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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

17.2K
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...
17.2K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

20.4K
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,...
20.4K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

19.4K
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%...
19.4K
Genetic Screens02:46

Genetic Screens

5.9K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.9K
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

98
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...
98
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

119
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...
119

You might also read

Related Articles

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

Sort by
Same author

Optimized Animal Models for the Genetic Evaluation of Conformation Traits, Milking Ease, and Milking Temperament in Dairy Gir Cattle.

Animals : an open access journal from MDPI·2026
Same author

Bayesian Recursive and Structural Equation Models to Infer Causal Links Among Gait Visual Scores on Campolina Horses.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2024
Same author

Livestock-Forest integrated system attenuates deleterious heat stress effects in bovine oocytes.

Animal reproduction science·2024
Same author

Resequencing of Brazilian locally adapted cattle breeds revealed variants in candidate genes and transcription factors for meat fatty acid profile.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2024
Same author

Predicting ruminal degradability and chemical composition of corn silage using near-infrared spectroscopy and multivariate regression.

PloS one·2024
Same author

Genomic analysis of feed efficiency traits in beef cattle using random regression models.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie·2023

Related Experiment Video

Updated: Apr 18, 2026

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

10.9K

SNPs selection using support vector regression and genetic algorithms in GWAS.

Fabrízzio Condé de Oliveira, Carlos Cristiano Hasenclever Borges, Fernanda Nascimento Almeida

    BMC Genomics
    |January 10, 2015
    PubMed
    Summary

    This study introduces a novel method combining genetic algorithms and Support Vector Regression to identify key genetic markers for complex traits. The approach effectively reduces redundant markers, enhancing predictive accuracy in real-world data.

    More Related Videos

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
    08:27

    Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

    Published on: July 27, 2021

    5.1K

    Related Experiment Videos

    Last Updated: Apr 18, 2026

    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

    10.9K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
    08:27

    Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

    Published on: July 27, 2021

    5.1K

    Area of Science:

    • Genetics
    • Bioinformatics
    • Machine Learning

    Background:

    • Selecting relevant Single Nucleotide Polymorphism (SNP) markers is crucial for understanding complex phenotypes.
    • Existing methods may not efficiently handle multi-attribute marker selection for continuous variables.

    Purpose of the Study:

    • To develop a new methodology for simultaneously selecting the most relevant SNPs for characterizing continuous phenotypes.
    • To leverage Support Vector Regression with Pearson Universal kernel and a binary genetic algorithm for marker selection.

    Main Methods:

    • A multi-attribute approach integrating statistical tools, machine learning, and computational intelligence.
    • Utilizing a binary genetic algorithm with Support Vector Regression (SVR) and Pearson Universal kernel (PUK) as the fitness function.
    • Application of the wrapper method on simulated and real-world genetic databases.

    Main Results:

    • In simulated data (1000 SNPs, 7 relevant), the method identified 21 markers (5 true, 16 false positives).
    • Reduced 50,752 SNPs to 3,073 in a real database, improving model accuracy.
    • Performance in simulated data with epistasis matched common Genome-Wide Association Study (GWAS) methodologies.

    Conclusions:

    • The proposed method effectively explains real phenotypes, such as milk production (PTA), by eliminating redundant markers.
    • The wrapper approach using GA and SVR with PUK significantly increases prediction accuracy on real data.
    • Pearson Universal Kernel (PUK) showed comparable performance to linear and RBF kernels.