Bias in Variance Components Due to Nonresponse in Twin Studies

2006 ◽  
Vol 9 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Annica Dominicus ◽  
Juni Palmgren ◽  
Nancy L. Pedersen
Keyword(s):  
Cognitive Abilities ◽  
Variance Components ◽  
Variance Component ◽  
Pair Correlation ◽  
Twin Studies ◽  
Likelihood Estimation ◽  
Genetic Influences ◽  
Perceptual Speed ◽  
Environmental Variance ◽  
Variance Component Estimates

AbstractIncomplete data on trait values may bias estimates of genetic and environmental variance components obtained from twin analyses. If the nonresponse mechanism is ‘ignorable’ then methods such as full information maximum likelihood estimation will produce consistent variance component estimates. If, however, nonresponse is ‘nonignorable’, then the situation is more complicated. We demonstrate that a within-pair correlation of nonresponse, possibly different for monozygotic (MZ) and dizygotic (DZ) twins, may well be compatible with ‘ignorability’. By means of Monte Carlo simulation, we assess the potential bias in variance component estimates for different types of nonresponse mechanisms. The simulation results guide the interpretation of analyses of data on perceptual speed from the Swedish Adoption/Twin Study of Aging. The results suggest that the dramatic decrease in genetic influences on perceptual speed observed after 13 years of follow-up is not attributable solely to dropout from the study, and thus support the hypothesis that genetic influences on some cognitive abilities decrease with age in late life.

Variance estimate for the Allen activity index

1998 ◽  
Vol 25 (6) ◽  
pp. 643 ◽  
Author(s):  
Richard M. Engeman ◽  
Lee Allen ◽  
Gary O. Zerbe
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Activity Index ◽  
Likelihood Estimation ◽  
Mixed Linear Model ◽  
Variance Formula ◽  
Variance Estimate ◽  
Wildlife Populations ◽  
Variance Component Estimates ◽  
Restricted Maximum Likelihood Estimation

The Allen activity index, originally developed for monitoring dingo populations, is statistically described as a mixed linear model, from which a variance formula for the index is derived. The resulting formula requires input of variance component estimates, the estimation of which is accomplished using restricted maximum-likelihood estimation. An example is used to demonstrate the calculation of the variance components and their use in the variance formula. Application of the variance formula substantially enhances the quantitative practicality of this useful index of wildlife populations.

scholarly journals Increasing Phenotypic and Genetic variations in Hyperactivity/Inattention Problems from Age 3 to 13 Years: A Cross-Sectional Twin Study

2014 ◽  
Vol 17 (6) ◽  
pp. 545-552 ◽  
Author(s):  
Yoon-Mi Hur
Keyword(s):  
Meta Analysis ◽  
Model Fitting ◽  
Twin Studies ◽  
Total Sample ◽  
Genetic Influences ◽  
Adhd Symptoms ◽  
Environmental Variance ◽  
Twin Design ◽  
Cross Sectional ◽  
Strengths And Difficulties

A twin design was used to examine the developmental nature of genetic, environmental, and phenotypic variations in hyperactivity and inattention problems (HIP). Mothers of 662 complete pairs of twins (273 monozygotic [MZ] pairs and 389 dizygotic [DZ] pairs) aged from 3 to 13 years (mean [SD] age = 8.3 [2.9] years) responded to the items of the HIP scale of the Strengths and Difficulties questionnaire via a telephone interview. Maximum likelihood MZ and DZ twin correlations in the total sample were 0.47 (95% CI: 0.37–0.55) and −0.01 (95% CI: −0.11–0.09). A standard univariate model incorporating age as a modifier was applied to the raw data. Results of model-fitting analyses showed that the phenotypic variation of HIP monotonically increased from age 3 to age 12 and that this increase was completely due to an increase in genetic variance, suggesting that it is genes that expand individual difference in ADHD symptoms with age during childhood. Child-specific environmental variance was constant during this age period. In terms of relative influences, total genetic factors increased from 33% (95% CI: 27–44%) at age 3 to 51% (95% CI: 28–71%) at age 13 and this increase was accompanied by a decrease in relative influences of child-specific environmental factors from 67% (95% CI: 56–73%) at age 3 to 49% (95% CI: 29–72%) at age 13. These estimates of genetic influences were somewhat lower than those found in most twin studies of ADHD symptoms. However, the increasing trend of genetic influences with age during childhood was consistent with the results of a recent meta-analysis of ADHD symptoms.

