Efficient Algorithms for Calculating Epistatic Genomic Relationship Matrices

Jiang, Yong; Reif, Jochen C.

doi:10.1534/genetics.120.303459

Cited by 22 publications

(26 citation statements)

References 52 publications

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“…Moreover, it has been shown that the superiority of epistasis models over the additive GBLUP in terms of predictive ability may vanish when the number of markers increases (Schrauf et al 2020 ). Also, the Hadamard products of the additive genomic relationship matrices provide only an approximation for the interaction effect model based on interactions between different loci (Martini et al 2020 ), and more correcting factors are required for interactions of higher degree (Jiang and Reif 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Future improvements will cover the use of epistatic genomic relationship matrix (EGRM) to control for the effect of diversity [29], as well as more advanced visualization approaches using either d3 or Cytoscape JavaScript library for dynamic web-based visualization. We also plan to add an end-to-end integration with cloud-based Random Forest implementation Vari-antSpark [15], to enable epistasis search within the ultra-high dimensional data of whole-genome sequencing cohorts.…”

Section: Discussionmentioning

confidence: 99%

Fast and Accurate Exhaustive Higher-Order Epistasis Search with BitEpi

Bayat

Hosking

Jain

et al. 2019

Preprint

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Motivation: Higher-order epistatic interactions can be the driver for complex genetic diseases. An exhaustive search is the most accurate method for identifying interactive SNPs. While there is a fast bitwise algorithm for pairwise exhaustive searching (BOOST), higher-order exhaustive searching has yet to be efficiently optimized. Results: In this paper, we introduce BitEpi, a program to detect and visualize higher-order epistatic interactions using an exhaustive search. BitEpi introduces a novel bitwise algorithm that can perform higher-order analysis more quickly and is the first bitwise algorithm to search for 4-SNP interactions. Furthermore, BitEpi increases detection accuracy by using a novel entropy-based power analysis. BitEpi visualizes significant interactions in a publication-ready interactive graph. BitEpi is 56 times faster than MDR for 4-SNP searching and is up to 1.33 and 2.09 times more accurate than BOOST and MPI3SNP respectively.

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“…sum of the diagonal elements) was equal to the number of animals [ 21 ]. As implied from a study by Vitezica et al [ 21 ] and as illustrated by Jiang et al [ 22 ], the Hadamard product between the corresponding matrices includes the squares of the same SNP and reciprocal products between two SNPs (e.g., SNP A by SNP B and SNP B by SNP A). Thus, for the AA interaction, which was shown to have non-negligible variance components for all traits, we calculated AA avoiding these issues and compared it with AA calculated with the Hadamard product of A .…”

Section: Methodsmentioning

confidence: 99%

Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

et al. 2021

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Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

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Efficient Algorithms for Calculating Epistatic Genomic Relationship Matrices

Cited by 22 publications

References 52 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

Fast and Accurate Exhaustive Higher-Order Epistasis Search with BitEpi

Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

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