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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

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,...
Pedigree Analysis01:35

Pedigree Analysis

Overview
Pedigree Analysis01:35

Pedigree Analysis

Overview

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Related Experiment Video

Updated: Jun 5, 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

Interpretable machine learning for cattle breed classification and SNP prioritization.

Farzad Atrian-Afiani1, Gábor Mészáros2, Johann Sölker2

  • 1Universität für Bodenkultur Wien, Vienna, Austria. farzad.atrian-afiani@boku.ac.at.

Genetics, Selection, Evolution : GSE
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately identified endangered cattle breeds using genome-wide single nucleotide polymorphisms (SNPs). This approach improves breed classification and pinpoints key genetic markers for conservation efforts.

Related Experiment Videos

Last Updated: Jun 5, 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

Area of Science:

  • Genomics
  • Bioinformatics
  • Conservation Biology

Background:

  • Conserving endangered cattle breeds is vital for biodiversity and genetic resources.
  • Traditional breed classification methods lack precision for closely related breeds.
  • Genomic data offers potential for improved breed identification.

Purpose of the Study:

  • Develop a machine learning model for accurate cattle breed identification using single nucleotide polymorphisms (SNPs).
  • Identify specific SNPs that discriminate between endangered Austrian cattle breeds.
  • Establish a genomic framework for cattle breed conservation.

Main Methods:

  • Applied Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) classifiers to genome-wide SNP data from 6850 individuals across 11 breeds.
  • Utilized SHapley Additive exPlanations (SHAP) for model interpretability.
  • Tuned hyperparameters via five-fold cross-validation and evaluated models on independent test sets.

Main Results:

  • Achieved mean classification accuracies of 0.842 (LightGBM) and 0.837 (Random Forest).
  • Identified the top 100 SNPs crucial for breed separation, with some being breed-specific and others reflecting population structure.
  • Highlighted specific SNPs (e.g., ARS-BFGL-NGS-97995, DIAS-308) for distinguishing breeds and clusters.

Conclusions:

  • Machine learning provides scalable, accurate genomic tools for cattle breed prediction.
  • Identified SNPs serve as practical markers for breed management, monitoring, and policy.
  • The study offers a robust framework for genomic conservation of endangered cattle breeds.