scholarly journals Estimation of Parental Effects Using Polygenic Scores

2021 ◽  
Author(s):  
Jared V. Balbona ◽  
Yongkang Kim ◽  
Matthew C. Keller
Keyword(s):  
Structural Equation ◽  
Behavioral Genetics ◽  
Environmental Influence ◽  
Relative Magnitude ◽  
Equation Modeling ◽  
Polygenic Score ◽  
Genetics Research ◽  
Polygenic Scores ◽  
Genetic And Environmental Factors ◽  
Familial Environment

AbstractOffspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents’ nontransmitted alleles, one can estimate the effect of “genetic nurture”—a type of passive gene–environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to (a) account for the influence of different types of assortative mating, (b) estimate the total variation due to additive genetic effects and their covariance with the familial environment (i.e., the full genetic nurture effect), and (c) model situations where a parental trait influences a different offspring trait. By utilizing structural equation modeling techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.

scholarly journals Estimation of parental effects using polygenic scores

2020 ◽  
Author(s):  
Jared Balbona ◽  
Yongkang Kim ◽  
Matthew C. Keller
Keyword(s):  
Behavioral Genetics ◽  
Environmental Influence ◽  
Relative Magnitude ◽  
Polygenic Score ◽  
Genetics Research ◽  
Gene Environment ◽  
Polygenic Scores ◽  
Genetic And Environmental Factors ◽  
Familial Environment ◽  
Additive Genetic Effects

AbstractOffspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents’ nontransmitted alleles, one can estimate the effect of “genetic nurture”— a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to a) account for the influence of assortative mating at both equilibrium and disequilibrium (after a single generation of assortment), and b) include measured parental phenotypes, allowing for the estimation of the total variation due to additive genetic effects and their covariance with the familial environment. By utilizing path analysis techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.

Distribution and Heritability of Recurrent Ear Infections

1997 ◽  
Vol 106 (8) ◽  
pp. 624-632 ◽  
Author(s):  
Kari J. Kvaerner ◽  
Jennifer R. Harris ◽  
Kristian Tambs ◽  
Per Magnus
Keyword(s):  
Structural Equation ◽  
Genetic Factors ◽  
Age Of Onset ◽  
Population Based ◽  
Equation Modeling ◽  
Relative Contribution ◽  
Genes And Environment ◽  
Ear Infections ◽  
The Mean ◽  
Familial Environment

The distribution of recurrent ear infections was obtained from a population-based sample of 2,750 pairs of Norwegian twins born between 1967 and 1974. The lifetime prevalence of self-reported recurrent ear infections was 8.9%, with a significant predominance of female cases. The mean age of onset was 4.2 years, with a gradual decrease in occurrence from 2 to 7 years of age. Among monozygotic pairs, the rate of tetrachoric correlation between co-twins was almost identical in males (0.73, SE 0.08) and females (0.74, SE 0.06), but among the dizygotic pairs the correlation was clearly higher in males (0.53, SE 0.12) than in females (0.20, SE 0.12). The value in the unlike-sexed dizygotic twins (0.25, SE 0.05) was intermediate to that of the like-sexed male and female dizygotic pairs. The relative contribution of genes and environment to variability in the predisposition to develop otitis media was estimated by means of structural equation modeling. Variation in liability to ear infections was mainly explained by additive genetic and dominance factors in females, for whom heritability was estimated at 74%. The remaining 26% of the variation in liability was explained by individual environmental factors. In males, 45% of the variation could be accounted for by genetic factors, 29% by common familial environment, and the remaining 26% by individual environmental effects.

scholarly journals Twin Studies on Obsessive–Compulsive Disorder: A Review

2005 ◽  
Vol 8 (5) ◽  
pp. 450-458 ◽  
Author(s):  
Daniël S. van Grootheest ◽  
Daniëlle C. Cath ◽  
Aartjan T. Beekman ◽  
Dorret I. Boomsma
Keyword(s):  
Obsessive Compulsive Disorder ◽  
Structural Equation ◽  
Twin Studies ◽  
Conclusive Evidence ◽  
Equation Modeling ◽  
Dimensional Approach ◽  
Obsessive Compulsive ◽  
Twin Research ◽  
Compulsive Disorder ◽  
Genetic And Environmental Factors

AbstractGenetic factors have historically been thought of as important in the development of obsessive–compulsive disorder (OCD). For the estimation of the relative importance of genetic and environmental factors, twin studies are an obvious approach. Twin studies of OCD have a long history, starting in 1929. In this review, over 70 years of twin research of OCD is presented, using four different approaches that represent the steps in the twin research of OCD from past to present. These steps include (1) case-studies of twins with OCD from the old literature; (2) twin studies of OCD using Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria; (3) twin studies of OCD using a dimensional approach, comparing resemblances in monozygotic and dizygotic twins; and (4) twin studies of OCD using a dimensional approach, analyzing the data with Structural Equation Modeling. It is concluded that only the studies using the last method have convincingly shown that, in children, obsessive–compulsive (OC) symptoms are heritable, with genetic influences in the range of 45% to 65%. In adults, studies are suggestive for a genetic influence on OC symptoms, ranging from 27% to 47%, but a large twin study using a bio- metrical approach with continuous data is still needed to provide conclusive evidence. Strategies for future twin studies of OCD are discussed.

