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A random regression model for total litter weight in mice.

Ricarda Elisabeth Jahnel1, Martina Langhammer1, Norbert Reinsch1

  • 1Research Institute for Farm Animal Biology (FBN), Dummerstorf 18196, Germany.

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Measuring total litter weight (LW) offers a cost-effective alternative to individual birth weight in multiparous animals. A new random regression model reveals how genetic effects on LW vary with litter size, aiding efficient breeding strategies.

Keywords:
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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Reproductive Biology

Background:

  • Individual birth weight measurement is costly in multiparous species.
  • Total litter weight (LW) is a less expensive summary trait.
  • Previous models did not account for litter size effects on genetic variance.

Purpose of the Study:

  • To propose and evaluate a random regression model for total litter weight (LW).
  • To model genetic effects of dams and sires as linear slopes on litter size.
  • To compare variance components across diverse mouse lines.

Main Methods:

  • Utilized restricted maximum likelihood (REML) estimation for variance components.
  • Applied a random regression model to 11,430 total litter weights (175,446 pups).
  • Separately analyzed four distinct mouse lines (FZTDU, DU6, DUC, DUK).

Main Results:

  • Sire genetic variance component was significant in two of the four mouse lines.
  • Heritability and reliability of genetic effects are dependent on litter size.
  • Average heritability for total LW ranged from 0.12 to 0.32 across lines.

Conclusions:

  • The proposed random regression model provides a more detailed analysis of total litter weight data.
  • This model can be applied to other multiparous species for cost-effective selection.
  • It enables more accurate genetic evaluations for traits influenced by litter size.