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

Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Cochran's Q Test01:17

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...

You might also read

Related Articles

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

Sort by
Same author

Multivariate mendelian randomization for joint inferences of correlated outcomes.

European journal of epidemiology·2026
Same author

Peripheral vascular function, including endothelium-dependent measures, and dementia risk: The Framingham Heart Study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies.

American journal of human genetics·2026
Same author

Interactive effects of telomere length and genetic variants on Alzheimer disease risk across multiple ancestral populations.

Alzheimer's research & therapy·2026
Same author

Thymic health consequences in adults.

Nature·2026
Same author

Bayesian Mendelian randomization methods for index trait bias correction in subsequent trait genome-wide association studies.

HGG advances·2026
Same journal

Comparison of methods for assessing effects of risk factors on disease progression in Mendelian randomization under index event bias.

American journal of human genetics·2026
Same journal

Deciding "what" to screen for and "when": The importance of natural history information.

American journal of human genetics·2026
Same journal

Homologous recombination deficiency-driven genomic instability in ovarian cancer as an indicator of BRCA1 and BRCA2 variant pathogenicity.

American journal of human genetics·2026
Same journal

Individuals who deviate from polygenic expectation are enriched for damaging variants in genes linked to rare disease.

American journal of human genetics·2026
Same journal

Integrating social determinants of health and genetic risk in disease risk models.

American journal of human genetics·2026
Same journal

De novo variants in LDB1 are linked to distinct neurodevelopmental phenotypes determined by variant location and differing pathomechanisms.

American journal of human genetics·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Testing for population subdivision and association in four case-control studies.

Kristin G Ardlie1, Kathryn L Lunetta, Mark Seielstad

  • 1Genomics Collaborative, 99 Erie Street, Cambridge, MA, 02139, USA. kardlie@genomicsinc.com

American Journal of Human Genetics
|July 4, 2002
PubMed
Summary
This summary is machine-generated.

Population structure can impact disease association studies. This research found minimal population structure in matched U.S. and European case-control samples, suggesting they are unlikely to cause widespread false-positive results in genetic association studies.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 28, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

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

Area of Science:

  • Population Genetics
  • Genetic Epidemiology

Background:

  • Replication of disease-marker associations is a challenge in genetic studies.
  • Population structure is a suspected cause of unreplicated findings.
  • Few case-control studies have been rigorously evaluated for population stratification.

Purpose of the Study:

  • To assess the presence of detectable population subdivision in moderate-sized case-control samples.
  • To investigate the impact of population structure on the reliability of genetic association studies.
  • To provide insights into factors affecting the replication of genetic association findings.

Main Methods:

  • Examined four case-control samples (N=3,472) including U.S. whites and African Americans with hypertension, and U.S. whites and Polish whites with type 2 diabetes.
  • Utilized sum of case-control allele frequency chi-squared statistics for 9 STR and 35 SNP markers to test for population structure.
  • Replicated the PPARg Pro12Ala polymorphism association in type 2 diabetes populations.

Main Results:

  • Weak evidence of population structure was detected only in the African American sample.
  • Refining the African American sample to include only individuals with U.S.-born parents and grandparents eliminated the detected stratification.
  • The study suggests that carefully matched, moderate-sized case-control samples in cosmopolitan populations are unlikely to have significant population structure leading to false-positive associations.

Conclusions:

  • Population structure is unlikely to be a major driver of unreplicated genetic associations in well-matched, moderate-sized case-control studies from cosmopolitan populations.
  • Factors such as differences in study power due to sample size and allele frequencies may play a more significant role in the replication problem.
  • Careful sample matching and characterization are crucial for robust genetic association studies.