The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Variance Components Models for Gene–Environment Interaction in Twin Analysis
Open accessAbstract
Gene-environment interaction is likely to be a common and important source of variation for complex behavioral traits. Often conceptualized as the genetic control of sensitivity to the environment, it can be incorporated in variance components twin analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. The model allows for one or more environmental moderator variables (that possibly interact with each other) that may i). be continuous or binary ii). differ between twins within a pair iii). interact with residual environmental as well as genetic effects iv) have nonlinear moderating properties v). show scalar (different magnitudes) or qualitative (different genes) interactions vi). be correlated with genetic effects acting upon the trait, to allow for a test of gene-environment interaction in the presence of gene-environment correlation. Aspects and applications of a class of models are explored by simulation, in the context of both individual differences twin analysis and, in a companion paper (Purcell & Sham, 2002) sibpair quantitative trait locus linkage analysis. As well as elucidating environmental pathways, consideration of gene-environment interaction in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.
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“In order to ensure that effect sizes were placed on a metric that was comparable across studies, we ensured that phenotypes and continuous SES measures were Z-transformed (based on the M and SD from data pooled across all twins and siblings of all relatedness types) prior to fitting the Purcell (2002) model.”
From Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence (2016) · cites this paper as Purcell (2002)
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Citations
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Hung, I., Stringaris, A. K., Liu, C., Ganiban, J. M., & Saudino, K. J. (2025) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2025.2484799
Gene-environment interplay and public policies
Muslimova, D., & Rietveld, N. (2025) · doi:10.48550/arxiv.2503.22441
“An important disadvantage of this approach is that the variance of Y may differ across the quantiles (Domingue et al., 2022; Purcell, 2002), making the effect sizes difficult to compare.”
Vinh, P. B., Verhulst, B., MAES, H. H. M., Dolan, C. V., & Neale, M. C. (2024) · Behavior Genetics · doi:10.1007/s10519-024-10207-9
Eichhorn, H., & Mönkediek, B. (2024) · Journal of Family Research · doi:10.20377/jfr-988
“However, different model extensions allow testing for gene-environment interplay, such as gene-environment interaction models (Purcell, 2002).”
BackgroundMethodRoudbar, M. A., Vahedi, S. M., Jin, J., Jahangiri, M., Lanjanian, H., Habibi, D., Masjoudi, S., Riahi, P., Fateh, S. T., Neshati, F., et al. (2024) · Human Genomics · doi:10.1186/s40246-024-00669-7 · Cited by 1
“Moreover, twin studies might have overestimated heritability due to genetic interactions [44], gene-environment interactions [45], or violation of environment assumptions [46].”
BackgroundVinh, P., Verhulst, B., Dolan, C. V., Neale, M. C., & Maes, H. (2024) · Research Square · doi:10.21203/rs.3.rs-4576809/v1
“Genotype-by-environment interactions may be tested at the observed level by including interacting variables (Purcell, 2002), ideally exogenous causes of the traits to avoid collider bias.”
BackgroundThe associations of child screen time with psychiatric problems: the role of genetic confoundingPreprint
Zhang, Y., Choi, K., Delaney, S., Ge, T., Pingault, J., & Tiemeier, H. (2023) · medRxiv · doi:10.1101/2023.03.21.23286931
“In contrast, h twin may overestimate heritability due to genetic interactions, 25 gene-environment interactions, 26 or by violating the equal environment assumption in twin studies.”
BackgroundBehavior Genetics Association 50th Annual Meeting Abstracts
van Dongen, J., Hagenbeek, F. A., Hannon, E. J., Suderman, M., Roetman, P. J., Sugden, K., Chiocchetti, A. G., Ismail, K., Mulder, R. H., Hafferty, J. D., et al. (2020) · Behavior Genetics · doi:10.1007/s10519-020-10018-8 · Cited by 2
“Method We computed moderation models of G 9 E, CxE and ExE (Purcell 2002) to assess how daycare attendance moderates genetic, shared environment and unique environment contributions to school readiness.”
MethodNick Martin’s Contribution to GxE Research
Colodro‐Conde, L., & Couvy‐Duchesne, B. (2020) · Twin Research and Human Genetics · doi:10.1017/thg.2020.35
“By using the GxE twin model proposed by Purcell (2002), the authors showed a significant increase in genetic and environmental variance in gambling as a function of SES.”
MethodResult(2020) · Presses Universitaires de France eBooks · doi:10.3917/puf.lagra.2020.01.0431
Genetic and Environmental Influences on Cognitive Abilities in Extreme Poverty
Hur, Y., & Bates, T. C. (2019) · Twin Research and Human Genetics · doi:10.1017/thg.2019.92 · Cited by 11
“We next moved to test evidence of SES interaction effects (Purcell, 2002).”
