Journal article
Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits
Heredity, Vol.121(6), pp.648-662
12/2018
Handle:
https://hdl.handle.net/2376/118206
PMCID: PMC6221880
PMID: 29765161
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 .
Metrics
16 Record Views
Details
- Title
- Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits
- Creators
- Jiabo Wang - Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USAZhengkui Zhou - Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, ChinaZhe Zhang - Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, ChinaHui Li - Department of Animal Science and Technology, Northeast Agricultural University, Harbin, ChinaDi Liu - Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Science, Harbin, ChinaQin Zhang - Department of Animal Breeding and Genetics, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, ChinaPeter J Bradbury - United States Department of Agriculture - Agricultural Research Service, Ithaca, New York, USAEdward S Buckler - United States Department of Agriculture - Agricultural Research Service, Ithaca, New York, USAZhiwu Zhang - Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, USA. Zhiwu.Zhang@wsu.edu
- Publication Details
- Heredity, Vol.121(6), pp.648-662
- Academic Unit
- Biological Systems Engineering, Department of; Crop and Soil Sciences, Department of
- Publisher
- England
- Identifiers
- 99900548585601842
- Language
- English
- Resource Type
- Journal article