Efficient weighting methods for genomic best linear unbiased prediction (BLUP) adaption to the genetic architectures of quantitative traits
Ren, Duanyang (2020), Efficient weighting methods for genomic best linear unbiased prediction (BLUP) adaption to the genetic architectures of quantitative traits, Dryad, Dataset, https://doi.org/10.5061/dryad.9zw3r22bz
Genomic best linear unbiased prediction (GBLUP) assumes equal variance for all marker effects, which is suitable for traits that conform to the infinitesimal model. For traits controlled by major genes, Bayesian methods with shrinkage priors or genome-wide association study (GWAS) methods can be used to identify causal variants effectively. The information from Bayesian/GWAS methods can be used to construct the weighted genomic relationship matrix (G). However, it remains unclear which methods perform best for traits varying in genetic architecture. Therefore, we developed several methods to optimize the performance of weighted GBLUP and compare them with other available methods using simulated and real datasets. First, two types of methods (marker effects with local-shrinkage or normal prior) were used to obtain test statistics and estimates for each marker effect. Second, three weighted G matrices were constructed based on the marker information from the first step: (1) the genomic-feature weighted G (GFWG), (2) the estimated marker-variance weighted G (EVWG), and (3) the absolute value of estimated marker-effect weighted G (AEWG). Following the above process, six different weighted GBLUP methods (local-shrinkage/normal prior GF/EV/AE-WGBLUP) were proposed for genomic prediction. Analyses with both simulated and real data demonstrated that these options offer flexibility for optimizing the weighted GBLUP for traits with a broad spectrum of genetic architectures. The advantage of weighting methods over GBLUP in terms of accuracy were trait dependent, ranging from 14.8% to marginal for simulated traits and from 44% to marginal for real traits. Local-shrinkage prior EVWGBLUP is superior for traits mainly controlled by loci of large effect. Normal prior AEWGBLUP performs well for traits mainly controlled by loci of moderate effect. For traits controlled by some loci with large effects (explain 25%~50% genetic variance) and a range of loci with small effects, GFWGBLUP has advantages. In conclusion, the optimal weighted GBLUP method for genomic selection should take both the genetic architecture and number of QTLs of traits into consideration carefully.
Documentations of simulated traits (A&B, where A is the heritability and B is the QTL number) including information of individuals in the reference groups (tradataX, where X is the replicate number), information of individuals in the validation groups (candidateX, where X is the replicate number), and information of QTLs (qtlinfoX, where X is the replicate number). The first four columns in tradataX/candidatedataX represent individual ID Number, true breeding value, environmental effect, and phenotypic value, respectively. The six columns of qtlinfoX represent QTL locus (denoted by the SNP site position), allele frequency, other allele frequency, QTL effect variance, QTL genotypic variance and QTL allele effect respectively.
National Natural Science Foundation of China, Award: 31972560