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Modelling QTL effect on BTA06 using random regression test day models.

T Suchocki1, J Szyda, Q Zhang

  • 1Department of Genetics, Wrocław University of Environmental and Life Sciences, Kożuchowska 7, 51-631, Wrocław, Poland. tomasz.suchocki@up.wroc.pl

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Investigating quantitative trait locus (QTL) effects over time reveals dynamic genetic influences on dairy cattle production traits. Longitudinal models are crucial for accurately detecting time-varying QTL effects, unlike time-independent models.

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Area of Science:

  • Animal Genetics
  • Statistical Genomics
  • Dairy Cattle Breeding

Background:

  • Quantitative trait locus (QTL) effects are typically modeled as time-independent in statistical genetics.
  • Traits recorded over time, such as milk production in dairy cattle, necessitate investigating dynamic genetic influences.
  • Understanding time-varying QTL effects is crucial for improving selection strategies and genetic gain in livestock.

Purpose of the Study:

  • To estimate the position and effect of QTL for milk, fat, and protein yields, and somatic cell score in Chinese Holstein-Friesian cattle.
  • To determine if QTL effects for these traits remain constant or vary throughout lactation.
  • To compare the efficacy of time-independent versus time-varying QTL models.

Main Methods:

  • Analysis of test day records from 716 daughters of 23 sires from 23 paternal half-sib families.
  • Genotyping at 14 microsatellites on BTA6 near casein loci.
  • Application of three statistical models: a lactation model, a random regression model with a time-constant QTL, and a random regression model with a time-varying QTL.

Main Results:

  • At least one significant QTL was identified for each production trait.
  • QTL effects for milk and protein yields were found to be variable over time.
  • A significant QTL effect for fat yield was detected by all three models, but time-varying models offered a more nuanced understanding.

Conclusions:

  • Modeling QTL effects as time-dependent is essential for accurately identifying genetic loci influencing traits recorded repeatedly.
  • Time-independent QTL models may average out effects, potentially masking significant genetic variations throughout lactation.
  • Longitudinal models are superior for detecting loci that significantly influence trait variation over time in dairy cattle.