WISC factor structure, the specificity of WISC factors, and diagnostic correlates of WISC profiles were studied to investigate the contribution of WISC profile analysis to child psychiatric diagnosis. METHOD: The fit of various factor models was tested and differences between various clinical groups regarding three WISC patterns were studied, using the WISC-RN (the Dutch version of the WISC-R) scores of a group of 465 Dutch children (mean age 11.2 years) referred to a psychiatric clinic. RESULTS: The traditional factor models were replicated in this study. However, most of the variance in the factors could be explained by an underlying factor, "general intelligence," suggesting that WISC factors measure specific cognitive abilities only to a limited degree. Another important finding is that the various DSM-III-R and Child Behavior Checklist diagnoses could not be distinguished on the basis of WISC profiles. CONCLUSION: The data demonstrate that the relationship between WISC factors and specific cognitive abilities and neuropsychological functions needs further clarification in order to improve the validity of the traditional use of WISC profiles as a source of diagnostic information.

Adolescent↗

PubMed1997

Effects of censored variables on family studies.

In the present paper effects of censored variables on estimates of genetic and environmental influences were studied. Analytic results showed with 50% censoring, about 15-20% of the variance may be attributed to the wrong source and that this amount increases rapidly with more than 70% censoring. Censoring effects on comparisons between different genetic studies, subgroups within a study (e.g., sex or age groups), or different behaviors (e.g., the heritabilities of delinquency and depression) were also examined. Results indicated that censoring may be quite influential for these kinds of comparisons. For instance, it was demonstrated that, especially for unstandardized solutions, small initial group differences in means can lead to seriously biased conclusions concerning the resemblance in biometric parameters. Finally, a stimulation study supported the applicability of the general analytical results and showed that summed scores of censored Likert-type items may be seriously affected by censoring.

Analysis of Variance↗

PubMed1997

Continuity and change in children's social maladjustment: a developmental behavior genetic study.

Two developmental models were used to study genetic and environmental mechanisms underlying continuity and change in children's maladjustment. The transmission model assumed that successive levels of functioning were causally linked and that earlier experiences or prior genetic influences affected later maladjustment. The liability model related continuity in problem behavior to stable underlying environmental or genetic factors. The analyses pertained on average to 436 pairs of full siblings, 119 pairs of half siblings, and 122 pairs of cousins for whom maternal ratings of problem behaviors were available at ages 4-6, 6-8, and 8-10. Nonshared environmental influences appeared to be most important for changes in children's problem behaviors and did not have significant effects on age-to-age continuity. To represent the genetic and shared environmental mechanisms underlying stability in problem behavior, the authors preffered liability models without time specific effects.

Adult↗

PubMed1996

A genetic study of maternal and paternal ratings of problem behaviors in 3-year-old twins.

Genetic and environmental influences on problem behaviors were studied in 3-year-old twins. Fathers' and mothers' ratings of problem behaviors in twins-236 monozygotic (MZ) girls, 210 MZ boys, 238 dizygotic (DZ) girls, 265 DZ boys, and 409 DZ opposite sex pairs- were obtained with the Child Behavior Checklist for Ages 2-3 (T.M. Achenbach, 1992). Twin correlations and results from a model fitting approach showed that genetic, shared environmental, and nonshared environmental influences accounted on average for about 64%, 9%, and 27% of the variance. Although shared environmental influences were small for most scales, they were important for Total Problems and somewhat larger for Externalizing than for Internalizing behaviors. Significant sex differences in genetic and environmental influences and evidence for sibling contrast effects were found for the Overactive scale.

Child Behavior Disorders↗

PubMed1995

A twin-singleton comparison of problem behaviour in 2-3-year-olds.

Twin-singleton differences in problem behaviours in 2-3-year-olds were studied. Maternal ratings of children's problem behaviours were obtained with the CBCL/2-3. The twin sample consisted of 1363 twin pairs (456 MZ, 907 DZ), the sample of singletons consisted of 420 children from the general population. Results indicated that the general level of problem behaviours in twins was broadly comparable to that in singletons. Four of the seven syndromes showed lower scores for twins. These differences, however, were small and mainly caused by lower scores for DZ twins in comparison to MZ twins and singletons. Part of the difference could be attributed to the higher maternal age in the twin groups. Higher means for boys were found for the total problem score, and the Aggressive and Overactive syndromes.

Adult↗

PubMed1994

A study of problem behaviors in 10- to 15-year-old biologically related and unrelated international adoptees.

Genetic and environmental influences on problem behaviors were studied in a sample of international adoptees. Parental ratings of childrens' problem behaviors were obtained with the Child Behavior Checklist (CBCL). The sample (mean age, 12.4 years) comprised a group of biological siblings (111 pairs), a group of nonbiological siblings (221 pairs), and a group of singletons (94). Nonshared environmental influences were most important for problem behaviors studied in this paper. Genetic influences were substantial for Externalizing behaviors but unimportant for Internalizing behaviors. For the CBCL total problem score, Attention Problems, and Externalizing behaviors, the results of the present study were in agreement with findings from twin studies. The lack of genetic influences on Internalizing behaviors contrasts with results from twin studies. For the total problem score, the Externalizing grouping, Delinquent Behavior, and Aggressive Behavior, variances for singletons were significantly smaller than for siblings. Model fit indices indicated that these differences in variances are better attributed to smaller effects of factors associated with sibship size than to active influences of siblings on each other. Significant sex differences were found for 8 of the 10 scales. The larger variances for boys on the Externalizing grouping and Aggressive Behavior could be explained by genetic influences.

Adolescent↗

PubMedDate not supplied

Behavior problems among children from different family structures: the influence of genetic self-selection.

To examine both genetic and environmental influences on children's behavior problems in households defined by marital status and sibling relatedness, this study applied behavioral genetic methodology to four groups totalling 1524 sibling pairs drawn from 796 households: (1) two-parent full siblings, (2) two-parent half siblings, (3) mother-only full siblings, and (4) mother-only half siblings. Model-fitting procedures found that within-group variation on four subscales from the Behavior Problems Index was best explained by a model including both genetic and shared environmental factors. This model was then fit to the behavior problems means of the four groups. Its successful fit to these mean structures suggested that mean-level differences between groups were explained with the same influences that accounted for within-group variation. Genetic influences accounted for 81% to 94% of the mean-level difference in behavior problems between the two-parent, full sibling and the mother-only, half sibling groups. In contrast, shared environmental influences accounted for 67% to 88% of the mean-level difference in behavior problems between the two-parent, full sibling and mother-only, full sibling groups. The genetic influences are interpreted in terms of genetic self-selection into family structures.

Adolescent↗