Location: Plant, Soil and Nutrition ResearchTitle: Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits Author
|Wang, Jiabo - Northeast Agricultural University|
|Zhou, Zhengkui - Chinese Academy Of Agricultural Sciences|
|Li, Hui - Northeast Agricultural University|
|Liu, Di - Heilongjiang Academy Of Agricultural Sciences|
|Zhang, Qin - China Agricultural University|
|Buckler, Edward - Ed|
|Zhang, Zhiwu - Northeast Agricultural University|
Submitted to: Heredity
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/17/2018
Publication Date: 5/16/2018
Citation: Wang, J., Zhou, Z., Li, H., Liu, D., Zhang, Q., Bradbury, P., Buckler IV, E.S., Zhang, Z. 2018. Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity. doi: 10.1038/s41437-018-0075-0
DOI: https://doi.org/10.1038/s41437-018-0075-0 Interpretive Summary: The cost of measuring performance of plant and animal varieties in the field has been gradually increasing for many years. On the other hand, the cost of using genetic markers has been decreasing rapidly. As a result, the use of genetic markers to predict field performance is replacing some field evaluations and helping plant and animal breeders make improvements faster. This study describes the development and testing of two new methods for using markers to predict performance. The new methods are relatively fast and easy to use and show superior performance for certain types of traits compared to other existing methods. In addition, the methods have been implemented in a freely available software package. The improved software has the potential to improve the outcomes of plant and animal breeding programs. Because the agricultural sector contributes about one trillion dollars to the US economy annually, even small incremental improvements in variety performance can have a huge impact.
Technical 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 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.