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

Heredity ◽  
2019 ◽  
Vol 124 (2) ◽  
pp. 274-287 ◽  
Author(s):  
Emre Karaman ◽  
Mogens S. Lund ◽  
Guosheng Su
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Single Step ◽  
Bayesian Regression ◽  
Practical Implementation ◽  
Whole Genome ◽  
Single Nucleotide ◽  
Variance Estimates ◽  
Region Size ◽  
Variance Structure

Abstract Widely used genomic prediction models may not properly account for heterogeneous (co)variance structure across the genome. Models such as BayesA and BayesB assume locus-specific variance, which are highly influenced by the prior for (co)variance of single nucleotide polymorphism (SNP) effect, regardless of the size of data. Models such as BayesC or GBLUP assume a common (co)variance for a proportion (BayesC) or all (GBLUP) of the SNP effects. In this study, we propose a multi-trait Bayesian whole genome regression method (BayesN0), which is based on grouping a number of predefined SNPs to account for heterogeneous (co)variance structure across the genome. This model was also implemented in single-step Bayesian regression (ssBayesN0). For practical implementation, we considered multi-trait single-step SNPBLUP models, using (co)variance estimates from BayesN0 or ssBayesN0. Genotype data were simulated using haplotypes on first five chromosomes of 2200 Danish Holstein cattle, and phenotypes were simulated for two traits with heritabilities 0.1 or 0.4, assuming 200 quantitative trait loci (QTL). We compared prediction accuracy from different prediction models and different region sizes (one SNP, 100 SNPs, one chromosome or whole genome). In general, highest accuracies were obtained when 100 adjacent SNPs were grouped together. The ssBayesN0 improved accuracies over BayesN0, and using (co)variance estimates from ssBayesN0 generally yielded higher accuracies than using (co)variance estimates from BayesN0, for the 100 SNPs region size. Our results suggest that it could be a good strategy to estimate (co)variance components from ssBayesN0, and then to use those estimates in genomic prediction using multi-trait single-step SNPBLUP, in routine genomic evaluations.

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

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Beef Cattle ◽  
Genomic Prediction ◽  
Quantitative Trait ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Best Linear Unbiased

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.

scholarly journals Reliabilities of Genomic Prediction for Young Stock Survival Traits Using 54K SNP Chip Augmented With Additional Single-Nucleotide Polymorphisms Selected From Imputed Whole-Genome Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Grum Gebreyesus ◽  
Mogens Sandø Lund ◽  
Goutam Sahana ◽  
Guosheng Su
Keyword(s):  
Genomic Prediction ◽  
Component Model ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Young Stock ◽  
Recessive Lethal ◽  
Percentage Points ◽  
Two Component ◽  
Prediction Reliability

This study investigated effects of integrating single-nucleotide polymorphisms (SNPs) selected based on previous genome-wide association studies (GWASs), from imputed whole-genome sequencing (WGS) data, in the conventional 54K chip on genomic prediction reliability of young stock survival (YSS) traits in dairy cattle. The WGS SNPs included two groups of SNP sets that were selected based on GWAS in the Danish Holstein for YSS index (YSS_SNPs, n = 98) and SNPs chosen as peaks of quantitative trait loci for the traits of Nordic total merit index in Denmark–Finland–Sweden dairy cattle populations (DFS_SNPs, n = 1,541). Additionally, the study also investigated the possibility of improving genomic prediction reliability for survival traits by modeling the SNPs within recessive lethal haplotypes (LET_SNP, n = 130) detected from the 54K chip in the Nordic Holstein. De-regressed proofs (DRPs) were obtained from 6,558 Danish Holstein bulls genotyped with either 54K chip or customized LD chip that includes SNPs in the standard LD chip and some of the selected WGS SNPs. The chip data were subsequently imputed to 54K SNP together with the selected WGS SNPs. Genomic best linear unbiased prediction (GBLUP) models were implemented to predict breeding values through either pooling the 54K and selected WGS SNPs together as one genetic component (a one-component model) or considering 54K SNPs and selected WGS SNPs as two separate genetic components (a two-component model). Across all the traits, inclusion of each of the selected WGS SNP sets led to negligible improvements in prediction accuracies (0.17 percentage points on average) compared to prediction using only 54K. Similarly, marginal improvement in prediction reliability was obtained when all the selected WGS SNPs were included (0.22 percentage points). No further improvement in prediction reliability was observed when considering random regression on genotype code of recessive lethal alleles in the model including both groups of the WGS SNPs. Additionally, there was no difference in prediction reliability from integrating the selected WGS SNP sets through the two-component model compared to the one-component GBLUP.

