Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

Wang, Jiabo; Zhou, Zhengkui; Zhang, Zhe; Li, Hui; Liu, Di; Zhang, Qin; Bradbury, Peter J.; Buckler, Edward S.; Zhang, Zhiwu

doi:10.1038/s41437-018-0075-0

Cited by 61 publications

(72 citation statements)

References 55 publications

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“…Bayesian LASSO (Least Absolute Shrinkage and Selection Operator; PLOS ONE | https://doi.org/10.1371/journal.pone.0228724 February 7, 2020 1 / 15 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 [5]), BayesA, BayesB [4], RKHS (Reproducing Kernel Hilbert Spaces; [6]), and genomic BLUP [7] are examples of the most notorious. Additionally, further adaptations have been proposed to principal methods such as compressed BLUP, SUPER BLUP [8], and GP with higher-effect markers differentially modeled (e.g., as fixed effects; [3,9,10]). Simulations and empirical studies have shown that the ability of GP to associate phenotypic patterns to genomic variations is intrinsically related to the genetic architecture of traits [4,11,12].…”

Section: Introductionmentioning

confidence: 99%

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

et al. 2020

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Genome-wide association studies (GWAS) is one of the most popular methods of studying the genetic control of traits. This methodology has been intensely performed on inbred genotypes to identify causal variants. Nonetheless, the lack of covariance between the phenotype of inbred lines and their offspring in cross-pollinated species (such as maize) raises questions on the applicability of these findings in a hybrid breeding context. To address this topic, we incorporated previously reported parental lines GWAS information into the prediction of a low heritability trait in hybrids. This was done by marker-assisted selection based on significant markers identified in the lines and by genomic prediction having these markers as fixed effects. Additive-dominance GWAS of hybrids, a non-conventional procedure, was also performed for comparison purposes. Our results suggest that incorporating information from parental inbred lines GWAS led to decreases in the predictive ability of hybrids. Correspondingly, inbred lines and hybrids-based GWAS yielded different results. These findings do not invalidate GWAS on inbred lines for selection purposes, but mean that it may not be directly useful for hybrid breeding. OPEN ACCESSCitation: Galli G, Alves FC, Morosini JS, Fritsche-Neto R (2020) On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids. PLoS ONE 15(2): e0228724. https://doi.org/10.1371/journal.

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Section: Introductionmentioning

confidence: 99%

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

et al. 2020

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“…Concerning additive models, genomic best linear unbiased prediction (GBLUP, Meuwissen et al, 2001;VanRaden, 2007) is a widely-used linear mixed model (Da et al, 2014;Rönnegård and Shen, 2016;Covarrubias-Pazaran et al, 2018). The computational steps involved in GBLUP are much faster than Bayesian methods and it has been difficult to find a method which consistently outperforms GBLUP when predicting complex traits (Wang et al, 2018). Daetwyler et al (2010) showed that BayesB can yield higher accuracy than GBLUP for traits controlled by a small number of quantitative trait nucleotides, emphasizing that the genetic architecture of the trait has an important impact on which method may predict better (Wimmer et al, 2013;Momen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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

Vojgani

Pook

Martini

et al. 2020

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We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested in 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 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. Over 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. The average increase across traits and locations was 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.Key MessageThe prediction accuracy of genomic prediction of phenotypes can be increased by only including top ranked pairwise SNP interactions into the prediction models.

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“…On the other hand, genetic diversity within the TR is fundamental for estimating marker effects appropriately (Norman et al, 2018). Finally, the region in the genome where individuals are more similar also affects genomic predictions, especially for oligogenic traits (Zhang et al, 2010;Wang et al, 2018). Some strategies have been designed to weight genetic relationship matrices on the basis of their marker effects regardless of their position or linkage disequilibrium [trait-specific relationship matrix best linear unbiased prediction (taBLUP)] (Zhang et al, 2010), or weighting only bin-selected quantitative trait nucleotides [super best linear unbiased prediction (sBLUP)] (Wang et al, 2014).…”

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confidence: 99%

Training Population Optimization for Genomic Selection

et al. 2019

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The effectiveness of genomic selection in breeding programs depends on the phenotypic quality and depth, the prediction model, the number and type of molecular markers, and the size and composition of the training population (TR). Furthermore, population structure and diversity have a key role in the composition of the optimal training sets. Our goal was to compare strategies for optimizing the TR for specific testing populations (TE). A total of 1353 wheat (Triticum aestivum L.) and 644 rice (Oryza sativa L.) advanced lines were evaluated for grain yield in multiple environments. Several within-TR optimization strategies were compared to identify groups of individuals with increased predictive ability. Additionally, optimization strategies to choose individuals from the TR with higher predictive ability for a specific TE were compared. There is a benefit in considering both the population structure and the relationship between the TR and the TE when designing an optimal TR for genomic selection. A weighted relationship matrix with stratified sampling is the best strategy for forward predictions of quantitative traits in populations several generations apart. G enomic selection (GS) consists of selecting individuals from a TE on the basis of genotypic values predicted from their genome-wide molecular marker scores and a statistical model adjusted with individuals that have phenotypic and genotypic information (Meuwissen et al., 2001). The group of individuals that were phenotyped and genotyped is called the TR (Heffner et al. 2009). Genomic selection is preferred over marker-assisted selection approaches for complex traits (Habier et al., 2007; Lorenz et al., 2011) because it includes all molecular markers in the prediction model and because it considers the quantitative trait loci of both major and minor effects (Xu, 2003; Jannink et al., 2010; Poland and Rife, 2012; Smith et al., 2018). Simulated and empirical cross-validation studies in plants show that GS can accelerate progress in plant breeding compared with marker-assisted selection, resulting in higher genetic gains (

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Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

Cited by 61 publications

References 55 publications

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids

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

Training Population Optimization for Genomic Selection

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