Why twin studies really don't tell us much about human heritability

1999 ◽  
Vol 22 (5) ◽  
pp. 904-905 ◽  
Author(s):  
Sidney J. Segalowitz
Keyword(s):  
Research Design ◽  
Twin Studies ◽  
Genetic Influences ◽  
Environmental Variance ◽  
Design Behaviour

The derivation of heritability from human twin studies involves serious methodological flaws. Heritability is consistently overestimated because of biological confounds of twinning, consistent and often gross underestimation of the environmental variance, and nonadditive genetic influences that can hugely exaggerate heritability values. Despite this bad research design, behaviour geneticists continue to publish results implying that their heritability results are valid.

Sampling Errors of Variance Components in Small Sample Multifacet Generalizability Studies

1978 ◽  
Vol 3 (4) ◽  
pp. 319-346 ◽  
Author(s):  
Philip L. Smith
Keyword(s):  
Monte Carlo ◽  
Variance Components ◽  
Variance Component ◽  
Generalizability Theory ◽  
Small Sample ◽  
Least Square ◽  
Sampling Errors ◽  
Sample Stability ◽  
Sampling Behavior ◽  
Variance Component Estimates

The paper describes the small sample stability of least square estimates of variance components within the context of generalizability theory. Monte Carlo methods are used to generate data conforming to some selected multifacet generalizability designs to illustrate the sampling behavior of variance component estimates. Based on the findings, recommendations are made concerning the design of efficient small sample generalizability studies.

The Sensitivity of Variance Component Estimates to Underreporting: Method and Application to Substance Abuse Data

2007 ◽  
Vol 10 (5) ◽  
pp. 721-728 ◽  
Author(s):  
Paul W. Andrews ◽  
Kenneth S. Kendler ◽  
Nathan Gillespie ◽  
Michael C. Neale
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Illicit Drug ◽  
Sensitive Information ◽  
Twin Design ◽  
Psychological Constructs ◽  
Willingness To Disclose ◽  
Classical Twin Design ◽  
Illicit Drug Abuse ◽  
Variance Component Estimates

AbstractMany studies of human behavior and psychological constructs rely on subjects' willingness to disclose information about themselves. This is problematic for phenotypes that require the disclosure of sensitive information, such as sexual behavior or illicit drug use, which are likely to be underreported. We describe a method for evaluating how sensitive variance component estimates are to underreporting. The method involves estimating, by maximum likelihood, the original population proportions of the response classes, and adjusting them for a set of hypothesized underreporting parameters. If the true values of the underreporting parameters were known, the researcher could estimate the variance components based on these values. Usually, underreporting levels are not known with certainty. However, it is possible to assume a specific value for the underreporting rate, obtain response pattern proportions adjusted for this rate, and then to conduct the analyses on these revised estimates. By repeating the procedure across the range of plausible underreporting values, the researcher can assess how sensitive the variance component estimates are to variation in underreporting. We apply this method to a sample of male-male twin pairs who reported on themselves and their co-twins for illicit drug abuse and dependence (DAD). We show how underreporting influences estimates of additive genetic, common environment, and specific environment variance components (A, C, and E) obtained for DAD in a classical twin design.

scholarly journals The Heritability of Cognitive Aging: A Systematic Review of Longitudinal Twin Studies

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 1026-1026
Author(s):  
Alice Kim ◽  
Alyssa Kam ◽  
Maxwell Kofman ◽  
Christopher Beam
Keyword(s):  
Systematic Review ◽  
Cognitive Ability ◽  
Cognitive Performance ◽  
Cognitive Aging ◽  
Environmental Influences ◽  
Twin Studies ◽  
Genetic Influences ◽  
Environmental Variance ◽  
Meta Analyses ◽  
Adult Twins

Abstract Heritability of cognitive ability changes across late adulthood, although whether genetic variance increases or decreases in importance is not understood well. We performed a systematic review of the heritability of cognitive ability derived from longitudinal twin studies of middle-aged and older adult twins. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, articles were identified in APA PsycINFO and Clarivate Web of Science electronic databases. Identified articles were screened by title and abstract; remaining full-text articles were then fully evaluated. Reference sections served as an additional method for identification of relevant articles. In total, 3,106 articles were identified and screened, 28 of which were included and were based on data from 10 longitudinal twin studies published from 1994-2021. There are large genetic influences on an initial level of cognitive performance across domains whereas there are small to moderate genetic influences on change in performance with age. Evidence was less definitive about whether the same or different genetic factors contribute to both level and change. Non-shared environmental influences appeared to drive individual changes in cognitive performance. Heritability tended to either be stable or decline after 65 years, possibly because of the increasing importance of non-shared environmental influences on cognitive ability. Recent studies report increases in heritability across specific subtests and domains. Shared environmental variance accounted for little variance in cognitive ability. Emerging research questions and future directions for understanding genetic and environment influences in the context of gene-environment interplay are highlighted in this review.

