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

ABSTRACT: 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 adapting to new sequencing technologies and traits with different genetic architectures. In this study, we found two ways to change the kinship derivation in the BLUP method that improve prediction accuracy while maintaining the computational advantage. First, using the settlement under progressively excl

SUBMITTER: Wang J 

PROVIDER: S-EPMC6221880 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature