Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome

Karaman, Emre; Lund, Mogens Sandø; Su, Guosheng

doi:10.1038/s41437-019-0273-4

Cited by 20 publications

(25 citation statements)

References 63 publications

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“…The GEBV bias obtained in SC3 and SC5 may be due to the inefficient estimation of SNP weights in predicting crossbred information, as a merged dataset (purebred and crossbred) or just purebred information was used to estimate the SNP weights to predict GEBVs in the crossbred animals in SC3 and SC5, respectively. As previously mentioned, alternative ways to derive the SNP weights have been proposed, which could lead to better predictive performance Karaman et al, 2019). In general, less biased GEBVs were obtained in SC2, which is in agreement with several studies in the literature with regards to the superiority of multiple-trait models to predict the performance of crossbred populations (Tusell et al, 2016;Pocrnic et al, 2019).…”

Section: Regression Coefficientssupporting

confidence: 86%

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

et al. 2020

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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].

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Section: Regression Coefficientssupporting

confidence: 86%

“…The weights derivation used is the easiest way to implement the WssGBLUP in commercial breeding programs, which justify the application of the method. Alternative ways to derive the SNP weights have been proposed and might result in better predictive ability Karaman et al, 2019), for example through Bayesian approaches.…”

Section: Ssgblup Vs Wssgblupmentioning

confidence: 99%

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

et al. 2020

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“…In addition to LD patterns, it is also possible that QTL effects and/or QTL allele frequencies differ among the breeds, while some QTL may only segregate in one breed [ 25 , 40 ]. Needless to say, even if the QTL properties were the same among the breeds, SNP effects would still be different to the extent that LD between SNPs and QTL differs between them [ 11 , 12 , 30 , 41 ].…”

Section: Discussionmentioning

confidence: 99%

“…In BayesA, it is assumed that each SNP (1 SNP) follows a normal distribution with null mean and a locus-specific variance, while in GBLUP it is assumed that all SNPs (WG) have null means and a common variance. To consider the heterogeneous variance of SNP effects among different genome regions using model ( 1 ), the matrix of genotypes and vector of SNP effects were partitioned into S subsets each with loci ( ), and priors were assigned to each sub-vector of : [ 29 , 30 ]. The (s) were further assigned a scaled inverse chi-square prior with a number of degrees of freedom ( df ) and a scale parameter ( S ): .…”

Section: Methodsmentioning

confidence: 99%

Genomic prediction using a reference population of multiple pure breeds and admixed individuals

Karaman

Croué

et al. 2021

Genet Sel Evol

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Background In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip. Results For purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population’s (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds. Conclusions Combining all available data, pure breeds’ and admixed population’s data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.

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“…Besides, it was shown that the window procedures were more capable than single ones to decrease uncertainty [ 47 ] and better capturing the signal from the QTL in that region for traits influenced by few QTL with a relatively large effect [ 6 , 46 ]. Previous reports in simulation studies showed that common variances on the partitioning genome region followed by the selection of 100 SNPs within those selected regions resulted in a higher accuracy over one SNP specific variance [ 14 , 48 ]. Also, Teissier et al [ 35 ] investigated a similar approach (WssGBLUP) to the analysis of milk production traits, udder type traits, and somatic cell scores in the two French dairy goat populations and used a window size of 40 SNPs for all traits.…”

Section: Discussionmentioning

confidence: 99%

Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Mehrban

Naserkheil

Lee

et al. 2021

Genes

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The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

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Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome

Cited by 20 publications

References 63 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

Genomic prediction using a reference population of multiple pure breeds and admixed individuals

Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

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