scholarly journals Heritability of Type 2 Diabetes in the Washington State Twin Registry

2019 ◽  
Vol 22 (2) ◽  
pp. 95-98 ◽  
Author(s):  
Ally R. Avery ◽  
Glen E. Duncan
Keyword(s):  
Type 2 Diabetes ◽  
Environmental Factors ◽  
Structural Equation ◽  
Genetic Factors ◽  
Washington State ◽  
Equation Modeling ◽  
Twin Registry ◽  
Genetic And Environmental Factors ◽  
Adult Twins

AbstractApproximately 12% of U.S. adults have type 2 diabetes (T2D). Diagnosed T2D is caused by a combination of genetic and environmental factors including age and lifestyle. In adults 45 years and older, the Discordant Twin (DISCOTWIN) consortium of twin registries from Europe and Australia showed a moderate-to-high contribution of genetic factors of T2D with a pooled heritability of 72%. The purpose of this study was to investigate the contributions of genetic and environmental factors of T2D in twins 45 years and older in a U.S. twin cohort (Washington State Twin Registry, WSTR) and compare the estimates to the DISCOTWIN consortium. We also compared these estimates with twins under the age of 45. Data were obtained from 2692 monozygotic (MZ) and same-sex dizygotic (DZ) twin pairs over 45 and 4217 twin pairs under 45 who responded to the question ‘Has a doctor ever diagnosed you with (type 2) diabetes?’ Twin similarity was analyzed using both tetrachoric correlations and structural equation modeling. Overall, 9.4% of MZ and 14.7% of DZ twins over the age of 45 were discordant for T2D in the WSTR, compared to 5.1% of MZ and 8% of DZ twins in the DISCOTWIN consortium. Unlike the DISCOTWIN consortium in which heritability was 72%, heritability was only 52% in the WSTR. In twins under the age of 45, heritability did not contribute to the variance in T2D. In a U.S. sample of adult twins, environmental factors appear to be increasingly important in the development of T2D.

scholarly journals Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

2018 ◽  
Author(s):  
Andrew D. Grotzinger ◽  
Mijke Rhemtulla ◽  
Ronald de Vlaming ◽  
Stuart J. Ritchie ◽  
Travis T. Mallard ◽  
...  
Keyword(s):  
Complex Traits ◽  
Structural Equation ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Covariance Structure ◽  
Equation Modeling ◽  
Joint Analysis ◽  
Summary Statistics ◽  
Individual Traits ◽  
Polygenic Scores

AbstractMethods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced “atlases” of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

scholarly journals A Life Course Study of Genetic and Environmental Influences on Work Incapacity

2019 ◽  
Vol 23 (1) ◽  
pp. 16-22
Author(s):  
Karoline B. Seglem ◽  
Fartein A. Torvik ◽  
Espen Røysamb ◽  
Line C. Gjerde ◽  
Per Magnus ◽  
...  
Keyword(s):  
Structural Equation ◽  
Large Population ◽  
Environmental Influences ◽  
Population Based ◽  
National Registry ◽  
Equation Modeling ◽  
Aging Workforce ◽  
Total Proportion ◽  
Genetic And Environmental Factors ◽  
High Degree

AbstractWork incapacity is a major public health challenge and an economic burden to both society and individuals. Understanding the underlying causes is becoming ever more relevant as many countries face an aging workforce. We examined stability and change in genetic and environmental factors influencing work incapacity from age 18 until retirement, and sex differences in these effects. The large population-based sample comprised information from 28,759 twins followed for up to 23 years combined with high-quality national registry data. We measured work incapacity as the total proportion of potential workdays lost due to sickness absence, rehabilitation and disability benefits. Structural equation modeling with twin data indicated moderate genetic influences on work incapacity throughout life in both men and women, with a high degree of genetic stability from young to old adulthood. Environmental influences were mainly age-specific. Our results indicate that largely the same genetic factors influence individual differences in work incapacity throughout young, middle and older adulthood, despite major differences in degree of work incapacity and probable underlying medical causes.

