Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A simple method of model fitting for adoption data.

H Coon1, G Carey, D W Fulker

  • 1Institute for Behavioral Genetics, University of Colorado, Boulder 80309.

Behavior Genetics
|May 1, 1990
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Training in movement disorders during neurology residency in France: A national survey on educational gaps and future perspectives.

Revue neurologique·2025
Same author

Combined genome-wide linkage and targeted association analysis of head circumference in autism spectrum disorder families.

Journal of neurodevelopmental disorders·2017
Same author

The 'Practice Entrepreneur' - An Australian case study of a systems thinking inspired health promotion initiative.

Health promotion international·2017
Same author

Multivariate Familial Analysis of Cognitive Measures in the Colorado Family Reading Study.

Multivariate behavioral research·2016
Same author

Identifying rare variants for genetic risk through a combined pedigree and phenotype approach: application to suicide and asthma.

Translational psychiatry·2014
Same author

SLC6A3 coding variant Ala559Val found in two autism probands alters dopamine transporter function and trafficking.

Translational psychiatry·2014
Same journal

Direct and Indirect Genetic Effects on Child ADHD Traits in Early and Mid-Childhood: Trio Genome-Wide Complex Trait Analyses in a Large Norwegian Birth Registry Cohort.

Behavior genetics·2026
Same journal

Behavioral Disinhibition Model of Addiction: A Review and New Findings from the Minnesota Twin Family Study.

Behavior genetics·2026
Same journal

Tracing the Right Path: Determination of Large Pedigree Segmentation and Relatedness.

Behavior genetics·2026
Same journal

Genetic and Environmental Associations Between Processing Speed and Executive Functions Across Adolescence and Established Adulthood.

Behavior genetics·2026
Same journal

Heritability of Functional Literacy: Evidence from a Classical Twin Design.

Behavior genetics·2026
Same journal

Geneticists' Understandings of Genetic Causation.

Behavior genetics·2026
See all related articles

This study introduces a flexible, linearized model for analyzing adoption data without strict assumptions on genetic transmission or mating. It allows for testing data consistency and the balance of genetic versus environmental influences on traits like cognitive ability.

Area of Science:

  • Behavioral Genetics
  • Quantitative Genetics
  • Statistical Modeling

Background:

  • Traditional models for adoption data often rely on restrictive assumptions about genetic transmission and assortative mating.
  • These assumptions can limit the applicability and interpretation of findings in genetic and environmental influence studies.

Purpose of the Study:

  • To present a novel, linearized statistical model for analyzing adoption data that bypasses traditional restrictive assumptions.
  • To enable robust testing of internal data consistency and the relative contributions of genetic and environmental factors.
  • To offer a flexible tool for initial exploration of complex adoption study designs.

Main Methods:

  • Development and application of a linearized statistical model.
  • Utilized standard statistical packages (LISREL, EQS) for model fitting.

Related Experiment Videos

  • Applied the model to parent-child general cognitive ability data from the Colorado Adoption Project (CAP).

    • Main Results:

    • The proposed model successfully fits adoption data, demonstrating its practical utility.
    • The model allows for the assessment of genetic and environmental transmission parameters without stringent prior assumptions.
    • Demonstrated the model's capability in analyzing general cognitive ability transmission in the CAP dataset.

    Conclusions:

    • The presented linearized model offers a flexible and accessible approach for analyzing adoption data.
    • It facilitates the examination of genetic and environmental influences and data integrity.
    • This model serves as a valuable tool for preliminary investigations and can be extended to more complex research designs.