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FSEM: Functional Structural Equation Models for Twin Functional Data.

S Luo1, R Song1, M Styner2

  • 1Departments of Statistics, North Carolina State University, Cary, North Carolina, USA.

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|May 7, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces functional structural equation models (FSEMs) to analyze genetic and environmental influences on twin functional data. The novel methods effectively dissect these effects and quantify their impact on brain development.

Keywords:
Covariance functionFunctional structural equation modelGenetic and environmental effectsWeighted likelihood ratio test

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Area of Science:

  • Biostatistics
  • Quantitative Genetics
  • Neuroimaging Analysis

Background:

  • Understanding the interplay of genetic and environmental factors is crucial for dissecting complex traits.
  • Functional data analysis offers powerful tools for analyzing longitudinal or spatially correlated data, such as neuroimaging measures.
  • Existing models may not fully capture the dynamic associations between genetic/environmental influences and functional data over time or space.

Purpose of the Study:

  • To develop a novel class of functional structural equation models (FSEMs) for analyzing twin functional data.
  • To dissect functional genetic and environmental effects on twin functional data.
  • To characterize the varying associations between functional data and covariates.

Main Methods:

  • A three-stage estimation procedure is proposed to estimate varying coefficient functions and covariance operators for genetic and environmental effects.
  • An inference procedure using weighted likelihood ratio statistics is developed for testing genetic/environmental effects.
  • Theoretical analysis of estimated functions, statistics, and operators is systematically conducted.

Main Results:

  • The proposed FSEMs successfully dissect genetic and environmental effects on twin functional data.
  • The estimation and inference procedures demonstrate good finite-sample performance in Monte Carlo simulations.
  • Application to twin white-matter tracts quantifies genetic and environmental influences on brain development.

Conclusions:

  • The developed functional structural equation models provide a robust framework for analyzing genetic and environmental influences on functional data.
  • The methodology allows for the characterization of varying associations and provides a basis for statistical inference.
  • This approach offers valuable insights into the etiology of complex traits, exemplified by its application to white-matter tract development.