Variance decomposition using an IRT measurement model.
paper
Cited
Public
- Authors
- van den Berg, StΓ©phanie M; Glas, Cees A W; Boomsma, Dorret I
- Year
- 2007
- Journal
- Behavior genetics
- PMID
- 17534709
- DOI
- 10.1007/s10519-007-9156-1
- PMCID
- PMC1914301
Large scale research projects in behaviour genetics and genetic epidemiology are often based on questionnaire or interview data. Typically, a number of items is presented to a number of subjects, the subjects' sum scores on the items are computed, and the variance of sum scores is decomposed into a number of variance components. This paper discusses several disadvantages of the approach of analysing sum scores, such as the attenuation of correlations amongst sum scores due to their unreliability. It is shown that the framework of Item Response Theory (IRT) offers a solution to most of these problems. We argue that an IRT approach in combination with Markov chain Monte Carlo (MCMC) estimation provides a flexible and efficient framework for modelling behavioural phenotypes. Next, we use data simulation to illustrate the potentially huge bias in estimating variance components on the basis of sum scores. We then apply the IRT approach with an analysis of attention problems in young adult twins where the variance decomposition model is extended with an IRT measurement model. We show that when estimating an IRT measurement model and a variance decomposition model simultaneously, the estimate for the heritability of attention problems increases from 40% (based on sum scores) to 73%.
Correlations of simulated sum scores as a function of true correlation at the latent level, variance of the latent trait (quality of the scale), and scatter of the item Ξ² parameters (entire scale or only top half, i.e., all > 0)
1β20 of 72
Next β
| Name | Type |
|---|---|
| 1PL IRT model local | drug |
| a1 local | variant |
| a2 local | variant |
| additive genetic influences | cohort |
| AE model local | phenotype |
| attention problems | phenotype |
| Attention problems (AP) subscale local | phenotype |
| behavioral phenotypes | phenotype |
| birth cohort | cohort |
| children | cohort |
| conservatism local | phenotype |
| construct local | phenotype |
| Continuous traits local | phenotype |
| disease | phenotype |
| dominance genetic effects local | phenotype |
| dominance genetic variance local | phenotype |
| DZ correlation local | phenotype |
| DZ twin cohort local | cohort |
| Empirical data local | cohort |
| epistasis local | phenotype |
| extraversion | phenotype |
| Family design local | cohort |
| general population | cohort |
| Genetic variance | phenotype |
| healthy | phenotype |
| heritability | phenotype |
| Heritability coefficient local | phenotype |
| heritiability local | phenotype |
| Indirectly measured phenotypes local | phenotype |
| IRT measurement model local | drug |
| latent data local | phenotype |
| latent trait | phenotype |
| latent trait ΞΈ local | phenotype |
| Liability_score local | phenotype |
| log-transformed sum scores local | phenotype |
| Log-transformed sum scores local | drug |
| Lower end of scale local | phenotype |
| measurement error local | phenotype |
| Multilog local | drug |
| MZ correlation local | phenotype |
| MZ twin cohort local | cohort |
| Netherlands Twin Register | cohort |
| Nonβadditive genetic component local | phenotype |
| non-shared environmental effects local | phenotype |
| non-shared environmental variance local | phenotype |
| Normal AE model local | drug |
| Person | cohort |
| phenotype of interest | phenotype |
| polygenic traits local | phenotype |
| psychiatric disorders | phenotype |
| psychopathology | phenotype |
| Psychopathology scales local | phenotype |
| reliability local | phenotype |
| Reliability local | phenotype |
| sex | phenotype |
| shared environmental influence | phenotype |
| Sibling pair local | cohort |
| Sick local | phenotype |
| simulated data | cohort |
| Simulated item data local | cohort |
| sum scores | drug |
| sum scores local | phenotype |
| symptom | phenotype |
| Symptom_parameter local | phenotype |
| trait | phenotype |
| Twin cohort | cohort |
| Upper end of scale local | phenotype |
| Van den Berg et al. 2006c local | cohort |
| YASR local | cohort |
| young adults | cohort |
| Young Adult Self-Report local | phenotype |
No uploaded files.
Not in any collection.
