Literature DB >> 33118912

How nonshared environmental factors come to correlate with heredity.

Christopher R Beam1, Patrizia Pezzoli2, Jane Mendle3, S Alexandra Burt4, Michael C Neale5, Steven M Boker6, Pamela K Keel7, Kelly L Klump4.   

Abstract

Conventional longitudinal behavioral genetic models estimate the relative contribution of genetic and environmental factors to stability and change of traits and behaviors. Longitudinal models rarely explain the processes that generate observed differences between genetically and socially related individuals. We propose that exchanges between individuals and their environments (i.e., phenotype-environment effects) can explain the emergence of observed differences over time. Phenotype-environment models, however, would require violation of the independence assumption of standard behavioral genetic models; that is, uncorrelated genetic and environmental factors. We review how specification of phenotype-environment effects contributes to understanding observed changes in genetic variability over time and longitudinal correlations among nonshared environmental factors. We then provide an example using 30 days of positive and negative affect scores from an all-female sample of twins. Results demonstrate that the phenotype-environment effects explain how heritability estimates fluctuate as well as how nonshared environmental factors persist over time. We discuss possible mechanisms underlying change in gene-environment correlation over time, the advantages and challenges of including gene-environment correlation in longitudinal twin models, and recommendations for future research.

Entities:  

Keywords:  affect; developmental behavioral genetics; gene–environment interplay; longitudinal modeling; mood

Mesh:

Year:  2020        PMID: 33118912      PMCID: PMC8081739          DOI: 10.1017/S0954579420001017

Source DB:  PubMed          Journal:  Dev Psychopathol        ISSN: 0954-5794


  62 in total

Review 1.  Heritability estimates versus large environmental effects: the IQ paradox resolved.

Authors:  W T Dickens; J R Flynn
Journal:  Psychol Rev       Date:  2001-04       Impact factor: 8.934

2.  Mixed-effects variance components models for biometric family analyses.

Authors:  John J McArdle; Carol A Prescott
Journal:  Behav Genet       Date:  2005-09       Impact factor: 2.805

3.  Centering predictor variables in cross-sectional multilevel models: a new look at an old issue.

Authors:  Craig K Enders; Davood Tofighi
Journal:  Psychol Methods       Date:  2007-06

Review 4.  Genetic and environmental continuity in personality development: a meta-analysis.

Authors:  Daniel A Briley; Elliot M Tucker-Drob
Journal:  Psychol Bull       Date:  2014-06-23       Impact factor: 17.737

5.  Understanding "What Could Be": A Call for 'Experimental Behavioral Genetics'.

Authors:  S Alexandra Burt; Kathryn S Plaisance; David Z Hambrick
Journal:  Behav Genet       Date:  2018-08-13       Impact factor: 2.805

6.  Multivariate analysis and development behavioral genetics: developmental change as well as continuity.

Authors:  R Plomin
Journal:  Behav Genet       Date:  1986-01       Impact factor: 2.805

7.  Development and validation of brief measures of positive and negative affect: the PANAS scales.

Authors:  D Watson; L A Clark; A Tellegen
Journal:  J Pers Soc Psychol       Date:  1988-06

8.  Stability in symptoms of anxiety and depression as a function of genotype and environment: a longitudinal twin study from ages 3 to 63 years.

Authors:  M G Nivard; C V Dolan; K S Kendler; K J Kan; G Willemsen; C E M van Beijsterveldt; R J L Lindauer; J H D A van Beek; L M Geels; M Bartels; C M Middeldorp; D I Boomsma
Journal:  Psychol Med       Date:  2014-09-04       Impact factor: 7.723

9.  Meta-analysis of the heritability of human traits based on fifty years of twin studies.

Authors:  Tinca J C Polderman; Beben Benyamin; Christiaan A de Leeuw; Patrick F Sullivan; Arjen van Bochoven; Peter M Visscher; Danielle Posthuma
Journal:  Nat Genet       Date:  2015-05-18       Impact factor: 38.330

10.  GE covariance through phenotype to environment transmission: an assessment in longitudinal twin data and application to childhood anxiety.

Authors:  Conor V Dolan; Johanna M de Kort; Toos C E M van Beijsterveldt; Meike Bartels; Dorret I Boomsma
Journal:  Behav Genet       Date:  2014-05-01       Impact factor: 2.805

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  1 in total

1.  Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data.

Authors:  Brad Verhulst; Joshua N Pritikin; James Clifford; Elizabeth Prom-Wormley
Journal:  Behav Genet       Date:  2021-04-26       Impact factor: 2.805

  1 in total

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