Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits
文献类型： 外文期刊
作者： Wang, Jiabo 2 ; Zhou, Zhengkui 3 ; Zhang, Zhe 4 ; Li, Hui 1 ; Liu, Di 2 ; Zhang, Qin 5 ; Bradbury, Peter J. 6 ; Buckler, 1 ;
作者机构： 1.Northeast Agr Univ, Dept Anim Sci & Technol, Harbin, Heilongjiang, Peoples R China
2.Heilongjiang Acad Agr Sci, Inst Anim Husb, Harbin, Heilongjiang, Peoples R China; Washington State Univ, Dept Crop & Soil Sci, Pullman, WA 99164 USA
3.Chinese Acad Agr Sci, Inst Anim Sci, Beijing, Peoples R China
4.South China Agr Univ, Coll Anim Sci, Guangdong Prov Key Lab Agroanim Genom & Mol Breed, Guangzhou 510642, Guangdong, Peoples R China
5.China Agr Univ, Coll Anim Sci & Technol, Dept Anim Breeding & Genet, Beijing 100193, Peoples R China
6.USDA ARS, Ithaca, NY 14853 USA
期刊名称：HEREDITY （ 影响因子：3.821； 五年影响因子：4.553 ）
ISSN： 0018-067X
年卷期： 2018 年 121 卷 6 期
页码：
收录情况： SCI
摘要： 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 exclusive relationship (SUPER) algorithm, we substituted all available markers with estimated quantitative trait nucleotides (QTNs) to derive kinship. Second, we compressed individuals into groups based on kinship, and then used the groups as random effects instead of individuals. The two methods were named as SUPER BLUP (sBLUP) and compressed BLUP (cBLUP). Analyses on both simulated and real data demonstrated that these two methods offer flexibility for evaluating a variety of traits, covering a broadened realm of genetic architectures. For traits controlled by small numbers of genes, sBLUP outperforms Bayesian LASSO (least absolute shrinkage and selection operator). For traits with low heritability, cBLUP outperforms both gBLUP and Bayesian LASSO methods. We implemented these new BLUP alphabet series methods in an R package, Genome Association and Prediction Integrated Tool (GAPIT), available at http://zzlab.net/GAPIT.
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