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Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency.

Y Lu1, M J Vandehaar1, D M Spurlock2

  • 1Department of Animal Science, Michigan State University, East Lansing 48824.

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|February 4, 2018
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Summary

Genome-wide association studies identified significant genomic regions on BTA12 and BTA26 influencing dairy cattle feed efficiency (FE). Comparing traditional residual feed intake (RFI) with a multiple trait (MT) model revealed distinct genetic associations for feed intake versus energy metabolism traits.

Keywords:
feed efficiencygenome-wide associationmultiple trait

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Livestock Science

Background:

  • Feed efficiency (FE) is a crucial trait in dairy cattle production, impacting profitability and environmental sustainability.
  • Genome-wide association (GWA) studies are powerful tools for identifying genetic regions that influence complex traits like FE.
  • Understanding the genetic architecture of FE is essential for developing targeted breeding strategies.

Purpose of the Study:

  • To conduct a genome-wide association study (GWA) to identify genomic regions influencing dairy cattle feed efficiency (FE).
  • To compare the effectiveness of a classical residual feed intake (RFI) model with a multiple trait (MT) approach for modeling FE.
  • To investigate the genetic relationships between feed intake and energy metabolism traits in dairy cows.

Main Methods:

  • Utilized phenotypic data (dry matter intake, milk energy, metabolic body weight) and SNP genotypes from 4,916 dairy cows.
  • Employed a single-step genomic best linear unbiased prediction (BLUP) procedure for both RFI and MT models.
  • Performed GWA analysis on single SNP markers and subsequently on nonoverlapping 1-Mb windows.

Main Results:

  • Single SNP marker effects were small and not statistically significant for either FE measure.
  • Significant associations between FE and genomic regions on Bos taurus autosomes BTA12 and BTA26 were detected using 1-Mb window analysis.
  • No overlap was found between genomic regions for the MT FE measure (DMI|MILKE,MBW) and energy sink traits (MILKE, MBW).
  • GWA for dry matter intake (DMI) showed genetic associations with energy sink traits, influencing comparisons with FE measures like RFI.

Conclusions:

  • GWA analysis successfully identified specific genomic windows on BTA12 and BTA26 associated with dairy cattle feed efficiency.
  • The study highlights the importance of the chosen FE modeling approach, with the MT model providing insights distinct from energy metabolism traits.
  • Findings have implications for interpreting GWA studies on feed intake and feed efficiency, emphasizing the need to consider underlying genetic correlations.