Methodology for Twin Studies of Aging

2019 ◽  
Author(s):  
Michael J. Lyons ◽  
Chandra A. Reynolds ◽  
William S. Kremen ◽  
Carol E. Franz
Keyword(s):  
Structural Equation ◽  
Molecular Genetic ◽  
Monozygotic Twins ◽  
Twin Studies ◽  
Environmental Exposures ◽  
Genetic Influences ◽  
Equation Modeling ◽  
Environment Interaction ◽  
Gene Environment ◽  
Correlation Prior

The rapidly increasing number of people age 65 and older around the world has important implications for public health and social policy, making it imperative to understand the factors that influence the aging process. Twin studies can provide information that addresses critical questions about aging. Twin studies capitalize on a naturally occurring experiment in which there are some pairs of individuals who are born together and share 100% of their segregating genes (monozygotic twins) and some pairs that share approximately 50% (dizygotic twins). Twins can shed light on the relative influence of genes and environmental factors on various characteristics at various times during the life course and whether the same or different genetic influences are operating at different times. Twin studies can investigate whether characteristics that co-occur reflect overlapping genetic or environmental determinants. Discordant twin pairs provide an opportunity for a unique and powerful case-control study. There are numerous methodological issues to consider in twin studies of aging, such as the representativeness of twins and the assumption that the environment does not promote greater similarity within monozygotic pairs than dizygotic pairs. Studies of aging using twins may include many different types of measures, such as cognitive, psychosocial, biomarkers, and neuroimaging. Sophisticated statistical techniques have been developed to analyze data from twin studies. Structural equation modeling has proven to be especially useful. Several issues, such as assessing change and dealing with missing data, are particularly salient in studies of aging and there are a number of approaches that have been implemented in twin studies. Twins lend themselves very well to investigating whether genes influence one’s sensitivity to environmental exposures (gene-environment interaction) and whether genes influence the likelihood that an individual will experience certain environmental exposures (gene-environment correlation). Prior to the advent of modern molecular genetics, twin studies were the most important source of information about genetic influences. Dramatic advances in molecular genetic technology hold the promise of providing great insight into genetic influences, but these approaches complement rather than supplant twin studies. Moreover, there is a growing trend toward integrating molecular genetic methods into twin studies.

Genetics of Personality Disorders

2018 ◽  
Author(s):  
Ted Reichborn-Kjennerud ◽  
Kenneth S. Kendler
Keyword(s):  
Personality Disorders ◽  
Molecular Genetic ◽  
Twin Studies ◽  
Genetic Influences ◽  
Controversial Issues ◽  
Future Directions ◽  
Normal Personality ◽  
Diagnostic And Statistical Manual ◽  
Gene Environment ◽  
Genetic Contributions

This chapter reviews the evidence for genetic contributions to the etiology of personality disorders (PDs) as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM; 5th ed.). This approach and some of the controversial issues associated with its development are briefly described in the first section. The second section evaluates the evidence for genetic influence on DSM PDs from family and twin studies using quantitative genetic methods. Studies that move beyond individual PDs are also reviewed, together with studies on the extent to which common genetic factors influence PDs and normal personality traits and PDs and pathological personality trait domains. Stability of genetic influences on PDs over time are also examined. Molecular genetic studies are reviewed in the third section. The fourth section deals with gene environment interplay, and the final section discusses future directions in the exploration of genetic influences on PDs.

Genetic Influences on Eating Disorders

2017 ◽  
Author(s):  
Tracey D. Wade ◽  
Cynthia Bulik
Keyword(s):  
Eating Disorders ◽  
Molecular Genetic ◽  
Critical Role ◽  
Twin Studies ◽  
Genetic Influences ◽  
Shared Environment ◽  
Nonshared Environment ◽  
Gene Environment ◽  
Increase Risk ◽  
Eating Practices

The current chapter reviews our progress in understanding how genes influence eating disorders by addressing the following areas: (1) how recognition of genetic influences on eating disorders emerged; (2) the complexities of gene environment interplay; (3) what twin studies can tell us about gene environment interplay, and (4) the current state of molecular genetic studies. It is concluded that both genes and nonshared environment play a critical role in the explanatory framework for the etiology of eating disorders. Shared environment is likely to contribute to the development of cognition and attitudes that may initiate disordered eating practices. Researchers are on the cusp of identifying specific genes that are implicated, and explication of the manner in which genes and the environment work together to increase risk for eating disorders hinges on the collection of larger samples.

scholarly journals umx: Twin and Path-Based Structural Equation Modeling in R

2019 ◽  
Vol 22 (1) ◽  
pp. 27-41 ◽  
Author(s):  
Timothy C. Bates ◽  
Hermine Maes ◽  
Michael C. Neale
Keyword(s):  
Structural Equation Modeling ◽  
Ordinal Data ◽  
Structural Equation ◽  
Behavior Genetics ◽  
Model Specification ◽  
Equation Modeling ◽  
Environment Interaction ◽  
Gene Environment ◽  
Twin Models ◽  
Pathway Models

AbstractStructural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.

