title: The latent class twin method.
abstract: The twin method refers to the use of data from same-sex identical and fraternal twins to estimate the genetic and environmental contributions to a trait or outcome. The standard twin method is the variance component twin method that estimates heritability, the fraction of variance attributed to additive genetic inheritance. The latent class twin method estimates two quantities that are easier to interpret than heritability: the genetic prevalence, which is the fraction of persons in the genetic susceptibility latent class, and the heritability fraction, which is the fraction of persons in the genetic susceptibility latent class with the trait or outcome. We extend the latent class twin method in three important ways. First, we incorporate an additive genetic model to broaden the sensitivity analysis beyond the original autosomal dominant and recessive genetic models. Second, we specify a separate survival model to simplify computations and improve convergence. Third, we show how to easily adjust for covariates by extending the method of propensity scores from a treatment difference to zygosity. Applying the latent class twin method to data on breast cancer among Nordic twins, we estimated a genetic prevalence of 1%, a result with important implications for breast cancer prevention research.
authors.affiliation: Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland 20892-7354, U.S.A.. sb16i@nih.gov.
authors.forename: Stuart G
authors.initials: SG
authors.lastname: Baker
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D003627:Data Interpretation, Statistical; D005260:Female; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D006801:Humans; D008957:Models, Genetic; D015995:Prevalence; D012537:Scandinavian and Nordic Countries; D018433:Twin Studies as Topic / Q000706:statistics & numerical data*
keywords: Breast cancer; Latent class; Missing heritability; Phantom heritability; Propensity score; Survival variance components