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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...

You might also read

Related Articles

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

Sort by
Same author

Prussian Blue Analogue-Derived NiFe Sulfide Enabling Synergistic ORR/OER via Tuned Electronic Structures for Zn-Air Batteries.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Statistics and AI - A Fireside Conversation.

Harvard data science review·2026
Same author

Ultrasound-Guided Pectoral Nerve Block for Cardiac Implantable Electronic Device Implantation: A Prospective Randomized Controlled Trial of Postprocedural Analgesic Benefit in an Asian Population.

Anesthesiology research and practice·2026
Same author

Atrial fibrillation and metabolic syndrome: an updated review of mechanisms, risk factors, and therapeutic strategies.

Frontiers in cardiovascular medicine·2026
Same author

RNA-binding protein hnRNPD induces epithelial-mesenchymal transition in Wilms' tumor via facilitating MAP4K4 mRNA stability.

Molecular genetics and genomics : MGG·2026
Same author

Machine learning workflows in climate modelling: design patterns and insights from case studies.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

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

Identifying influential regions in extremely rare variants using a fixed-bin approach.

Michael Agne1, Chien-Hsun Huang, Inchi Hu

  • 1Department of Statistics, Columbia University, 1255 Amsterdam Avenue, Room 1005, MC 4690, New York, NY 10027, USA. mra2110@columbia.edu.

BMC Proceedings
|March 1, 2012
PubMed
Summary

This study identifies influential regions of rare single-nucleotide polymorphisms (SNPs) associated with disease risk. A fixed-bin approach using 100-SNP and 30-SNP bins effectively pinpointed causal SNPs, including two within the ELAVL4 gene.

More Related Videos

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

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

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Identifying genetic variants associated with disease is crucial for understanding disease mechanisms.
  • Rare variants, particularly private variants, pose challenges for association studies due to their low frequency.
  • Existing methods for analyzing rare variants often struggle with insufficient statistical power or arbitrary region definitions.

Purpose of the Study:

  • To identify regions of single-nucleotide polymorphisms (SNPs) significantly associated with disease response rate among rare variants.
  • To evaluate the effectiveness of a fixed-bin approach for collapsing rare variants compared to gene-based methods.
  • To pinpoint specific genomic regions and causal SNPs influencing disease susceptibility.

Main Methods:

  • Analysis of Genetic Analysis Workshop 17 data, focusing on rare variants and their association with affected status (case vs. control).
  • Implementation of a fixed-bin approach, grouping a preset number of SNPs (100-SNP and 30-SNP bins) to analyze regional variant composition.
  • Comparison of the fixed-bin approach with a null hypothesis assuming uniform distribution of rare variants across cases and controls.

Main Results:

  • Several highly influential regions containing causal SNPs were identified using both 100-SNP and 30-SNP fixed bins.
  • The 100-SNP approach detected seven causal SNPs within the most significant regions.
  • The 30-SNP approach identified three causal SNPs, with two overlapping SNPs located in the ELAVL4 gene, validated by both methods.

Conclusions:

  • The fixed-bin approach is effective for identifying regions of rare variants associated with disease risk.
  • Collapsing rare variants within fixed-size bins enhances the power to detect associations, even for private variants.
  • The ELAVL4 gene region emerged as a significant locus for disease association, highlighting its potential role in the studied condition.