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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Genome Wide Association Studies Using Multiple-lactation Breeding Value in Holsteins.

Kwang-Hyun Cho1, Jae-Don Oh1, Hee-Bal Kim2

  • 1Animal Genomics and Breeding Center, Hankyong National University, Anseong 456-749, Korea .

Asian-Australasian Journal of Animal Sciences
|February 7, 2015
PubMed
Summary

This study identified significant genetic markers for milk production traits in Holstein cows across four lactations. Differences in genetic associations between the first and later lactations highlight the utility of multi-lactation models for marker-assisted selection.

Keywords:
Genome Wide AssociationHolsteinLactationMilk Production TraitsSingle Nucleotide Polymorphism

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Area of Science:

  • Animal Genetics
  • Dairy Science
  • Quantitative Genetics

Background:

  • Estimated breeding values (EBVs) are crucial for genetic improvement in dairy cattle.
  • Understanding lactation-specific genetic effects is essential for accurate breeding strategies.

Purpose of the Study:

  • To identify single nucleotide polymorphism (SNP) markers associated with milk production traits across multiple lactations.
  • To compare genetic marker associations between the first lactation and subsequent lactations (2nd-4th).
  • To evaluate the genetic utility of multi-lactation models for marker-assisted selection.

Main Methods:

  • Genome-wide association study (GWAS) using high-density SNP genotypes from 456 Holstein animals.
  • Analysis of EBVs for milk production traits from 1st to 4th lactation.
  • Statistical testing of marker significance using a multiple lactation model.

Main Results:

  • Significant differences in SNP marker associations were observed between the first lactation and subsequent lactations.
  • A consistent trend was found in the mean deviation and correlation of estimated genetic effects across lactations.
  • Common significant markers across all lactations and for different traits were identified.

Conclusions:

  • Lactation-specific genetic architectures for milk production traits necessitate the use of multi-lactation models.
  • Identified significant markers can aid in quantitative trait loci exploration and marker-assisted selection for improved milk production.
  • The findings support the genetic usefulness of considering lactation as a distinct trait in breeding value estimation.