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Raihani Wahdah, G. R. (2018) · International Journal of Biosciences · doi:10.12692/ijb/13.2.42-53
Behavior Genetics: From Heritability to Gene Finding
Nivard, M., Dolan, C., Middeldorp, C., & Boomsma, D. (2017) · doi:10.1017/9781107415782.015
“Purcell (2002) developed a general model to investigate G×E interaction in the presence of gene–environment correlation, i.e., where moderation is by a continuously distributed moderator (G×E), which is itself possibly subject to genetic and environmental effects.”
BackgroundGenetic and Environmental Influences on Individual Differences in Sleep Duration During Adolescence
Velde, S. J. T., van der Aa, N., Boomsma, D. I., Someren, E. J. W. V., de Geus, E. J. C., Brug, J., & Bartels, M. (2013) · Twin Research and Human Genetics · doi:10.1017/thg.2013.74 · Cited by 11
“To assess whether there was evidence for changes in the genetic architecture of sleep duration throughout adolescence, a moderator model was used (Purcell, 2002).”
Cornes, B., Medland, S., Lind, P., Nyholt, D., Montgomery, G., & Martin, N. (2009) · Twin Research and Human Genetics · doi:10.1375/twin.12.3.276
“Thus, the path coefficients for each variance component are represented by the equation: Any variable which has a moderating, or interactive, effect on a trait may also have a mediating, or main effect (Purcell, 2002).”
InfluentialKaksos- ja perhetutkimukset geneettisten ja ympäristötekijöiden vaikutuksen arvioimisessa
Silventoinen, K., & Kaprio, J. (2008) · Sosiaalilääketieteellinen Aikakauslehti · doi:10.23990/sa.715
“Purcell 2002).”
Jacobson, K. C. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa245 · Cited by 1
Adversity Magnifies Genetic Influence on Savings BehaviorPreprint
Li, W., Yi, J., & Zhang, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4821061
Minică, C. C., Neale, M. C., Boomsma, D. I., & van Dongen, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820951-6.00007-7
Hamdi, N. R., South, S. C., & Krueger, R. F. (2020) · Springer eBooks · doi:10.1007/978-1-0716-0933-0_12 · Cited by 1
Horwitz, B. N., Cao, M., & Lee, S. (2020) · Springer eBooks · doi:10.1007/978-1-0716-0933-0_11
Moscati, A. (2017) · VCU Scholars Compass (Virginia Commonwealth University) · doi:10.25772/4mmm-kt65
“38 for MD heritability (81); the twin estimate should represent an upper bound on the range of snp-based heritability for a given trait, due to including, in addition to all additive genetic effects of any magnitude (101), any unmodeled dominance or epistatic effects (102), interaction between additive genetic effects…”
BackgroundHuman Vulnerability for Brain and Cognitive Traits
Geary, D. C. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801562-9.00007-7
Hahn, E. (2013) · Publications of the UdS (Saarland University) · doi:10.22028/d291-23359
Bates, T. C. (2013) · Princeton University Press eBooks · doi:10.2307/j.ctt3fgxz9.14
Developmental Behavioral Genetics and Education
Spinath, F. M., Spinath, B., & Borkenau, P. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.92009-9
Metadata
- Authors
- Purcell, S.
- Affiliations
- King's College London (GB)
- Corresponding author
- Purcell, Shaun
- ORCID
- Purcell, Shaun
- Venue
- Twin Research
- Publisher
- Cambridge University Press (CUP)
- Published
- 2002-12-01
- Volume
- 5
- Issue
- 6
- Pages
- 554-571
- ISSN
- 1369-0523 (ISSN-L)
- 0000-0000
- OpenAlex
- W2052208159
- Cited by
- 924
- References
- 0
- Citations by year
- 2026: 20 · 2025: 26 · 2024: 27 · 2023: 39 · 2022: 52 · 2021: 40 · 2020: 50 · 2019: 36 · 2018: 37 · 2017: 54 · 2016: 49 · 2015: 60 · 2014: 56 · 2013: 46 · 2012: 45
- FWCI
- 6.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Twin Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- National Eye Institute (EY12562, R01EY012562)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Assisted Reproductive Technology and Twin Pregnancy
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Keywords
- gene-environment interaction, variance component models, gene-environment correlation, environmental moderators, individual differences, quantitative trait loci, gene mapping, nonlinear interactions, environmental pathways
- MeSH terms
- Computer Simulation, Environment, Genetics, Behavioral, Humans, Models, Genetic, Twins
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