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Twin models female8/12/2023 ![]() Prescott CA (2004) Using the mplus computer program to estimate models for continuous and categorical data from twins. Ozaki K, Toyoda H, Iwama N, Kubo S, Ando J (2011) Using non-normal sem to resolve the acde model in the classical twin design. Nelder JA, Wedderburn RWM (1972) Generalized linear models. Neale MC, Hunter MD, Pritikin JN, Zahery M, Brick TR, Kirkpatrick RM, Estabrook R, Bates TC, Maes HH, Boker SM (2016) OpenMx 2.0: extended structural equation and statistical modeling. Technical report, Virginia Common wealth University, Department of Psychiatry. Neale MC, Maes HH (2004) Methodology for genetic studies of twins and families. McGue M, Christensen K (1997) Genetic and environmental contributions to depression symptomatology: evidence from danish twins 75 years of age and older. McArdle JJ, Prescott CA (2005) Mixed-effects variance components models for biometric family analyses. Liang K-Y, Zeger SL (1986) Longitudinal data analysis using generalized linear models. Oxford University Press, Fundamentals of Genetic Epidemiology Khoury MJ, Beaty TH, Cohen BH (1993) Fundamentals of genetic epidemiology. Jørgensen B, Kokonendji CC (2016) Discrete dispersion models and their tweedie asymptotics. Jørgensen B, Knudsen SJ (2004) Parameter orthogonality and bias adjustment for estimating functions. Jørgensen B (1997) The theory of dispersion models. J Royal Statist Soc Series B 49(2):127–162 Jørgensen B (1987) Exponential dispersion models. Holst KK, Scheike TH, Hjelmborg JB (2016) The liability threshold model for censored twin data. Biometrics 65(2):584–589įolstein MF, Folstein SE, McHugh PR (1975) Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. Nat Rev Genet 3:872–882įeng R, Zhou G, Zhang M, Zhang H (2009) Analysis of twin data using sas. Stat Model 19(6):617–633īoomsma D, Busjahn A, Peltonen L (2002) Classical twin studies and beyond. Stat Modellīonat WH, Petterle RR, Hinde J, Demétrio CG (2019) Flexible quasi-beta regression models for continuous bounded data. Stat Modell 18(1):24–49īonat WH, Peterle R, Hinde J, Demétrio CGB (2018) Flexible regression models for continuous bounded data. J Statist Comput Simulat 87(11):2138–2152īonat WH, Jørgensen B, Kokonendji CC, Hinde J, Demétrio CGB (2018) Extended Poisson–Tweedie: properties and regression models for count data. J Royal Statist Soc: Series C 65:649–675īonat WH, Kokonendji CC (2017) Flexible tweedie regression models for continuous data. We illustrate the proposed models through simulation studies and six data analyses and provide computational implementation in R through the package mglm4twin.īonat WH, Jørgensen B (2016) Multivariate covariance generalized linear models. The marginal specification of our models allows us to extend classic models and biometric indices such as the bivariate heritability, genetic, environmental and phenotypic correlations to non-Gaussian data. ![]() The non-normality is taken into account by actually modelling the mean and variance relationship, while the covariance structure is modelled by means of a linear covariance model including the option to model the dispersion components as functions of known covariates in a regression model fashion. In this paper, we propose a flexible and unified statistical modelling framework for analysing multivariate Gaussian and non-Gaussian twin and family data. ![]() The multivariate analysis of twin and family data is in general based on structural equation modelling or linear mixed models that essentially decomposes sources of covariation as originally suggested by Fisher. Multivariate twin and family studies are one of the most important tools to assess diseases inheritance as well as to study their genetic and environment interrelationship.
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