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While not all determinants for hippocampus weight are known, it is a good model of a more polygenic trait.
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If a trait is more polygenic, then the inclusion of sequence information may not increase the accuracy of breeding values unless the reference population is very large.
Novel statistical approaches for analyzing GWAS data have recently been developed to be more sensitive to polygenic traits.
However, this phenomenon is likely more dramatic when polygenic traits are selected in traditional breeding programs with non-transgenic crops.
BayesB has shown little benefit over GBLUP models if the traits are more polygenic in nature e.g. [ 31].
Empirical genomic selection results suggest that many traits are more polygenic than what was simulated previously [ 3].
It has been noted that multiple-trait models were better able to capture genetic correlation between traits when major QTL were present, as compared to traits with a more polygenic architecture [ 28].
Hence, genomic breeding values may be predicted more accurately than for a purely polygenic trait.
Our previous genetic studies found gill raker number to be a highly polygenic trait, controlled by more than 15 QTL (Glazer et al. 2014; Miller et al. 2014).
Subsequently, GWAS have driven the transition from primarily studying monogenic traits to ones of a more polygenic nature.
Least square means from the linear regression model suggest higher persistency of accuracy over time for traits with high estimates of π (egg weight, body weight), indicating that the GEBV for these traits capture more information through LD or linkage with QTL, compared to traits which seem to be more polygenic and where markers explain mostly pedigree relationships.
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