Gene–environment interplay in the etiology of psychosis

2018 ◽  
Vol 48 (12) ◽  
pp. 1925-1936 ◽  
Author(s):  
Alyson Zwicker ◽  
Eileen M. Denovan-Wright ◽  
Rudolf Uher
Keyword(s):  
Environmental Factors ◽  
Genetic Risk ◽  
Environmental Exposures ◽  
Response To Treatment ◽  
Human Potential ◽  
Specific Gene ◽  
Environment Interaction ◽  
Gene Environment ◽  
Increased Risk ◽  
The Impact

AbstractSchizophrenia and other types of psychosis incur suffering, high health care costs and loss of human potential, due to the combination of early onset and poor response to treatment. Our ability to prevent or cure psychosis depends on knowledge of causal mechanisms. Molecular genetic studies show that thousands of common and rare variants contribute to the genetic risk for psychosis. Epidemiological studies have identified many environmental factors associated with increased risk of psychosis. However, no single genetic or environmental factor is sufficient to cause psychosis on its own. The risk of developing psychosis increases with the accumulation of many genetic risk variants and exposures to multiple adverse environmental factors. Additionally, the impact of environmental exposures likely depends on genetic factors, through gene–environment interactions. Only a few specific gene–environment combinations that lead to increased risk of psychosis have been identified to date. An example of replicable gene–environment interaction is a common polymorphism in theAKT1gene that makes its carriers sensitive to developing psychosis with regular cannabis use. A synthesis of results from twin studies, molecular genetics, and epidemiological research outlines the many genetic and environmental factors contributing to psychosis. The interplay between these factors needs to be considered to draw a complete picture of etiology. To reach a more complete explanation of psychosis that can inform preventive strategies, future research should focus on longitudinal assessments of multiple environmental exposures within large, genotyped cohorts beginning early in life.

Abstract MP11: Systematic Discovery Of Thousands Of Gene-Environment Interactions For Cardiometabolic Serum Biomarkers

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Kenneth E Westerman
Keyword(s):  
Genetic Variants ◽  
Cardiometabolic Risk ◽  
Interaction Analysis ◽  
Environmental Exposures ◽  
Cardiometabolic Health ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
A Genome ◽  
The Mean

Background: Gene-environment interaction (GEI) analysis enables us to understand how genetic variants modify the effects of environmental exposures on cardiometabolic risk factors, providing a foundation for genome-based precision medicine. Ideally, these interactions could be mapped comprehensively across all measured genetic variants, exposures, and outcomes, but this approach is computationally intensive and statistically underpowered. Recent studies have shown that variance-quantitative trait loci (vQTLs), or genetic variants that associate with differential variance of an outcome, are substantially enriched for underlying GEIs. Here, we sought to first identify vQTLs for cardiometabolic traits, then use this smaller genetic search space to uncover novel gene-environment interactions across thousands of environmental exposures. Methods: A two-stage, multi-ancestry analysis was conducted in 355,790 unrelated participants from the UK Biobank. First, we performed a genome-wide vQTL scan for each of 20 serum metabolic biomarkers, including but not limited to lipids, lipoproteins, and glycemic measures. This scan used Levene’s test to identify genetic markers whose genotypes are associated with the variance, rather than the mean, of the biomarker. Next, we collected over 2000 variables corresponding to socioeconomic, dietary, lifestyle, and clinical exposures, and conducted an interaction analysis for each combination of exposure and vQTL-biomarker pair. For each stage, the analysis was initially stratified by ancestry then meta-analyzed to generate the primary set of results. Results: vQTLs were identified at 514 independent regions in the genome, with most of these genetic variants already known to affect the mean biomarker level. In the subsequent gene-environment interaction analysis, we found 2,162 significant interactions passing a stringent significance threshold adjusted for multiple testing ( p < 0.05 / 578 vQTL-biomarker pairs / 2140 exposures = 4х10 -8 ). Some of these expanded on existing findings; for example, genetic marker rs2393775 in the HNF1A gene interacted with education level (as a proxy for socioeconomic status) to influence hsCRP ( p = 1.3х10 -10 ), building on a previous finding that HNF1A variants modify the effect of perceived stress on cardiovascular outcomes. Others highlighted novel biology, such as an interaction between variants near the fatty liver-associated gene TM6SF2 and oily fish intake on total and LDL-cholesterol levels ( p = 6.6х10 -9 ). Conclusions: Our systematic GEI discovery effort identified thousands of interactions that may impact cardiometabolic risk, both expanding on previous research and identifying novel biological mechanisms. This catalog of vQTLs and interactions can inform future mechanistic studies and provides a knowledge base for genome-centered precision approaches to cardiometabolic health.

