title: Statistical Inference of Biometrical Genetic Model With Cultural Transmission.
abstract: Twin and family studies establish the foundation for studying the genetic, environmental and cultural transmission effects for phenotypes. In this work, we make use of the well established statistical methods and theory for mixed models to assess cultural transmission in twin and family studies. Specifically, we address two critical yet poorly understood issues: the model identifiability in assessing cultural transmission for twin and family data and the biases in the estimates when sub-models are used. We apply our models and theory to two real data sets. A simulation is conducted to verify the bias in the estimates of genetic effects when the working model is a sub-model.
authors.affiliation: Department of Statistical Science School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, China.
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Guo
authors.affiliation: School of Statistics and Management, Shanghai University of Finance and Economic, Shanghai, 200433, China.
authors.forename: Tian
authors.initials: T
authors.lastname: Ji
authors.affiliation: Department of Statistical Science School of Mathematics and Computational Science & State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center & Zhongshan Medical School & Xinhua College, Sun Yat-Sen University, Guangzhou, 510275, China.
authors.forename: Xueqin
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA.
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Maoming People's Hospital Maoming 525000, China.
authors.forename: Shouqiang
authors.initials: S
authors.lastname: Zhong
keywords: Biometrical Genetic model; Cultural transmission; Identifiability; Likelihood ratio test; Mixed-effects model; Twin and family study