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

Compound regressive models for family data.

G E Bonney1

  • 1Department of Biostatistics, Fox Chase Cancer Center, Philadelphia, Pa. 19111.

Human Heredity
|January 1, 1992
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

Conceptualizing human variation.

Nature genetics·2004
Same author

Ascertainment corrections based on smaller family units.

American journal of human genetics·1998
Same author

The use of logistic models for the analysis of codon frequencies of DNA sequences in terms of explanatory variables.

Biometrics·1994
Same author

Genetic analysis combining path analysis with regressive models: the TAU model of multifactorial transmission.

Human heredity·1994
Same author

Investigating the genetic basis for ankylosing spondylitis. Linkage studies with the major histocompatibility complex region.

Arthritis and rheumatism·1994
Same author

Genetic analysis combining path analysis with regressive models: the BETA path model of polygenic and familial environmental transmission.

Genetic epidemiology·1994
Same journal

Comparative profiles of pediatric Mendeliome: A Single-Centre 572-Whole-Exome Sequencing Study in Xinjiang.

Human heredity·2026
Same journal

Erratum.

Human heredity·2026
Same journal

Exploratory Analysis of HMGB1 Genetic Variants and Their Potential Association with Lung Cancer Susceptibility and Chemotherapy Response in a Chinese Population.

Human heredity·2025
Same journal

Weighted Burden Analysis of Rare Genetic Variants Identifies Novel Genes with Effects on BMI.

Human heredity·2025
Same journal

Generalized Stable Population and Agent-Based Models of Phenotypic Transmission in Human Populations, with an Application to Body Size.

Human heredity·2025
Same journal

Proteinase-activated receptor 2 (PAR-2) expression and F2RL1 genetic variants are associated with asthma: a case-control study in the Chinese population.

Human heredity·2025
See all related articles

New regressive models analyze family data, accounting for excess within-sibship variation from shared environments or genes. These models extend genetic analysis to complex family structures and multiple genetic factors.

Area of Science:

  • Biostatistics
  • Statistical Genetics
  • Quantitative Genetics

Background:

  • Traditional regressive models for family data have limitations in capturing complex genetic and environmental influences.
  • Existing models may not fully account for within-sibship covariation exceeding standard assumptions, particularly when birth order is not a primary factor.

Purpose of the Study:

  • To extend regressive models for family data analysis.
  • To incorporate excess within-sibship covariation arising from unspecified factors like shared environment and multiple genes.
  • To develop practical algorithms for these compound regressive models applicable to complex pedigrees.

Main Methods:

  • Development of compound regressive models, a variant of class D regressive models with within-sibship interchangeability.

Related Experiment Videos

  • Comparison of Elston-Stewart and Morton-MacLean algorithms for mixed models of inheritance to motivate algorithm derivation.
  • Extension of models to pedigrees of arbitrary structure and multilocus problems.

    • Main Results:

    • The proposed compound regressive models can accommodate excess within-sibship covariation beyond standard genetic models.
    • These models allow for a probability distribution of within-sibship cumulative risk from unspecified factors.
    • The models ensure equal sib-sib correlation within a sibship, reflecting within-sibship interchangeability.

    Conclusions:

    • Compound regressive models offer a flexible framework for analyzing family data with complex genetic and environmental architectures.
    • The derived algorithms facilitate the application of these models to diverse pedigree structures and multilocus genetic analyses.
    • This approach enhances the ability to model shared environmental and polygenic influences in familial aggregation studies.