Introduction and Overview of Statistical Approaches to Gene × Environment Interactions for Complex Phenotypes

2016 ◽  
Author(s):  
Michael Windle
Keyword(s):  
Molecular Genetic ◽  
Genetic Influences ◽  
Biological Pathway ◽  
Substantial Evidence ◽  
Genetic Studies ◽  
Individualized Intervention ◽  
Regulatory Processes ◽  
Complex Phenotypes ◽  
Gene Environment ◽  
Statistical Approaches

This chapter provides an introduction and overview of important issues that served as motivations for this book. For many complex phenotypes (e.g., depression, diabetes, obesity, substance use), there is substantial evidence that while genetic influences are important, so are environmental influences; moreover, there is substantial evidence from both behavior genetic studies (e.g., twin and adoptee studies) and molecular genetic studies (both human and infrahuman) that genes commonly interact with environmental factors in predicting complex phenotypes. The fields of genomics and other –omics (e.g., proteomics, metabolomics) provide exciting opportunities to advance science and foster the goals of public health and a more individualized intervention approach (e.g., precision medicine). The goals of these more individualized approaches would benefit greatly not only by advances in genomics and other –omics, but also by incorporating information both on environments and their interactions with genomic and other biological material and regulatory processes (e.g., environmental signal to biological pathway responses). Such findings would thereby offer more flexible guidance to a broader range of prevention, intervention, and treatment targets, and facilitate more tailored programs based on a fuller complement of G and E influences.

scholarly journals Evidence for Shared Genetic Influences on Self-Reported ADHD and Autistic Symptoms in Young Adult Australian Twins

2008 ◽  
Vol 11 (6) ◽  
pp. 579-585 ◽  
Author(s):  
Angela M. Reiersen ◽  
John N. Constantino ◽  
Marisa Grimmer ◽  
Nicholas. G. Martin ◽  
Richard D. Todd
Keyword(s):  
Young Adult ◽  
Structural Equation ◽  
Self Report ◽  
Genetic Influences ◽  
Equation Modeling ◽  
Phenotypic Correlation ◽  
Social Responsiveness ◽  
Adhd Symptoms ◽  
Autistic Symptoms ◽  
Shared Genetic

AbstractRecent clinic-based and population-based studies have shown evidence of association between ADHD and autistic symptoms in children and adolescents as well as evidence for genetic overlap between these disorders. The objective of the current study was to confirm the association between autistic and ADHD symptoms in a young adult twin sample assessed by self-report, and investigate whether shared genetic and/or environmental factors can explain the association. We performed twin-based structural equation modeling using self-report data from 11 Social Responsiveness Scale (SRS) items and 12 DSM-IV ADHD inattentive and impulsive symptom items obtained from 674 young adult Australian twins. Phenotypic correlation between autistic and ADHD symptoms was moderate. The most parsimonious univariate models for SRS and ADHD included additive genetic effects and unique environmental effects, without sex differences. ADHD and autistic traits were both moderately heritable. In a bivariate model, genetic correlation (rg) between SRS and ADHD was 0.72. Our results suggest that in young adults, a substantial proportion of the genetic influences on self-reported autistic and ADHD symptoms may be shared between the two disorders.

scholarly journals Genetic moderation of environmental risk for depression and anxiety in adolescent girls

2001 ◽  
Vol 179 (2) ◽  
pp. 116-121 ◽  
Author(s):  
Judy Silberg ◽  
Michael Rutter ◽  
Michael Neale ◽  
Lindon Eaves
Keyword(s):  
Life Events ◽  
Structural Equation ◽  
Genetic Factors ◽  
Negative Life Events ◽  
Genetic Effect ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Significant Gene ◽  
Independent Life

BackgroundThere is huge individual variation in people's response to negative life events.AimsTo test the hypothesis that genetic factors moderate susceptibility to the environmentally mediated risks associated with negative life events.MethodThe Virginia Twin Study of Adolescent Behavioral Development (VTSABD) was used to study the effects of independent life events (assessed from maternal interview) on depression/anxiety (assessed from child interview) in 184 same-gender female twin pairs, aged 14–17 years, measured on two occasions.ResultsThere was no genetic effect on the independent negative life events studied. A significant gene–environment interaction was found using structural equation modelling. There was no effect of independent life events on adolescents' depression in the absence of parental emotional disorder, but a significant effect in its presence.ConclusionsThere is an environmentally mediated effect of life events on depression/anxiety. Genetic factors play a significant role in individual differences in susceptibility to these environmentally mediated risks.

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