In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Harmonization of Neuroticism and Extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium: an application of Item Response Theory. | 2014 | 24828478 |
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Harmonization of SDQ and ASEBA Phenotypes: Measurement Variance Across Cohorts. | JoviΔ M et al. | β | 2025 | β |
| Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption. | Chen G et al. | β | 2025 | β |
| Using Bifactor Twin Modeling to Assess the Genetic and Environmental Dimensionality of Adult ADHD Symptoms. | Knyspel J et al. | β | 2025 | β |
| Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect. | Bruins S et al. | β | 2024 | β |
| Genotype-Environment Interaction in ADHD: Genetic Predisposition Determines the Extent to Which Environmental Influences Explain Variability in the Symptom Dimensions Hyperactivity and Inattention. | Schwabe I et al. | β | 2024 | β |
| Harmonizing the CBCL and SDQ ADHD scores by using linear equating, kernel equating, item response theory and machine learning methods. | JoviΔ M et al. | β | 2024 | β |
| Sports club participation impacts life satisfaction in adolescence: A twin study. | Dings A et al. | β | 2024 | β |
| Bias in Gene-by-Environment Interaction Effects with Sum Scores; An Application to Well-being Phenotypes. | Pelt DHM et al. | β | 2023 | β |
| Genotype-Environment Interaction in ADHD: Genetic Predisposition Determines the Extent to Which Environmental Influences Explain Variability in the Symptom Dimensions Hyperactivity and Inattention | Schwabe I et al. | β | 2023 | β |
| Psychometric properties of sum scores and factor scores differ even when their correlation is 0.98: A response to Widaman and Revelle. | McNeish D | β | 2023 | β |
| Stressful life events increase the risk of major depressive episodes: A population-based twin study. | BjΓΈrndal LD et al. | β | 2023 | β |
| Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities. | Mallard TT et al. | β | 2022 | β |
| Pathfinder: a gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences. | Malanchini M et al. | β | 2021 | β |
| Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes. | Luningham JM et al. | β | 2019 | β |
| Etiological Distinction Across Dimensions of Math Anxiety. | Lukowski SL et al. | β | 2019 | β |
| Joint factorial structure of psychopathology and personality. | RosenstrΓΆm T et al. | β | 2019 | β |
| Psychometric Modelling of Longitudinal Genetically Informative Twin Data. | Schwabe I et al. | β | 2019 | β |
| The moderating role of SES on genetic differences in educational achievement in the Netherlands. | de Zeeuw EL et al. | β | 2019 | β |
| Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies. | Schwabe I et al. | β | 2019 | β |
| An Extended Twin-Pedigree Study of Neuroticism in the Netherlands Twin Register. | Boomsma DI et al. | β | 2018 | β |
| Extracting stability increases the SNP heritability of emotional problems in young people. | Cheesman R et al. | β | 2018 | β |
| Childhood behaviour problems show the greatest gap between DNA-based and twin heritability. | Cheesman R et al. | β | 2017 | β |
| Sum Scores in Twin Growth Curve Models: Practicality Versus Bias. | Luningham JM et al. | β | 2017 | β |
| Dependence of Gene-by-Environment Interactions (GxE) on Scaling: Comparing the Use of Sum Scores, Transformed Sum Scores and IRT Scores for the Phenotype in Tests of GxE. | Murray AL et al. | β | 2016 | β |
| Evidence for Gender-Dependent Genotype by Environment Interaction in Adult Depression. | Molenaar D et al. | β | 2016 | β |
| Genes, Culture and Conservatism-A Psychometric-Genetic Approach. | Schwabe I et al. | β | 2016 | β |
| Obstructive Sleep Apnea Syndrome in Company Workers: Development of a Two-Step Screening Strategy with a New Questionnaire. | Eijsvogel MM et al. | β | 2016 | β |
| Analysis of Behavioral and Emotional Problems in Children Highlights the Role of Genotype Γ Environment Interaction. | Molenaar D et al. | β | 2015 | β |
| Assessing genotype by environment interaction in case of heterogeneous measurement error. | Schwabe I et al. | β | 2014 | β |
| Harmonization of Neuroticism and Extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium: an application of Item Response Theory. | van den Berg SM et al. | β | 2014 | β |
| Testing systematic genotype by environment interactions using item level data. | Molenaar D et al. | β | 2014 | β |
| Three-and-a-half-factor model? The genetic and environmental structure of the CBCL/6-18 internalizing grouping. | FraniΔ S et al. | β | 2014 | β |
| Using item response theory to model multiple phenotypes and their joint heritability in family data. | Fragoso TM et al. | β | 2014 | β |
| Self-efficacy is mainly genetic, not learned: a multiple-rater twin study on the causal structure of general self-efficacy in young people. | Waaktaar T et al. | β | 2013 | β |
| A genome-wide meta-analysis of association studies of Cloninger's Temperament Scales. | Service SK et al. | β | 2012 | β |
| Genetic and environmental causes of variation in perceived loneliness in young people. | Waaktaar T et al. | β | 2012 | β |
| Genetic and environmental causes of variation in trait resilience in young people. | Waaktaar T et al. | β | 2012 | β |
| Power of IRT in GWAS: successful QTL mapping of sum score phenotypes depends on interplay between risk allele frequency, variance explained by the risk allele, and test characteristics. | van den Berg SM et al. | β | 2012 | β |
| Validating the 28-tender joint count using item response theory. | Siemons L et al. | β | 2011 | β |
| Binge eating disorder: a symptom-level investigation of genetic and environmental influences on liability. | Mitchell KS et al. | β | 2010 | β |
| Phenotypic complexity, measurement bias, and poor phenotypic resolution contribute to the missing heritability problem in genetic association studies. | van der Sluis S et al. | β | 2010 | β |
| The mediating effect of parental neglect on adolescent and young adult anti-sociality: a longitudinal study of twins and their parents. | Eaves LJ et al. | β | 2010 | β |
| Group differences in the heritability of items and test scores. | Wicherts JM et al. | β | 2009 | β |
| Imposing nonlinear constraints when estimating genetic and cultural transmission under assortative mating: a simulation study using Mx and BUGS. | van den Berg SM | β | 2009 | β |
| Item response theory and clinical measurement. | Reise SP et al. | β | 2009 | β |
| A general test for gene-environment interaction in sib pair-based association analysis of quantitative traits. | van der Sluis S et al. | β | 2008 | β |