Phenotype Prediction Under Epistasis

Vojgani, Elaheh; Pook, Torsten; Simianer, Henner

doi:10.1007/978-1-0716-0947-7_8

Cited by 6 publications

(18 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the approaches of Jiang and Reif (2015) and Martini et al (2016) only capture the interactions whose products differ from zero (i.e., {22} genotype combinations for 0, 2 coded markers), our approach captures all possible genotype combinations ({00}, {02}, {20}, and {22}). Further, these epistasis relationship matrices and interaction effects were computed by bit-wise computations via the R-package miraculix (Schlather 2020), which carries out matrix multiplications about 15 times faster than regular matrix multiplications on genotype data in EpiGP R-package (Vojgani et al 2021). In the analyzed datasets containing up to 30′212 SNPs (and thus 456′397′578 interactions), the computing time required to set up the sERRBLUP relationship matrix was about 810 min out of which around 330 min was required to estimate all pairwise SNP interaction effects and 480 min was required to set up the sERRBLUP relationship matrix for selected proportion of interactions by utilizing the R-package miraculix with 15 cores on a server cluster with Intel E5-2650 (2X12 core 2.2 GHz) processors.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Computing times for sERRBLUP scale approximately quadratic in the number of markers were considered. The released EpiGP R-package (Vojgani et al 2021), which is available at https:// github. com/ evojg ani/ EpiGP, has been utilized for ERRB-LUP and sERRBLUP genomic prediction of phenotypes.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, the inclusion of epistasis, defined as the interaction between loci (Falconer and Mackay 1996;Lynch and Walsh 1998), into the genomic prediction model results in more accurate phenotype prediction (Hu et al 2011;Wang et al 2012;Mackay 2014;Martini et al 2016;Vojgani et al 2021) due to the considerable contribution of epistasis in genetic variation of quantitative traits (Mackay 2014). In this context, several statistical models have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In this approach, the full epistasis model was reduced to a model with a subset of the largest epistatic interaction effects, resulting in an increase in predictive ability (Martini et al 2016), through borrowing information across environments. Vojgani et al (2021) showed that the prediction accuracy can be increased even further by selecting the interactions with the highest absolute effect sizes / variances in the epistasis model. The resulting higher computational needs were offset by the development of a highly efficient software package "EpiGP" (Vojgani et al 2019) to perform computations in a bit-wise manner (Schlather 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The resulting higher computational needs were offset by the development of a highly efficient software package "EpiGP" (Vojgani et al 2019) to perform computations in a bit-wise manner (Schlather 2020). Thus, enabling to conduct such predictions with data sets of practically relevant size across environments in the same year, both with respect to sample size and number of markers (Vojgani et al 2021). As the number of interactions to account for increases quadratically with the number of included variables, the computational load of methods like EpiGP can quickly go out of control as a model with 600.000 SNPs, as present in high density arrays (Kranis et al 2013;Unterseer et al 2014), would result in more than a hundred billion interactions to account for.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bivariate Genomic Prediction of Phenotypes by Selecting Epistatic Interactions Across Years Based on Haplotype Blocks and Pruned Sets of SNPs

Vojgani¹,

Pook²,

Hölker³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this study, we investigated the ability of three models to improve prediction accuracy by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The results indicate that bivariate ERRBLUP is almost identical to bivariate GBLUP in prediction accuracy, while bivariate sERRBLUP has the highest prediction accuracy in most cases. The obtained prediction accuracies were similar when utilizing pruned sets of SNPs and haplotype blocks, while utilizing haplotype blocks reduces the computational load significantly compared to utilizing pruned sets of SNPs. The prediction accuracies of bivariate GBLUP, ERRBLUP and sERRBLUP have been assessed across eight phenotypic traits and studied datasets from 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot. We further investigated the genomic correlation, phenotypic correlation and trait heritability as factors affecting the bivariate models’ prediction accuracy, with genetic correlation between growing seasons being the most important one. For all three considered model architectures results were far worse when using a univariate version of the model.

show abstract

Epistasis and pleiotropy‐induced variation for plant breeding

Dwivedi,

Heslop‐Harrison,

Amas

et al. 2024

Plant Biotechnology Journal

View full text Add to dashboard Cite

SummaryEpistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy‐based phenotypic trade‐offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non‐pleiotropy‐induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large‐scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

show abstract

Phenotype Prediction Under Epistasis

Cited by 6 publications

References 34 publications

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

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

Bivariate Genomic Prediction of Phenotypes by Selecting Epistatic Interactions Across Years Based on Haplotype Blocks and Pruned Sets of SNPs

Epistasis and pleiotropy‐induced variation for plant breeding

Contact Info

Product

Resources

About