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...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

You might also read

Related Articles

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

Sort by
Same author

Bevacizumab withdrawal-associated cortical hyperperfusion in recurrent high-grade astrocytoma: An underrecognized MRI pitfall.

Radiology case reports·2026
Same author

Serum cotinine-assessed tobacco exposure, cadmium co-exposure, and childhood obesity: a cross-sectional study.

Environmental health : a global access science source·2026
Same author

L-carnitine in metabolic dysfunction-associated steatotic liver disease: mechanisms and therapeutic potential.

Frontiers in nutrition·2026
Same author

Diamine-Mediated Synergistic Engineering of Orientation and Interfacial Field of 3D/1D Heterojunctions for Efficient Perovskite Photovoltaics.

Nano-micro letters·2026
Same author

C2FAU-Net: A Deep Learning Approach with Multi-scale Strategy for Automated Delineation of Organs-at-risk in Cervical Cancer High-dose Rate Brachytherapy.

Journal of medical physics·2026
Same author

Dynamic Evolution Processes Between Excited Triplet and Doublet States in Organic Photo-Responsive Materials.

Angewandte Chemie (International ed. in English)·2026
Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026
Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026
Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026
Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026
Same journal

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
See all related articles

Related Experiment Video

Updated: May 23, 2026

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Two-phase stratified sampling designs for regional sequencing.

Zhijian Chen1, Radu V Craiu, Shelley B Bull

  • 1Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Toronto ON, Canada.

Genetic Epidemiology
|March 31, 2012
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) identify disease-linked genetic variants. This study introduces efficient two-phase stratified sampling for deep sequencing, improving genetic association analysis by combining data from both phases.

More Related Videos

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
11:23

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing
09:49

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

Published on: December 3, 2019

Related Experiment Videos

Last Updated: May 23, 2026

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
11:23

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing
09:49

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

Published on: December 3, 2019

Area of Science:

  • Genetics
  • Genomics
  • Epidemiology

Background:

  • Genome-wide association studies (GWAS) are effective for identifying genetic variants linked to complex diseases.
  • High-throughput sequencing technologies have reduced costs, but full sequencing remains financially challenging for large cohorts.
  • Two-phase sampling designs are utilized in large-scale studies when certain measurements are prohibitively expensive.

Purpose of the Study:

  • To investigate two-phase stratified sampling designs for genetic association studies.
  • To develop inference methods for sequence single-nucleotide polymorphism (SNP) variant associations using combined phase 1 and phase 2 data.
  • To enhance the efficiency of genetic association analyses compared to using phase 2 data alone.

Main Methods:

  • Genotyping of common tag SNPs in phase 1 for all subjects.
  • Stratified selection of a subset of subjects into phase 2 based on phase 1 tag SNP genotypes.
  • Deep sequencing of candidate regions in phase 2 subjects to identify sequence SNPs.
  • Development of statistical methods for analyzing combined data from both phases.

Main Results:

  • Proposed and evaluated alternative sampling designs for phase 2 subject selection within strata defined by tag SNP genotypes.
  • Developed methods for inferring sequence SNP variant associations using data from both phases.
  • Demonstrated improved efficiency in genetic association analysis when combining data from both phases compared to phase 2 data alone.

Conclusions:

  • Two-phase stratified sampling designs offer an efficient approach for genetic association studies involving deep sequencing.
  • Combined analysis of phase 1 and phase 2 data improves statistical power and efficiency.
  • This methodology facilitates the identification of genetic variants associated with complex diseases and traits under financial constraints.