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Why Breeding Values Estimated Using Familial Data Should Not Be Used for Genome-Wide Association Studies

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    Rights statement: Copyright © 2014 Ekine et al. This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Original languageEnglish
Pages (from-to)341-7
Number of pages7
Issue number2
Early online date20 Dec 2013
Publication statusPublished - Feb 2014


In animal breeding, the genetic potential of an animal is summarized as its estimated breeding value, which is derived from its own performance as well as the performance of related individuals. Here, we illustrate why estimated breeding values are not suitable as a phenotype for genome-wide association studies. We simulated human-type and pig-type pedigrees with a range of QTL effects (0.5-3% of phenotypic variance) and heritabilities (0.3-0.8). We analyzed 1000 replicates of each scenario with 4 models: a) a full mixed model including a polygenic effect, b) a regression analysis using the residual of a mixed model as a trait score (so called GRAMMAR approach), c) a regression analysis using the estimated breeding value as a trait score and d) a regression analysis that uses the raw phenotype as a trait score. We show that using breeding values as a trait score gives very high false positive rates (up 14% in human pedigrees and > 60% in pig pedigrees). Simulations based on a real pedigree show that additional generations of pedigree increase the type I error. Including the family relationship as a random effect provides the highest power to detect QTL, while controlling type I error at the desired level and providing the most accurate estimates of the QTL effect. Both the use of residuals and the use of breeding values result in deflated estimates of the QTL effect. We derive the contributions of QTL effects to the breeding value and residual and show how this affects the estimates.

    Research areas

  • genome-wide association, Family, structure , Type I, Error, Statistical power

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