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Identification of Genomic Variants Causing Variation in Quantitative Traits: A Review

Author

Listed:
  • Theo Meuwissen

    (Faculty of Biosciences, Norwegian University of Life Sciences, P.O. Box 5003, 1432 As, Norway)

  • Ben Hayes

    (Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia)

  • Iona MacLeod

    (Agriculture Victoria Research, Agribio, Bundoora, VIC 3083, Australia)

  • Michael Goddard

    (Agriculture Victoria Research, Agribio, Bundoora, VIC 3083, Australia
    School of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3011, Australia)

Abstract

Many of the important traits of livestock are complex or quantitative traits controlled by thousands of variants in the DNA sequence of individual animals and environmental factors. Identification of these causal variants would be advantageous for genomic prediction, to understand the physiology and evolution of important traits and for genome editing. However, it is difficult to identify these causal variants because their effects are small and they are in linkage disequilibrium with other DNA variants. Nevertheless, it should be possible to identify probable causal variants for complex traits just as we do for simple traits provided we compensate for the small effect size with larger sample size. In this review we consider eight types of evidence needed to identify causal variants. Large and diverse samples of animals, accurate genotypes, multiple phenotypes, annotation of genomic sites, comparisons across species, comparisons across the genome, the physiological role of candidate genes and experimental mutation of the candidate genomic site.

Suggested Citation

  • Theo Meuwissen & Ben Hayes & Iona MacLeod & Michael Goddard, 2022. "Identification of Genomic Variants Causing Variation in Quantitative Traits: A Review," Agriculture, MDPI, vol. 12(10), pages 1-11, October.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1713-:d:945003
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    References listed on IDEAS

    as
    1. Gerhard Moser & Sang Hong Lee & Ben J Hayes & Michael E Goddard & Naomi R Wray & Peter M Visscher, 2015. "Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model," PLOS Genetics, Public Library of Science, vol. 11(4), pages 1-22, April.
    2. Sunduimijid Bolormaa & Jennie E Pryce & Antonio Reverter & Yuandan Zhang & William Barendse & Kathryn Kemper & Bruce Tier & Keith Savin & Ben J Hayes & Michael E Goddard, 2014. "A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle," PLOS Genetics, Public Library of Science, vol. 10(3), pages 1-23, March.
    3. Jill E. Moore & Michael J. Purcaro & Henry E. Pratt & Charles B. Epstein & Noam Shoresh & Jessika Adrian & Trupti Kawli & Carrie A. Davis & Alexander Dobin & Rajinder Kaul & Jessica Halow & Eric L. No, 2020. "Expanded encyclopaedias of DNA elements in the human and mouse genomes," Nature, Nature, vol. 583(7818), pages 699-710, July.
    Full references (including those not matched with items on IDEAS)

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