Efficient weighting methods for genomic best linear-unbiased prediction (BLUP) adapted to the genetic architectures of quantitative traits

Abstract

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 data sets. 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, (2) the estimated marker-variance-weighted G, and (3) the absolute value of the estimated marker-effect-weighted G. Following the above process, six different weighted GBLUP methods (local shrinkage/normal-prior GF/EV/AEWGBLUP) 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 was trait dependant, 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 a 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.

Fig. 1. The power to detect QTLs based on four GWAS/GP methods in different GF set levels for simulated traits with different genetic architectures.

The power expressed as the percentage of the detected number of QTL in the GF set occupied the total number of QTL.

Fig. 2. The accuracy of GFWGBLUP based on four GWAS/GP methods in different GF set levels for simulated traits with different genetic architectures.

The dashed lines indicate the prediction accuracies of general GBLUP.

Fig. 3. The accuracy of EVWGBLUP and AEWGBLUP based on four GWAS/GP methods for simulated traits with different genetic architectures.

For the last group bars (the far right side of each plot), the weights are 1 for all markers, so these weighted GBLUPs are the same as the general GBLUP, and these groups of bars were used as the reference. The standard errors are indicated by the whiskers on the bars. ***indicates significant differences at P < 0.001, **indicates significant differences at 0.001 < P < 0.01.

Fig. 4. The performance of different weighted GBLUPs for traits controlled by loci with small and large/moderate effects simultaneously.

The heritabilities and phenotypic variances for scenarios 6a–f were all 0.8 and 100, respectively. In scenarios 6a–c, loci of a large effect explained 20%, 40%, and 60% of the phenotypic variances, respectively; the remaining genetic variance was assigned to 5000 loci of a small effect evenly. In scenarios 6d–f, loci of a moderate effect explained 20%, 40%, and 60% of the phenotypic variances, respectively; the remaining genetic variance was assigned to 5000 loci of a small effect evenly. A single locus of a large effect in scenarios 6a–c accounts for 2% of the phenotypic variance, and each locus of moderate effect in scenarios 6d–f accounts for 0.4% of the phenotypic variance. All scenarios were replicated ten times. The standard errors are indicated by the whiskers on the bars. Paired t-test was applied to compare the difference between methods, with P values adjusted by Bonferroni correction. ***indicates significant differences at P < 0.001, **indicates significant differences at 0.001 < P < 0.01, NS indicates no statistically significant difference.