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Estimation of Pool Construction and Technical Error

Author

Listed:
  • John Keele

    (USDA, ARS, U.S. Meat Animal Research Center, P.O. Box 166, Clay Center, NE 68933, USA)

  • Tara McDaneld

    (USDA, ARS, U.S. Meat Animal Research Center, P.O. Box 166, Clay Center, NE 68933, USA)

  • Ty Lawrence

    (Department of Agricultural Sciences, West Texas A&M University, Canyon, TX 79016, USA)

  • Jenny Jennings

    (Five Rivers Cattle Feeding, LLC, Amarillo, TX 79022, USA)

  • Larry Kuehn

    (USDA, ARS, U.S. Meat Animal Research Center, P.O. Box 166, Clay Center, NE 68933, USA)

Abstract

Pooling animals with extreme phenotypes can improve the accuracy of genetic evaluation or provide genetic evaluation for novel traits at relatively low cost by exploiting large amounts of low-cost phenotypic data from animals in the commercial sector without pedigree (data from commercial ranches, feedlots, stocker grazing or processing plants). The average contribution of each animal to a pool is inversely proportional to the number of animals in the pool or pool size. We constructed pools with variable planned contributions from each animal to approximate errors with different numbers of animals per pool. We estimate pool construction error based on combining liver tissue, from pulverized frozen tissue mass from multiple animals, into eight sub-pools containing four animals with planned proportionality (1:2:3:4) by mass. Sub-pools were then extracted for DNA and genotyped using a commercial array. The extracted DNA from the sub-pools was used to form super pools based on DNA concentration as measured by spectrophotometry with planned contribution of sub-pools of 1:2:3:4. We estimate technical error by comparing estimated animal contribution using sub-samples of single nucleotide polymorphism (SNP). Overall, pool construction error increased with planned contribution of individual animals. Technical error in estimating animal contributions decreased with the number of SNP used.

Suggested Citation

  • John Keele & Tara McDaneld & Ty Lawrence & Jenny Jennings & Larry Kuehn, 2021. "Estimation of Pool Construction and Technical Error," Agriculture, MDPI, vol. 11(11), pages 1-11, November.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:11:p:1091-:d:671803
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    References listed on IDEAS

    as
    1. Juba Nait Saada & Georgios Kalantzis & Derek Shyr & Fergus Cooper & Martin Robinson & Alexander Gusev & Pier Francesco Palamara, 2020. "Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    2. Peter M Visscher & Gibran Hemani & Anna A E Vinkhuyzen & Guo-Bo Chen & Sang Hong Lee & Naomi R Wray & Michael E Goddard & Jian Yang, 2014. "Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples," PLOS Genetics, Public Library of Science, vol. 10(4), pages 1-10, April.
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    Cited by:

    1. Heather Burrow & Michael Goddard, 2023. "Application of Genetics and Genomics in Livestock Production," Agriculture, MDPI, vol. 13(2), pages 1-4, February.

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