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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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.
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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Updated: Jun 21, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Large-Sample Genomic Data Mining for Quantitative Traits in U.S. Holstein Cows.

Yang Da1, Dzianis Prakapenka1, Zuoxiang Liang1

  • 1Department of Animal Science, University of Minnesota, Saint Paul, USA.

Journal of Data Mining in Genomics & Proteomics
|July 8, 2024
PubMed
Summary
This summary is machine-generated.

Large-scale genomic data from U.S. Holstein cattle reveal genetic variants impacting dairy traits. Discoveries include chromosome interactions affecting fat percentage and specific genotypes for heifer culling, showcasing genomic mining power.

Keywords:
EpistasisGenomic evaluationSingle nucleotide polymorphism

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Area of Science:

  • Animal Genomics
  • Quantitative Genetics
  • Dairy Science

Background:

  • U.S. Holstein cattle possess large datasets for genomic evaluation, including Single Nucleotide Polymorphism (SNP) genotypes and dairy quantitative trait phenotypes.
  • These extensive samples offer significant opportunities to identify genetic variants and mechanisms influencing economically important traits in Holstein cattle.

Purpose of the Study:

  • To leverage large-scale genomic data in Holstein cattle for the discovery of genetic variants and underlying mechanisms affecting quantitative traits.
  • To analyze specific studies focusing on fat percentage and reproductive traits to demonstrate the utility of large-sample genomic mining.

Main Methods:

  • Genomic evaluation using large sample sizes of Holstein cattle.
  • Analysis of Single Nucleotide Polymorphism (SNP) marker genotypes and phenotypic observations.
  • Investigating genetic associations for traits such as fat percentage and reproductive performance.

Main Results:

  • A study on fat percentage identified a chromosome region interacting with all other chromosomes.
  • Two studies on reproductive traits detected specific homozygous recessive genotypes strongly associated with negative outcomes, recommended for heifer culling.
  • Demonstrated the effectiveness of large-sample genomic data mining for uncovering novel genetic insights.

Conclusions:

  • Large-scale genomic data analysis in Holstein cattle is powerful for discovering genetic variants and mechanisms affecting quantitative traits.
  • Specific genetic findings, such as chromosome interactions and detrimental genotypes, have practical implications for dairy cattle breeding and management.
  • Genomic mining with extensive datasets represents a key strategy for advancing quantitative trait genetics in livestock.