{ "id": "epqqrtxme5", "slug": "twin-model", "title": "Twin model", "updated": "2026-10-15", "topic_path": [ { "id": "life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/topics/life" }, { "id": "life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health" }, { "id": "life.health.public", "label": "Public health and healthcare", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.public" }, { "id": "life.health.public.epidemiology-discipline", "label": "Epidemiology as a discipline", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.public.epidemiology-discipline" } ], "geo": [ { "id": "geo.nongeo.t1800.life.health", "label": "Non-geographic · 1800 to 1945: Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1800.life.health", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1800", "label": "Non-geographic · 1800 to 1945", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1800" }, { "id": "geo.nongeo.t1800.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1800.life" }, { "id": "geo.nongeo.t1800.life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1800.life.health" } ] }, { "id": "geo.nongeo.t1946.life.health.public.epidemiology-discipline", "label": "Non-geographic · 1946 to 2000: Epidemiology as a discipline", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.public.epidemiology-discipline", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1946", "label": "Non-geographic · 1946 to 2000", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946" }, { "id": "geo.nongeo.t1946.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life" }, { "id": "geo.nongeo.t1946.life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health" }, { "id": "geo.nongeo.t1946.life.health.public", "label": "Public health and healthcare", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.public" }, { "id": "geo.nongeo.t1946.life.health.public.epidemiology-discipline", "label": "Epidemiology as a discipline", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.public.epidemiology-discipline" } ] } ], "excerpt": "The twin model is a study design in genetics and epidemiology that compares identical and fraternal twins to estimate how much genes and environment shape traits and disease risk.", "snippet": "The twin model is a study design in genetics and epidemiology that compares identical and fraternal twins to estimate how much genes and environment shape traits and disease risk.", "node": "life.health.public.epidemiology-discipline", "markdown": "# Twin model\n\nThe twin model is a study design in human genetics and epidemiology that compares monozygotic (MZ) and dizygotic (DZ) twins to estimate how much of the variation in a trait or disease risk comes from genes and how much from the environment. Because MZ twins share all of their genes, the difference in their resemblance indexes genetic influence.[1](https://perspectivesinmedicine.cshlp.org/content/11/6/a039552.full) The classical design decomposes phenotypic variance into genetic and environmental components on the basis of the biometrical model \\( V_{P} = V_{G} + V_{E} \\).[2](https://pure.rug.nl/ws/files/807660058/Maximizing_the_value_of_twin_studies_in_health_and_behaviour.pdf) A meta-analysis of virtually all published twin studies of complex traits, covering 17,804 traits from 2,748 publications and 14,558,903 partly dependent twin pairs, reported an average heritability of 49% across traits.[3](https://www.nature.com/articles/ng.3285)\n\n| Key fact | Value |\n|---|---|\n| Traits covered by the fifty-year twin meta-analysis | 17,804 traits, 2,748 publications, 14,558,903 twin pairs[3](https://www.nature.com/articles/ng.3285) |\n| Average reported heritability across all traits | 49%; 69% of traits fit a purely additive genetic model[3](https://www.nature.com/articles/ng.3285) |\n| Falconer's heritability estimate | \\( h^{2} = 2(r_{\\mathrm{MZ}} - r_{\\mathrm{DZ}}) \\)[4](https://academic.oup.com/bib/article-pdf/3/2/119/439480/119.pdf) |\n| Probandwise concordance | \\( 2C/(2C+D) \\), C concordant and D discordant affected pairs[5](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf) |\n| Example heritabilities | Schizophrenia 0.81, autism spectrum disorders 0.71, asthma 0.60, major depression 0.37[5](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf) |\n| Example MZ vs DZ probandwise concordance | Type 1 diabetes 42.9% vs 7.4%; schizophrenia 40.8% vs 5.3%[5](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf) |\n| Population-based twin registers | Denmark, Norway, Sweden, Finland, Australia, the Netherlands, the USA, and the UK[4](https://academic.oup.com/bib/article-pdf/3/2/119/439480/119.pdf) |\n\n## How it works\n\nThe classical twin method decomposes total phenotypic variance P into additive genetic (A), dominance genetic (D), common or shared environmental (C), and unique environmental (E) components, with \\( V_{P} = V_{A} + V_{D} + V_{C} + V_{E} \\).