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

Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

You might also read

Related Articles

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

Sort by
Same author

Association between recent overdose and chronic pain among individuals in treatment for opioid use disorder.

PloS one·2022
Same author

Studying the Utility of Using Genetics to Predict Smoking-Related Outcomes in a Population-Based Study and a Selected Cohort.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco·2021
Same author

Dissecting the genetic overlap of smoking behaviors, lung cancer, and chronic obstructive pulmonary disease: A focus on nicotinic receptors and nicotine metabolizing enzyme.

Genetic epidemiology·2020
Same author

Genetic Variant in CHRNA5 and Response to Varenicline and Combination Nicotine Replacement in a Randomized Placebo-Controlled Trial.

Clinical pharmacology and therapeutics·2020
Same author

Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies.

Addiction biology·2020
Same author

Variants in the CHRNA5-CHRNA3-CHRNB4 Region of Chromosome 15 Predict Gastrointestinal Adverse Events in the Transdisciplinary Tobacco Use Research Center Smoking Cessation Trial.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco·2019

Related Experiment Video

Updated: May 24, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Stratify or adjust? Dealing with multiple populations when evaluating rare variants.

Robert C Culverhouse1, Anthony L Hinrichs, Brian K Suarez

  • 1Department of Medicine, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, USA. rculverh@dom.wustl.edu.

BMC Proceedings
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

We compared methods for analyzing rare genetic variants to find genes affecting traits. Collapsing rare variants worked well, but analyzing subpopulations separately is crucial when variants are population-specific.

More Related Videos

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Related Experiment Videos

Last Updated: May 24, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genetics

Background:

  • Genetic Analysis Workshop 17 (GAW17) dataset features a small sample size across eight populations.
  • The dataset is characterized by a high proportion of rare genetic variants.
  • Identifying genes influencing phenotypes requires effective analysis of rare variants.

Purpose of the Study:

  • To compare the effectiveness of two rare variant collapsing methods for gene-based association studies.
  • To evaluate the performance of stratified analyses versus pooled analyses using ethnicity as a covariate.
  • To determine the optimal approach for analyzing genetic data with population structure and rare variants.

Main Methods:

  • Rare variants were collapsed within genes to simplify genetic analysis.
  • Two distinct rare variant collapsing strategies were employed.
  • Association analyses were conducted using both stratified (subpopulation-specific) and pooled (ethnicity as covariate) approaches.

Main Results:

  • Both rare variant collapsing methods demonstrated similar efficacy in identifying genes harboring causative variants.
  • Stratified analyses were superior to pooled analyses when a phenotype-associated rare variant was confined to a single subpopulation.
  • Using ethnicity as a covariate in pooled analyses did not effectively substitute for separate subpopulation analyses.

Conclusions:

  • Rare variant collapsing is a viable strategy for gene-based association studies in datasets with limited sample sizes and rare variants.
  • Stratified analysis is essential for detecting phenotype-associated rare variants when they exhibit population-specific distribution.
  • Pooled analysis with ethnicity as a covariate is insufficient for capturing signals from population-specific rare variants.