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 Concept Videos

Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.5K
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
1.5K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

12.9K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
12.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

360
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
360
Multiple Allele Traits01:49

Multiple Allele Traits

32.9K
The Concept of Multiple Allelism
32.9K
Probability Laws01:49

Probability Laws

30.0K
Overview
30.0K
Polygenic Traits01:18

Polygenic Traits

7.2K
7.2K

You might also read

Related Articles

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

Sort by
Same author

Blood DNA methylation and breast cancer risk: a prospective nested case-control study.

EBioMedicine·2026
Same author

A long-term image-derived AI-based risk model for primary prevention of breast cancer in individuals at high risk.

Science translational medicine·2026
Same author

Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome.

Cell·2026
Same author

Endoxifen for mammographic density reduction-results from the KARISMA endoxifen trial.

Journal of the National Cancer Institute·2026
Same author

Statins and postmenopausal breast cancer risk; results from the KARMA cohort.

Cancer causes & control : CCC·2026
Same author

Time-Varying Hormonal Treatment and Metastasis-Free Survival Among ER+ Breast Cancer Patients: A Natural History Modelling Approach.

Statistics in medicine·2026

Related Experiment Video

Updated: May 6, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

9.2K

A unified model for estimating and testing familial aggregation.

Myeongjee Lee1, Paola Rebora, Maria Grazia Valsecchi

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden.

Statistics in Medicine
|October 24, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a unified model for familial risk, enhancing Cox regression to detail kinship effects. The model identifies significant kindred-specific disease risks, aiding future genetic biomarker studies.

Keywords:
bootstrapkindred-specific riskleukemianon-Hodgkin's lymphomarobust sandwich variancesurvival analysis

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

22.2K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.6K

Related Experiment Videos

Last Updated: May 6, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

9.2K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

22.2K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.6K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Genetics

Background:

  • Familial aggregation studies offer insights into genetic disease mechanisms.
  • Existing epidemiological literature uses diverse statistical methods for familial risk analysis.

Purpose of the Study:

  • To develop a unified statistical model for familial risk assessment.
  • To estimate and compare risks across specific first-degree and higher-degree kinships.
  • To identify kindred-specific disease risks for genetic biomarker study design.

Main Methods:

  • Extension of the Cox regression model to incorporate detailed kinship effects.
  • Utilized interaction terms for estimating and comparing risks among kinships.
  • Employed robust sandwich variance or bootstrap estimates for correlated family data.
  • Applied robust Wald tests for formal comparison of hazard ratios across kinships.

Main Results:

  • The unified model successfully estimated detailed effects of kinship.
  • Statistically significant kindred-specific disease risks were detected.
  • Results were consistent with simpler stratified methods but offered more detail.
  • Demonstrated application in adult leukemia and non-Hodgkin's lymphoma studies.

Conclusions:

  • The developed model provides a robust framework for analyzing familial disease risk.
  • Identified significant kinship effects can guide the design of genetic studies.
  • This approach advances the understanding of genetic contributions to disease.