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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,...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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Published on: June 21, 2018

Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in

N Long, D Gianola, G J M Rosa

    Developments in Biologicals
    |September 27, 2008
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a two-step SNP selection method to identify genetic markers associated with chick mortality in genome-wide association studies. The method significantly improved classification accuracy, pinpointing 17 SNPs linked to mortality variance.

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    Area of Science:

    • Genomics
    • Animal Breeding
    • Statistical Genetics

    Background:

    • Genome-wide association studies (GWAS) involve analyzing numerous single nucleotide polymorphisms (SNPs) against potentially fewer phenotypes.
    • Effective feature selection is crucial for identifying relevant SNPs in large genomic datasets.
    • Chick early mortality presents a significant challenge in commercial broiler production.

    Purpose of the Study:

    • To develop and evaluate a two-step SNP selection method for identifying genetic variants associated with chick mortality.
    • To assess the effectiveness of the method in a commercial broiler population.
    • To determine the proportion of mortality variance explained by selected SNPs.

    Main Methods:

    • A two-step SNP selection process combining information gain filtering and naïve Bayesian classification wrapping.
    • Discretization of continuous phenotypic data (chick mortality rates) for analysis.
    • Application of the method to 5000+ SNPs in 201 broiler sires, forming 11 case-control cohorts by varying mortality thresholds.
    • Comparison of selected SNP sets using predicted residual sum of squares (PRESS).

    Main Results:

    • Naïve Bayesian classification accuracy improved from 50% to 90% with the feature selection method.
    • The best case-control group identified 17 significant SNPs.
    • These 17 SNPs explained 31% of the variance in sire family mortality rates.

    Conclusions:

    • The developed two-step SNP selection method is effective for identifying genetic markers related to complex traits like early chick mortality.
    • The identified SNPs hold potential for marker-assisted selection to reduce progeny mortality in broiler lines.
    • This approach enhances the utility of GWAS in livestock breeding programs.