[4](https://academic.oup.com/bib/article-pdf/3/2/119/439480/119.pdf) MZ twins correlate 1 for A and D, DZ twins correlate 1/2 for A and 1/4 for D, both twin types correlate 1 for C, and E is uncorrelated, because it is by definition the part of the environment that makes co-twins different.[4](https://academic.oup.com/bib/article-pdf/3/2/119/439480/119.pdf) In the simplest ACE formulation the expected twin correlations are \\( r_{\\mathrm{MZ}} = A + C \\) and \\( r_{\\mathrm{DZ}} = 0.5A + C \\), which are solved as \\( A = 2(r_{\\mathrm{MZ}} - r_{\\mathrm{DZ}}) \\), \\( C = r_{\\mathrm{MZ}} - A \\), and \\( E = 1 - r_{\\mathrm{MZ}} \\), where A and C are standardized variance proportions.[1](https://perspectivesinmedicine.cshlp.org/content/11/6/a039552.full) The same logic extends to full covariance expectations of \\( V_{A} + V_{D} + V_{C} \\) for MZ and \\( 0.5V_{A} + 0.25V_{D} + V_{C} \\) for DZ pairs, which serve as input for genetic structural equation modeling (SEM) fitted by maximum likelihood.[5](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf)\n\nFor dichotomous traits such as disease, the design uses concordance. The probandwise concordance rate is the probability that the co-twin of an affected proband is also affected, calculated as \\( 2C/(2C+D) \\), where C is the number of concordant affected twin pairs and D the number of discordant pairs; the older pairwise rate is now considered obsolete because it cannot be interpreted without knowing the intensity of ascertainment.[5](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf) An MZ-to-DZ concordance ratio of about 2:1 indicates additive genetic influence.[4](https://academic.oup.com/bib/article-pdf/3/2/119/439480/119.pdf)\n\nThe comparison identifies genetic influence only under assumptions. The equal environments assumption, that trait-relevant environments are shared to the same extent by MZ and DZ pairs, is described as the most basic assumption of the method and has been much debated.[4](https://academic.oup.com/bib/article-pdf/3/2/119/439480/119.pdf) The design also assumes random mating: assortative mating increases the genetic similarity of DZ twins, which if unaccounted for inflates the C estimate and lowers the A estimate, a bias that can be addressed by adding parents or spouses of twins to the model.[6](https://publications.qimrberghofer.edu.au/attachment/download/969)\n\n## How it is done\n\nTwin correlations are estimated for MZ and DZ groups, and variance components models are fitted using maximum likelihood estimation or a structural equations approach, fitting covariance structure models to the MZ and DZ groups simultaneously; SEM has replaced Falconer's simple algebra because it can test sex differences and handle multivariate data.[4](https://academic.oup.com/bib/article-pdf/3/2/119/439480/119.pdf) For ordinal or dichotomous variables, a liability-threshold model assumes an underlying normally distributed continuum of liability (for example, to depression) and estimates twin correlations on that liability scale.[6](https://publications.qimrberghofer.edu.au/attachment/download/969) The equal environments assumption itself can be tested empirically, for example by comparing perceived zygosity in pairs mistaken about their own zygosity; tests of this kind have shown the assumption to be generally valid.[6](https://publications.qimrberghofer.edu.au/attachment/download/969) Much of the modern evidence base comes from population-based twin registers, which recruit twins from national populations and link them to health records; registry-linked methods have provided population-based heritability estimates on samples as large as 44,000 twin pairs.[5](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf)\n\n## Origin\n\nScientific study of twins goes back to [Francis Galton](https://www.edgepedia.org/francis-galton), whose essay \"The History of Twins, as a Criterion of the Relative Powers of Nature and Nurture\" asked whether initially similar twins subsequently grew unlike and what causes produced the dissimilarity.[7](https://www.galton.org/essays/1870-1879/galton-1875-history-twins.pdf) The paper is recorded as published in The Journal of the Anthropological Institute of Great Britain and Ireland in 1876.[8](https://doi.org/10.2307/2840900) Galton, however, did not propose the comparison between identical and fraternal twin resemblance that is the essence of the modern method.[9](https://genepi.qimr.edu.au/contents/p/staff/1990RendeBehavGen1990.pdf) [Characters](https://www.edgepedia.org/characters) nearly always present in both members of identical pairs but rarely in both fraternal pairs are hereditary.[5](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf) Descriptions of the modern method appeared in an article and in a book.