THE EFFECT OF ADJUSTING FOR YEARLY SELECTION TRENDS ON VARIANCE COMPONENT ESTIMATES

1989 ◽  
Vol 69 (2) ◽  
pp. 487-490
Author(s):  
W. W. GEARHEART ◽  
M. E. DAVIS ◽  
W. R. HARVEY
Keyword(s):  
Beef Cattle ◽  
Key Words ◽  
Variance Components ◽  
Variance Component ◽  
Error Variance ◽  
Component Selection ◽  
Variance Component Estimates ◽  
Selection Of

Computer-generated beef cattle data were used to investigate the effect of accounting for the yearly selection of parents on the bias and precision of sire and error variance component estimates. The adjustment for yearly selection reduced the biases of estimated sire variance components, but resulted in losses of precision of up to 25%. Key words: Beef cattle, variance component, selection

Quantifying idiosyncratic and shared contributions to stimulus evaluations

2018 ◽  
Author(s):  
Joel Eduardo Martinez ◽  
Friederike Funk ◽  
Alexander Todorov
Keyword(s):  
Repeated Measures ◽  
Variance Component ◽  
Psychological Problem ◽  
Psychological Research ◽  
Large Variability ◽  
Four Dimensions ◽  
Stimulus Evaluation ◽  
Crossed Random Effects ◽  
Novel Objects ◽  
Variance Component Estimates

A fundamental psychological problem is identifying the idiosyncratic and shared contributions to stimulus evaluation. However, there is no established method for estimating these contributions and the existing methods have led to divergent estimates. Moreover, in many studies participants rate the stimuli only once, although at least two measurements are required to estimate idiosyncratic contributions. Here, participants rated faces or novel objects on four dimensions (beautiful, approachable, likeable, dangerous) for a total of ten blocks to better estimate the preferences of individual raters. First, we show that both intra-rater and inter-rater agreement – measures related to idiosyncratic and shared contributions, respectively – increase with repeated measures. Second, to find best practices, we compared estimates from correlation indices and variance component approaches on stimulus-generality, evaluation-generality, data preprocessing steps, and sensitivity to measurement error (a largely ignored issue). The correlation indices changed monotonically and nonlinearly with more repeated measures. Variance component analyses showed large variability in estimates from only two repeated measures, but stabilized with more measures. While there was general agreement among approaches, the correlation approach was problematic for certain stimulus types and evaluation dimensions. Our results suggest that variance component estimates are more reliable as long as one collects more than two repeated measures, which is not the current norm in psychological research, and can be implemented using mixed models with crossed random effects. Recommendations for analysis and interpretations are provided.

Genetic Influences on Eating and the Eating Disorders

2010 ◽  
pp. 102-122
Author(s):  
Tracey D. Wade
Keyword(s):  
Eating Disorders ◽  
Disordered Eating ◽  
Association Studies ◽  
Twin Studies ◽  
Genetic Influences ◽  
Shared Environment ◽  
Complex Nature ◽  
Current State ◽  
Increase Risk ◽  
Eating Practices

The current chapter reviews our progress in understanding how genes influence eating and eating disorders (EDs) by addressing the following areas: (1) how recognition of genetic influences on eating and EDs emerged; (2) the complex nature of genetic action; (3) what twin studies can tell us about genetic influences; and (4) the current state of linkage and association studies. It is concluded that genes are an important part of the explanatory framework for the etiology of EDs, with an important contribution of the shared environment to the development of cognition and attitudes that may initiate disordered eating practices, and a critical contribution of the environment in providing a context within which genetic risk is more likely to be expressed. We currently have a limited understanding of the specific genes that are implicated, and the ways in which genes and the environment work together to increase risk for disordered eating.

scholarly journals Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akio Onogi ◽  
Toshio Watanabe ◽  
Atsushi Ogino ◽  
Kazuhito Kurogi ◽  
Kenji Togashi
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Variance Components ◽  
Variance Component ◽  
Predictive Accuracy ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Additive Effects ◽  
Additive Variance ◽  
Additive Genetic Effects

Abstract Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

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