Internet Users' Privacy Concerns and Beliefs About Government Surveillance

2010 ◽  
pp. 229-257
Author(s):  
Tamara Dinev ◽  
Massimo Bellotto ◽  
Paul Hart ◽  
Vincenzo Russo ◽  
Ilaria Serra ◽  
...  
Keyword(s):  
Structural Equation ◽  
The United States ◽  
Relative Magnitude ◽  
Equation Modeling ◽  
The Internet ◽  
Privacy Concerns ◽  
Internet Privacy ◽  
Perceived Need ◽  
Internet Users ◽  
Government Surveillance

The study examines differences in individual’s privacy concerns and beliefs about government surveillance in Italy and the United States. By incorporating aspects of multiple cultural theories, we argue that for both countries, the user’s decision to conduct e-commerce transactions on the Internet is influenced by privacy concerns, perceived need for government surveillance that would secure the Internet environment from fraud, crime and terrorism, and balancing concerns about government intrusion. An empirical model was tested using LISREL structural equation modeling and multigroup analysis. The results support the hypotheses with regard to direction and relative magnitude of the relationships. Italians exhibit lower Internet privacy concerns than individuals in the U.S., lower perceived need for government surveillance, and higher concerns about government intrusion. The relationships among the model constructs are also different across the two countries. Implications of the findings and directions for future work are discussed.

Sex differences in the genetic and environmental influences on the development of antisocial behavior

2002 ◽  
Vol 14 (2) ◽  
pp. 395-416 ◽  
Author(s):  
KRISTEN C. JACOBSON ◽  
CAROL A. PRESCOTT ◽  
KENNETH S. KENDLER
Keyword(s):  
Sex Differences ◽  
Antisocial Behavior ◽  
Structural Equation ◽  
Environmental Influence ◽  
Environmental Influences ◽  
Population Based ◽  
Equation Modeling ◽  
Retrospective Reports ◽  
Opposite Sex ◽  
Adult Twins

The present study uses a population-based sample of 6,806 adult twins from same-sex and opposite-sex twin pairs to examine sex differences in the underlying genetic and environmental architecture of the development of antisocial behavior (AB). Retrospective reports of AB during three different developmental periods were obtained: prior to age 15 years (childhood), age 15–17 years (adolescent), and age 18 years and older (adult). Structural equation modeling analyses revealed that there was no evidence for sex-specific genetic or sex-specific shared family environmental influences on the development of AB; that is, the types of genetic and environmental influence were similar for males and females. For both sexes, a model that allowed for genetic influences on adolescent and adult AB that were not shared with childhood AB fit better than a model with a single genetic factor. In contrast, shared environmental influences on adolescent and adult AB overlapped entirely with shared environmental influences on childhood AB. Genetic factors played a larger role in variation in childhood AB among females, whereas shared environmental factors played a larger role among males. However, heritability of AB increased from childhood to adolescence and adulthood for both sexes, and the magnitude of genetic and environmental influences on adolescent and adult AB was approximately equal across sex. We speculate that sex differences in timing of puberty may account for the earlier presence of genetic effects among females.

scholarly journals Measurement Error Correction of Genome-Wide Polygenic Scores in Prediction Samples

2017 ◽  
Author(s):  
Elliot M. Tucker-Drob
Keyword(s):  
Structural Equation Modeling ◽  
Error Correction ◽  
Instrumental Variable ◽  
Latent Variable ◽  
Structural Equation ◽  
Estimation Method ◽  
Equation Modeling ◽  
Variable Approach ◽  
Genome Wide ◽  
Polygenic Scores

Abstract/IntroductionDiPrete, Burik, & Koellinger (2017; http://dx.doi.org/10.1101/134197) propose using an instrumental variable (IV) framework to correct genome-wide polygenic scores (GPSs) for error, thereby producing disattenuated estimates of SNP heritability in predictions samples. They demonstrate their approach by producing two independent GPSs for Educational Attainment (“multiple indicators”) in a prediction sample (Health and Retirement Study; HRS) from independent sets of SNP regression weights, each computed from a different half of the discovery sample (EA2; Okbay et al. 2016), i.e. “by randomly splitting the GWAS sample that was used for [the GPS] construction.”Here, I elucidate how a structural equation modeling (SEM) framework that specifies true score variance in GPSs as a latent variable can be used to derive an equivalent correction to the IV approach proposed by DiPrete et al. (2017). This approach, which is rooted in a psychometric modeling tradition, has a number of advantages: (1) it formalizes the assumed data-generating model, (2) it estimates all parameters of interest in a single step, (3) is can be flexibly incorporated into a larger multivariate analysis (such as the “Genetic Instrumental Variable” approach proposed by DiPrete et al., 2017), (4) it can easily be adapted to relax assumptions (e.g. that the GPS indicators equally represent the true genetic factor score), and (5) it can easily be extended to include more than two GPS indicators. After describing how the multiple indicator approach to GPS correction can specified as a structural equation model, I demonstrate how a structural equation modeling approach can be used to correct GPSs for error using SNP heritability obtained using GREML or LD score regression to produce a correction that is equivalent to an approach recently proposed by Daniel Benjamin and colleagues. Finally, I briefly discuss what I view as some conceptual limitations surrounding the error correction approaches described, regardless of the estimation method implemented.

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