[9](https://genepi.qimr.edu.au/contents/p/staff/1990RendeBehavGen1990.pdf) An early landmark of the method applied to psychology was the 1937 book Twins: A Study of Heredity and [Environment](https://www.edgepedia.org/environment) by Horatio H. Newman, Frank N. Freeman, and Karl J. Holzinger, published by the University of Chicago Press.[10](https://doi.org/10.2307/2279415) Who really invented the method remains an unsettled question in specialist historical scholarship.[11](https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/early-research-on-human-genetics-using-the-twin-method-who-really-invented-the-method/A13A85A7EC5AEFEA8BFD947262933C48)\n\n## Variants\n\nThe classical design extends in several directions. Extended twin family designs add relatives beyond twins to separate additive from non-additive genetic variance and to detect assortative mating; three named designs are the Nuclear Twin Family Design, the Stealth design, and the Cascade design, the last described in Twin Research and Human Genetics in 2009.[12](https://pmc.ncbi.nlm.nih.gov/articles/PMC3228846/) • [13](https://doi.org/10.1375/twin.12.1.8) The classical model can also be extended to model causal relations between exposures and traits, discordant pairs, gene-environment interaction (G×E) conceptualized as moderation, gene-environment correlation via measured genetic or environmental variables, longitudinal data, and additional family members.[2](https://pure.rug.nl/ws/files/807660058/Maximizing_the_value_of_twin_studies_in_health_and_behaviour.pdf) A four-parameter ACDE model cannot be identified from ordinary MZ and DZ twin data alone, since the data provide too few statistics for four parameters; estimating ACDE generally requires additional relatives or other identifying information, so standard twin-only analyses usually fit ACE or ADE as alternatives.[14](https://www.diva-portal.org/smash/get/diva2:1585620/FULLTEXT01.pdf)\n\n## Applications\n\nThe fifty-year meta-analysis reported an overall heritability of 49% across all traits, and found that for 69% of traits the observed twin correlations fit a parsimonious model in which twin resemblance is solely due to additive genetic variation; the data were inconsistent with substantial influences from shared environment or non-additive genetic variation.[3](https://www.nature.com/articles/ng.3285) Disease-specific twin heritabilities include schizophrenia 0.81 and major depression 0.37 (both meta-analyses), autism spectrum disorders 0.71, bone mineral density 0.60 to 0.80, asthma 0.60, and total brain volume 0.66 to 0.97.[5](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf) Model choice changes the numbers: on BMI data from the UK and Australia, an ACDE model gave stable significant estimates of BMI heredity of 27% in the UK and 28% in Australia, whereas ACE and ADE often gave negative or non-significant estimates and suggested much higher values.[14](https://www.diva-portal.org/smash/get/diva2:1585620/FULLTEXT01.pdf)\n\n## Limitations and alternatives\n\nThe equal environments assumption is the main contested premise. Tests based on mistaken zygosity and on measured environments have generally supported it, and a review concludes it holds for most phenotypes.[6](https://publications.qimrberghofer.edu.au/attachment/download/969) • [2](https://pure.rug.nl/ws/files/807660058/Maximizing_the_value_of_twin_studies_in_health_and_behaviour.pdf) One identified threat is chorionicity: the prenatal environment differs systematically between monochorionic and dichorionic pregnancies, and chorionicity is a specific and crucial environmental influence on many human traits that may violate the assumption that trait-relevant environments are equal for MZ and DZ pairs.[15](https://pmc.ncbi.nlm.nih.gov/articles/PMC4858569/)\n\nA 2025 methodological critique argues that although twin SEM uses maximum likelihood estimation with good statistical properties, it rests on the strong assumption that \\( 2(r_{\\mathrm{MZ}} - r_{\\mathrm{DZ}}) \\) estimates A, which is overestimated by \\( 1.5D \\) if non-additive effects are incorrectly ignored; because individual values of A and D cannot actually be measured from twin correlation data, SEM frameworks tend to overestimate A while underestimating D, and \"\\( H^{2} \\) twin\" is a more appropriate label than \"\\( h^{2} \\)\".[16](https://www.nature.com/articles/s10038-025-01427-w) Genomic comparisons show why this matters. GREML-based whole-genome-sequencing heritability estimates are about 0.7 for height and 0.3 for BMI; the twin heritability for height of about 0.8 is almost fully recovered by its narrow-sense heritability, while the BMI twin heritability of about 0.7 is not.