Implications of <scp>SNP</scp> weighting on single‐step genomic predictions for different reference population sizes

Lourenço, Daniela; Fragomeni, B. O.; Bradford, H. L.; Menezes, Isabela Rocha; Ferraz, José Bento Sterman; Aguilar, Ignácio; Tsuruta, S.; Misztal, I.

doi:10.1111/jbg.12288

Cited by 46 publications

(58 citation statements)

References 29 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We expected that WssGBLUP would perform better for the scenarios SIM2 through SIM5, and especially for SIM4 and SIM5. Lourenco et al (2017) reported that for less polygenic traits (such as the simulated scenarios mentioned above), the accuracy might be higher when using WssGBLUP instead of ssGBLUP. WssGBLUP is advantageous for traits with a reduced number of causative genes because its assumption is similar to the genetic architecture of those traits: a finite number of markers affecting the trait.…”

Section: Ssgblup Vs Wssgblupmentioning

confidence: 99%

See 1 more Smart Citation

Comparing Alternative Single-Step GBLUP Approaches and Training Population Designs for Genomic Evaluation of Crossbred Animals

et al. 2020

View full text Add to dashboard Cite

As crossbreeding is extensively used in some livestock species, we aimed to evaluate the performance of single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) methods to predict Genomic Estimated Breeding Values (GEBVs) of crossbred animals. Different training population scenarios were evaluated: (SC1) ssGBLUP based on a single-trait model considering purebred and crossbred animals in a joint training population; (SC2) ssGBLUP based on a multiple-trait model to enable considering phenotypes recorded in purebred and crossbred training animals as different traits; (SC3) WssGBLUP based on a single-trait model considering purebred and crossbred animals jointly in the training population (both populations were used for SNP weights' estimation); (SC4) WssGBLUP based on a single-trait model considering only purebred animals in the training population (crossbred population only used for SNP weights' estimation); (SC5) WssGBLUP based on a single-trait model and the training population characterized by purebred animals (purebred population used for SNP weights' estimation). A complex trait was simulated assuming alternative genetic architectures. Different scaling factors to blend the inverse of the genomic (G −1 ) and pedigree (A −1 22 ) relationship matrices were also tested. The predictive performance of each scenario was evaluated based on the validation accuracy and regression coefficient. The genetic correlations across simulated populations in the different scenarios ranged from moderate to high (0.71-0.99). The scenario mimicking a completely polygenic trait (h 2 QTL = 0) yielded the lowest validation accuracy (0.12; for SC3 and SC4). The simulated scenarios assuming 4,500 QTLs affecting the trait and h 2 QTL = h 2 resulted in the greatest GEBV accuracies (0.47; for SC1 and SC2). The regression coefficients ranged from 0.28 (for SC3 assuming polygenic effect) to 1.27 (for SC2 considering 4,500 QTLs). In general, SC3 and SC5 resulted in inflated GEBVs, whereas other scenarios yielded deflated GEBVs. The scaling factors used to combine G −1 and A −1 22 had a small influence on the validation accuracies, but a greater effect on the regression coefficients. Due to the complexity of multiple-trait models Alvarenga et al. ssGBLUP Approaches for Crossbred Evaluation and WssGBLUP analyses, and a similar predictive performance across the methods evaluated, SC1 is recommended for genomic evaluation in crossbred populations with similar genetic structures [moderate-to-high (0.71-0.99) genetic correlations between purebred and crossbred populations].

show abstract

Section: Ssgblup Vs Wssgblupmentioning

confidence: 99%

“…The WssGBLUP has been successfully applied to several genomic prediction studies (Zhang et al, 2016;Lourenco et al, 2017;Guarini et al, 2019). However, to our best knowledge, there are no reports evaluating the prediction ability of WssGBLUP in crossbred animals, especially in F1 populations.…”

Section: Introductionmentioning

confidence: 99%

Comparing Alternative Single-Step GBLUP Approaches and Training Population Designs for Genomic Evaluation of Crossbred Animals

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Different formulas can be used to calculate SNP variance, but all of them are approximations. Several authors have reported decrease in GEBV accuracy and increase in bias over iterations [ 59 , 60 ] when variance is calculated based on squared SNP effects, especially for more polygenic traits. This is because SNP variance would reach extreme values over iterations.…”

