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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.1K
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...
15.1K
Polygenic Traits01:18

Polygenic Traits

68.7K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
68.7K
Cancer Prevention02:59

Cancer Prevention

7.5K
Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
7.5K
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

11.0K
Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
11.0K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

330
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
330
Cancer Survival Analysis01:21

Cancer Survival Analysis

584
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
584

You might also read

Related Articles

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

Sort by
Same author

Estimated preventable fraction of chronic disease attributed to long-term physical activity and diet quality, independent of body weight: a prospective cohort study of three US cohorts.

Lancet regional health. Americas·2026
Same author

Planetary Health Diet Index and breast cancer risk.

British journal of cancer·2026
Same author

Metabolomic signatures of dietary carbohydrates and differential association with type 2 diabetes.

Nature health·2026
Same author

Adherence to the Mediterranean diet and risk of pancreatic cancer: an analysis of 2.3 million participants in the Pooling Project of Prospective Studies of Diet and Cancer (DCPP).

European journal of epidemiology·2026
Same author

A multi-state survival model to identify risk factors for lethal ovarian cancer.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Circulating imidazole propionate and coronary heart disease risk: interplay between histidine intake, fiber, and gut microbiome.

BMC medicine·2026

Related Experiment Video

Updated: Dec 22, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K

Combined Associations of a Polygenic Risk Score and Classical Risk Factors With Breast Cancer Risk.

Pooja Middha Kapoor1,2, Nasim Mavaddat3, Parichoy Pal Choudhury4

  • 1Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Journal of the National Cancer Institute
|May 3, 2020
PubMed
Summary
This summary is machine-generated.

This study found that while genetic risk score (PRS313) and lifestyle factors don't interact, higher genetic risk amplifies the impact of traditional breast cancer risk factors.

More Related Videos

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

577
gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair
08:15

gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair

Published on: October 6, 2014

12.6K

Related Experiment Videos

Last Updated: Dec 22, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

577
gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair
08:15

gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair

Published on: October 6, 2014

12.6K

Area of Science:

  • Genetics
  • Epidemiology
  • Oncology

Background:

  • Breast cancer risk is influenced by both genetic predisposition and lifestyle factors.
  • Polygenic risk scores (PRS) are emerging tools for assessing genetic susceptibility.
  • Understanding the interplay between genetic and non-genetic risk factors is crucial for personalized prevention strategies.

Purpose of the Study:

  • To evaluate the joint associations between a 313-variant polygenic risk score (PRS313) and established breast cancer risk factors.
  • To investigate potential interactions between PRS313 and risk factors on breast cancer risk, overall and by estrogen receptor status.
  • To assess how genetic risk modifies the absolute lifetime risk projected by classical risk factors.

Main Methods:

  • Utilized data from 72,284 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium.
  • Employed standard logistic regression and a novel case-only method to analyze interactions.
  • Assessed goodness-of-fit for multiplicative models between PRS313 and individual risk factors.

Main Results:

  • No significant evidence of interaction was found between PRS313 and individual breast cancer risk factors after multiple testing correction.
  • Goodness-of-fit tests supported a multiplicative model for the combined effect of PRS313 and risk factors.
  • The projected absolute lifetime risk of breast cancer associated with classical risk factors was substantially greater for women with higher genetic risk (PRS313 and family history).
  • Women in the highest decile of genetic risk had, on average, 17.5% higher projected absolute lifetime risk compared to those in the lowest decile.

Conclusions:

  • While PRS313 and classical risk factors may not interact in a synergistic manner, higher genetic risk significantly amplifies the absolute lifetime risk conferred by these factors.
  • These findings highlight the importance of considering genetic risk when assessing and managing breast cancer risk.
  • The results have implications for targeted risk prevention strategies for women with elevated genetic susceptibility.