[16](https://www.nature.com/articles/s10038-025-01427-w) An identity-by-descent study using full-sibling pairs estimated heritability of height at about 0.75 and BMI at about 0.55, while IBD analysis in more distant relatives gave BMI about 0.3, a pattern the authors suggest reflects non-additive variance.[16](https://www.nature.com/articles/s10038-025-01427-w) The gap between twin heritability and the effects of variants identified by GWAS is the missing heritability problem.[17](https://journals.plos.org/plosgenetics/article?id=10.1371%2Fjournal.pgen.1008222) On the modeling side, hierarchical modeling has been proposed to improve the accuracy and precision of twin heritability estimation by reassessing the measurement error assumption in the popular ACE formulation.[18](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1522729/full)\n\n## References\n\n1. [Twins and Causal Inference: Leveraging Nature's Experiment (Cold Spring Harbor Perspectives in Medicine)](https://perspectivesinmedicine.cshlp.org/content/11/6/a039552.full)\n2. [Maximizing the value of twin studies in health and behaviour (review)](https://pure.rug.nl/ws/files/807660058/Maximizing_the_value_of_twin_studies_in_health_and_behaviour.pdf)\n3. [Meta-analysis of the heritability of human traits based on fifty years of twin studies](https://www.nature.com/articles/ng.3285)\n4. [Analytic approaches to twin data using structural equation models (Briefings in Bioinformatics)](https://academic.oup.com/bib/article-pdf/3/2/119/439480/119.pdf)\n5. [The continuing value of twin studies in the omics era (van Dongen et al., Nature Reviews Genetics)](https://genepi.qimr.edu.au/contents/publications/staff/VanDongen_etal_NatRevGen_640-Aug012.pdf)\n6. [Estimating Heritability from Twin Studies (book chapter, Grasby, Verweij, Mosing, Zietsch, Medland)](https://publications.qimrberghofer.edu.au/attachment/download/969)\n7. [Francis Galton, 'The History of Twins, as a Criterion of the Relative Powers of Nature and Nurture' (1875)](https://www.galton.org/essays/1870-1879/galton-1875-history-twins.pdf)\n8. [Francis Galton (1876). The History of Twins, as a Criterion of the Relative Powers of Nature and Nurture. The Journal of the Anthropological Institute of Great Britain and Ireland.](https://doi.org/10.2307/2840900)\n9. [Who discovered the twin method? (Rende, Plomin & Vandenberg, Behavior Genetics, 1990)](https://genepi.qimr.edu.au/contents/p/staff/1990RendeBehavGen1990.pdf)\n10. [Antonio Ciocco and colleagues (1937). Twins: A Study of Heredity and Environment.. Journal of the American Statistical Association.](https://doi.org/10.2307/2279415)\n11. [Early Research on Human Genetics Using the Twin Method: Who Really Invented the Method? (Twin Research and Human Genetics, 2009)](https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/early-research-on-human-genetics-using-the-twin-method-who-really-invented-the-method/A13A85A7EC5AEFEA8BFD947262933C48)\n12. [Are extended twin family designs worth the trouble? A comparison of the bias, precision, and accuracy of parameters estimated in four twin family models](https://pmc.ncbi.nlm.nih.gov/articles/PMC3228846/)\n13. [Matthew C. Keller and colleagues (2009). Modeling Extended Twin Family Data I: Description of the Cascade Model. Twin Research and Human Genetics.](https://doi.org/10.1375/twin.12.1.8)\n14. [Classical Models for Twin Data](https://www.diva-portal.org/smash/get/diva2:1585620/FULLTEXT01.pdf)\n15. [The Prenatal Environment in Twin Studies: A Review on Chorionicity](https://pmc.ncbi.nlm.nih.gov/articles/PMC4858569/)\n16. [On the modelling of variance components in classical twin studies (Journal of Human Genetics, 2025)](https://www.nature.com/articles/s10038-025-01427-w)\n17. [Solving the missing heritability problem (PLOS Genetics)](https://journals.plos.org/plosgenetics/article?id=10.1371%2Fjournal.pgen.1008222)\n18. [Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption (Frontiers in Genetics, 2025)](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1522729/full)\n\n---\n*Topic: Encyclopedia › Life and health › Human health and medicine › Public health and healthcare › Epidemiology as a discipline*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/twin-model", "markdown_url": "https://www.edgepedia.org/twin-model.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Twin model\", Edgepedia (EdgeChat), https://www.edgepedia.org/twin-model. Edgepedia Community License 1.0.", "credit_md": "\"[Twin model](https://www.edgepedia.org/twin-model)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/twin-model](https://www.edgepedia.org/twin-model). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Twin model\", Edgepedia (EdgeChat), https://www.edgepedia.org/twin-model. Edgepedia Community License 1.0.", "speakable": "The twin model is a study design in genetics and epidemiology that compares identical and fraternal twins to estimate how much genes and environment shape traits and disease risk." }