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

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

19.9K
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,...
19.9K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

19.3K
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%...
19.3K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

16.7K
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...
16.7K

You might also read

Related Articles

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

Sort by
Same author

Population-scale Y chromosome assemblies reveal recurrent remodeling within constrained architectures.

bioRxiv : the preprint server for biology·2026
Same author

Copy Number Variation in Native and Crossbred Pigs Provides Insights Into Genomic Consequences of Domestication.

Molecular ecology·2026
Same author

Overcrowding Stress in Livestock Production Alters Gut Microbiota Composition and Neuronal Nitric Oxide Synthase (nNOS) Expression in nNOS-HiBiT Knock-in Mouse Model.

Food science of animal resources·2026
Same author

Mycobacterium tuberculosis infection disrupts gut and respiratory microbial communities and networks with incomplete restoration after two months of treatment.

Gut pathogens·2026
Same author

Epigenomic methylome landscape of promoters in vertebrate genomes.

bioRxiv : the preprint server for biology·2026
Same author

MIrROR release 02: Expanded and refined 16S-ITS-23S rRNA operon dataset.

Scientific data·2026
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Apr 1, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

40.1K

Application of LogitBoost Classifier for Traceability Using SNP Chip Data.

Kwondo Kim1, Minseok Seo2, Hyunsung Kang3

  • 1Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151-921, Republic of Korea; C&K Genomics Inc., 514 Main Bldg., Seoul National University Research Park, San 4-2 Bongcheon-dong, Gwanak-gu, Seoul 151-919, Republic of Korea.

Plos One
|October 6, 2015
PubMed
Summary
This summary is machine-generated.

Identifying the origin of pigs is crucial for food safety. This study developed a machine learning model using SNP chip data to accurately predict pig place of origin (POO), enhancing traceability.

More Related Videos

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

10.8K
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

15.8K

Related Experiment Videos

Last Updated: Apr 1, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

40.1K
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

10.8K
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

15.8K

Area of Science:

  • Animal genetics
  • Food safety
  • Machine learning

Background:

  • Growing consumer concern over food safety, driven by animal-related diseases, necessitates accurate identification of animal places of origin (POO).
  • Limited research exists on leveraging advanced computational methods for POO prediction in livestock.
  • Traceability is essential for managing food safety risks and supply chain integrity.

Purpose of the Study:

  • To develop and evaluate a machine learning-based approach for predicting the place of origin (POO) of pigs.
  • To assess the effectiveness of a customized SNP chip in conjunction with classification algorithms for POO prediction.
  • To investigate the impact of kinship filtering on the accuracy of POO prediction models.

Main Methods:

  • Genotyping of 4,122 pigs from 104 farms using a customized SNP chip.
  • Performance evaluation of various classification algorithms, including LogitBoost, for POO prediction.
  • Application of a kinship coefficient-filtering approach to refine prediction model accuracy.

Main Results:

  • The LogitBoost-based prediction model demonstrated superior classification performance compared to other methods.
  • Increased accuracy in POO prediction was achieved with higher kinship-based cutoff values.
  • The study confirmed the practical applicability of machine learning and SNP chip data for pig traceability.

Conclusions:

  • Machine learning models, particularly LogitBoost, are effective for predicting pig place of origin (POO) using SNP chip data.
  • Kinship filtering can significantly enhance the accuracy of these predictive models.
  • The developed approach offers a valuable tool for improving food safety and traceability in the pork industry.