Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments

Abstract

Key Message The accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. Abstract We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings for predictions within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Across all studied traits, locations and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from −0.3 to + 27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP, with an average increase of 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for “sparse testing” approaches in which only a subset of the lines/hybrids of interest is observed at each location.

Choosing the right tool: Leveraging of plant genetic resources in wheat (Triticum aestivum L.) benefits from selection of a suitable genomic prediction model

“…The importance of additive-by-additive epistasis was found in many studies on wheat (Martini et al 2016 ; Jiang et al 2017 ; Raffo et al 2022 ) but by extension also for other autogamous plant species such as Arabidopsis ( Arabidopsis thaliana L.) (Reif et al 2009 ) and pigeonpea ( Cajanus cajan (L.) Millsp.) (Saxena et al 2021 ) and furthermore, also in doubled haploid lines of rapeseed ( Brassica napus L.) (Bocianowski et al 2017 ) and maize (Vojgani et al 2021 ). Due to the absence of dominance effects in wheat inbred lines, additive-by-additive epistasis is of large importance (Raffo et al 2022 ).…”

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks

“…Additionally, loci that were in high level of pairwise linkage disequilibrium (LD) were removed [38] through linkage disequilibrium based SNP pruning with PLINK v1.07 [33,39] with the parameters 50, 5 and 2 indicating the SNPs window size, the number of SNPs by which the SNP window is shifted and the variance inflation factor, respectively. This resulted in a data panel containing 25'437 SNPs for KE and 30'212 SNPs for PE [36]. Note that even a panel of 25'000 SNPs results in more than 1 billion SNP interactions to account for.…”

“…We used the bivariate statistical framework as the basis of the genomic prediction models which has been proposed in the recent work by [36]. In this regard, GBLUP, ERRBLUP and sERRBLUP as three different methods described in Vojgani et al (2021) [36] were used for genomic prediction of phenotypes which differ in dispersion matrices representing their covariance structure of the genetic effects. GBLUP as an additive model is based on a genomic relationship matrix calculated according to VanRaden (2008) [43].…”

“…The aim of this study is to compare the obtained prediction accuracies of bivariate genomic prediction models incorporating epistatic interactions through borrowing information across years when utilizing haplotype blocks versus pruned sets of SNPs, as a novel approach in epistasis models. As the accuracy of marker-based genomic prediction of phenotypes was shown to be increased by both borrowing information across environments and/or years [11,20] and the inclusion of epistasis into the prediction model [6,36], we combine these two approaches in both SNP-based and haplotype-based genomic prediction models to make the best use of the available information. We further find the optimum proportion of epistatic interactions of pruned sets of SNPs and haplotype blocks to be kept in the model in the variable selection step across years and compare their obtained predictive abilities.…”

Choosing the right tool: Leveraging of plant genetic resources in wheat (Triticum aestivum L.) benefits from selection of a suitable genomic prediction model

“…The importance of additive-by-additive epistasis was found in many studies on wheat (Martini et al 2016;Jiang et al 2017;Raffo et al 2022) but by extension also for other autogamous plant species such as Arabidopsis (Arabidopsis thaliana L.) (Reif et al 2009) and pigeonpea (Cajanus cajan (L.) Millsp.) (Saxena et al 2021) and furthermore, also in doubled haploid lines of rapeseed (Brassica napus L.) (Bocianowski et al 2017) and maize (Vojgani et al 2021). Due to the absence of dominance effects in wheat inbred lines, additive-by-additive epistasis is of large importance (Raffo et al 2022).…”