Simultaneous genetic analysis of longitudinal means and covariance structure in the simplex model using twin data

Conor V. Dolan; Peter C.M. Molenaar; Dorret I. Boomsma

doi:10.1007/BF01067666

Simultaneous genetic analysis of longitudinal means and covariance structure in the simplex model using twin data

Conor V. Dolan, Peter C.M. Molenaar, Dorret I. Boomsma

Human Development and Family Studies

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25 Scopus citations

Abstract

A longitudinal model based on the simplex model is presented to analyze simultaneously means and covariance structure using univariate longitudinal twin data. The objective of the model is to decompose the mean trend into components which can be attributed to those genetic and environmental factors which give rise to phenotypic individual differences and a component of unknown constitution which does not involve individual differences. Illustrations are given using simulated data and repeatedly measured weight obtained in a sample of 82 female twin pairs on six occasions.

Original language	English (US)
Pages (from-to)	49-65
Number of pages	17
Journal	Behavior Genetics
Volume	21
Issue number	1
DOIs	https://doi.org/10.1007/BF01067666
State	Published - Jan 1991

All Science Journal Classification (ASJC) codes

Ecology, Evolution, Behavior and Systematics
Genetics
Genetics(clinical)

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10.1007/BF01067666

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abstract = "A longitudinal model based on the simplex model is presented to analyze simultaneously means and covariance structure using univariate longitudinal twin data. The objective of the model is to decompose the mean trend into components which can be attributed to those genetic and environmental factors which give rise to phenotypic individual differences and a component of unknown constitution which does not involve individual differences. Illustrations are given using simulated data and repeatedly measured weight obtained in a sample of 82 female twin pairs on six occasions.",

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Simultaneous genetic analysis of longitudinal means and covariance structure in the simplex model using twin data

Abstract

All Science Journal Classification (ASJC) codes

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