Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome-wide Association Studies-GWAS

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...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

Pharmacogenomics: Identification of New Drug Targets

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

Principles of Pharmacogenetics: Types of Genetic Variants

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...

Comparing Copy Number Variations and SNPs

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%...

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...

You might also read

Related Articles

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

Sort by

Same author

Canadian Surgery Forum 2018: St. John's, NL Sept. 13-15, 2018.

Canadian journal of surgery. Journal canadien de chirurgie·2022

Same author

2021 Canadian Surgery Forum: Virtual, online Sept. 21-24, 2021.

Canadian journal of surgery. Journal canadien de chirurgie·2022

Same author

"In the tube" following sternotomy: A quasi-experimental study.

European journal of cardiovascular nursing·2021

Same author

Pouch-uterine volvulus: a variant of floppy pouch syndrome?

Techniques in coloproctology·2019

Same author

Implementing a Memory Clinic Model to Facilitate Recruitment into Early Phase Clinical Trials for Mild Cognitive Impairment and Alzheimer's Disease.

The journal of prevention of Alzheimer's disease·2019

Same author

Towards a characterisation of the wild legume bitter vetch (Lathyrus linifolius L. (Reichard) Bässler): heteromorphic seed germination, root nodule structure and N-fixing rhizobial symbionts.

Plant biology (Stuttgart, Germany)·2018

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Identifying disease polymorphisms from case-control genetic association data.

¹Natural Science Research Institute, Yonsei University, 134 Shinchon-Dong, Seodaemun-Ku, Seoul 120-749, Korea. lypark@yonsei.ac.kr

|October 16, 2010

Summary

This study introduces a new statistical method to identify true disease polymorphisms within gene regions, distinguishing them from linked markers. The approach uses linkage disequilibrium in controls and likelihood ratio tests for accurate genetic analysis.

More Related Videos

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

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

Published on: August 21, 2016

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

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

Published on: April 4, 2018

Related Experiment Videos

Last Updated: Jun 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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

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

Published on: August 21, 2016

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

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

Published on: April 4, 2018

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Case-control association studies often identify multiple associated polymorphisms in a gene region.
Distinguishing the causal disease polymorphism from linked markers is a significant challenge.
Existing methods lack the ability to address multiple disease polymorphisms using linkage disequilibrium (LD).

Purpose of the Study:

To develop a novel statistical method for detecting true disease polymorphisms within a gene region.
To differentiate causal variants from markers in linkage disequilibrium (LD).
To improve the fine-mapping of complex trait genetic architectures.

Main Methods:

Utilizes linkage disequilibrium (LD) patterns observed in control populations.
Employs model-based likelihood ratio tests to identify disease polymorphisms.
Evaluates performance with varying sample sizes and re-sequenced data.

Main Results:

The proposed method demonstrates reliable Type I and Type II error rates with sufficient sample sizes.
Performance is enhanced when utilizing re-sequenced genetic data.
The method effectively distinguishes disease polymorphisms from linked markers.

Conclusions:

A new statistical approach can accurately detect disease polymorphisms in gene regions.
This method offers valuable insights into the genetic architecture of complex traits.
Application to fine-mapping studies using dense genotyping or re-sequencing data is recommended.