Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

@inproceedings{Wang2018ExpandingTB,
  title={Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits},
  author={Jiabo Wang and Zhengkui Zhou and Zhe Zhang and Hui Li and Di Liu and Qin Zhang and Peter J. Bradbury and Edward S. Buckler and Zhiwu Zhang},
  booktitle={Heredity},
  year={2018}
}
Improvement of statistical methods is crucial for realizing the potential of increasingly dense genetic markers. Bayesian methods treat all markers as random effects, exhibit an advantage on dense markers, and offer the flexibility of using different priors. In contrast, genomic best linear unbiased prediction (gBLUP) is superior in computing speed, but only superior in prediction accuracy for extremely complex traits. Currently, the existing variety in the BLUP method is insufficient for… CONTINUE READING
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