Section: Software Methods and Algorithmsmentioning

confidence: 99%

“…Considering SNP variances when constructing G in ssGBLUP seems to improve the accuracy of predicting GEBV for data sets with small number of genotyped animals, but marginal or no improvement was observed for large genotyped populations (i.e., >10 k genotyped animals) [ 60 ], even for less polygenic traits. If the data allows to accurately estimate SNP effects, there is no advantage in selecting SNPs and tagging chromosome segments differently.…”

Section: Software Methods and Algorithmsmentioning

confidence: 99%

Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90

Lourenço

Legarra

Tsuruta

et al. 2020

Genes

View full text Add to dashboard Cite

Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis processing. Therefore, the incorporation of data on genotyped and non-genotyped animals in this method is straightforward. Since 2009, two main implementations of single-step were proposed. One is called single-step genomic best linear unbiased prediction (ssGBLUP) and uses single nucleotide polymorphism (SNP) to construct the genomic relationship matrix; the other is the single-step Bayesian regression (ssBR), which is a marker effect model. Under the same assumptions, both models are equivalent. In this review, we focus solely on ssGBLUP. The implementation of ssGBLUP into the BLUPF90 software suite was done in 2009, and since then, several changes were made to make ssGBLUP flexible to any model, number of traits, number of phenotypes, and number of genotyped animals. Single-step GBLUP from the BLUPF90 software suite has been used for genomic evaluations worldwide. In this review, we will show theoretical developments and numerical examples of ssGBLUP using SNP data from regular chips to sequence data.

show abstract

“…Alternatively, a number of studies proposed to weight the SNPs according to their effect or their variance to build the GRM in order to increase GBLUP prediction accuracy [10][11][12]. Different strategies have been proposed, relying on iterative schemes using SNP effects estimated from the GBLUP model [13][14][15][16] or on the posterior effects or variances obtained from some Bayesian methods [11,12,17]. It is also possible to directly estimate the variance associated with a single SNP or a subset of SNPs by fitting a specific GRM in a REML analysis (e.g., [18,19]).…”

Section: Introductionmentioning

confidence: 99%

Performances of Adaptive MultiBLUP, Bayesian regressions, and weighted-GBLUP approaches for genomic predictions in Belgian Blue beef cattle

Duarte

Gori²,

Hubin³

et al. 2020

BMC Genomics

View full text Add to dashboard Cite

Background: Genomic selection has been successfully implemented in many livestock and crop species. The genomic best linear unbiased predictor (GBLUP) approach, assigning equal variance to all SNP effects, is one of the reference methods. When large-effect variants contribute to complex traits, it has been shown that genomic prediction methods that assign a higher variance to subsets of SNP effects can achieve higher prediction accuracy. We herein compared the efficiency of several such approaches, including the Adaptive MultiBLUP (AM-BLUP) that uses local genomic relationship matrices (GRM) to automatically identify and weight genomic regions with large effects, to predict genetic merit in Belgian Blue beef cattle. Results: We used a population of approximately 10,000 genotyped cows and their phenotypes for 14 traits, mostly related to muscular development and body dimensions. According to the trait, we found that 4 to 25% of the genetic variance could be associated with 2 to 12 genomic regions harbouring large-effect variants. Noteworthy, three previously identified recessive deleterious variants presented heterozygote advantage and were among the most significant SNPs for several traits. The AM-BLUP resulted in increased reliability of genomic predictions compared to GBLUP (+ 2%), but Bayesian methods proved more efficient (+ 3%). Overall, the reliability gains remained thus limited although higher gains were observed for skin thickness, a trait affected by two genomic regions having particularly large effects. Higher accuracies than those from the original AM-BLUP were achieved when applying the Bayesian Sparse Linear Mixed Model to pre-select groups of SNPs with large effects and subsequently use their estimated variance to build a weighted GRM. Finally, the single-step GBLUP performed best and could be further improved (+ 3% prediction accuracy) by using these weighted GRM.

show abstract

Implications of SNP weighting on single‐step genomic predictions for different reference population sizes

Cited by 46 publications

References 29 publications

Comparing Alternative Single-Step GBLUP Approaches and Training Population Designs for Genomic Evaluation of Crossbred Animals

Comparing Alternative Single-Step GBLUP Approaches and Training Population Designs for Genomic Evaluation of Crossbred Animals

Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90

Performances of Adaptive MultiBLUP, Bayesian regressions, and weighted-GBLUP approaches for genomic predictions in Belgian Blue beef cattle

Contact Info

Product

Resources

About