scholarly journals Leveraging Transcriptomics Data for Genomic Prediction Models in Cassava

2017 ◽  
Author(s):  
Roberto Lozano ◽  
Dunia Pino del Carpio ◽  
Teddy Amuge ◽  
Ismail Siraj Kayondo ◽  
Alfred Ozimati Adebo ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Breeding Population ◽  
Biological Information ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Sources Of Information ◽  
Transcriptomics Data ◽  
The Individual

AbstractBackgroundGenomic prediction models were, in principle, developed to include all the available marker information; with this approach, these models have shown in various crops moderate to high predictive accuracies. Previous studies in cassava have demonstrated that, even with relatively small training populations and low-density GBS markers, prediction models are feasible for genomic selection. In the present study, we prioritized SNPs in close proximity to genome regions with biological importance for a given trait. We used a number of strategies to select variants that were then included in single and multiple kernel GBLUP models. Specifically, our sources of information were transcriptomics, GWAS, and immunity-related genes, with the ultimate goal to increase predictive accuracies for Cassava Brown Streak Disease (CBSD) severity.ResultsWe used single and multi-kernel GBLUP models with markers imputed to whole genome sequence level to accommodate various sources of biological information; fitting more than one kinship matrix allowed for differential weighting of the individual marker relationships. We applied these GBLUP approaches to CBSD phenotypes (i.e., root infection and leaf severity three and six months after planting) in a Ugandan Breeding Population (n = 955). Three means of exploiting an established RNAseq experiment of CBSD-infected cassava plants were used. Compared to the biology-agnostic GBLUP model, the accuracy of the informed multi-kernel models increased the prediction accuracy only marginally (1.78% to 2.52%).ConclusionsOur results show that markers imputed to whole genome sequence level do not provide enhanced prediction accuracies compared to using standard GBS marker data in cassava. The use of transcriptomics data and other sources of biological information resulted in prediction accuracies that were nominally superior to those obtained from traditional prediction models.

scholarly journals Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1591
Author(s):  
Sebastian Michel ◽  
Franziska Löschenberger ◽  
Ellen Sparry ◽  
Christian Ametz ◽  
Hermann Bürstmayr
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Wheat Breeding ◽  
Single Step ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Record Keeping ◽  
Line Breeding ◽  
Cost Efficient

The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain.

scholarly journals Genomic Prediction Using LD-Based Haplotypes Inferred From High-Density Chip and Imputed Sequence Variants in Chinese Simmental Beef Cattle

2021 ◽  
Vol 12 ◽  
Author(s):  
Hongwei Li ◽  
Bo Zhu ◽  
Ling Xu ◽  
Zezhao Wang ◽  
Lei Xu ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Prediction ◽  
Whole Genome Sequence ◽  
Data Sets ◽  
Whole Genome ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Individual Single Nucleotide Polymorphism ◽  
Improve Accuracy ◽  
The Individual

A haplotype is defined as a combination of alleles at adjacent loci belonging to the same chromosome that can be transmitted as a unit. In this study, we used both the Illumina BovineHD chip (HD chip) and imputed whole-genome sequence (WGS) data to explore haploblocks and assess haplotype effects, and the haploblocks were defined based on the different LD thresholds. The accuracies of genomic prediction (GP) for dressing percentage (DP), meat percentage (MP), and rib eye roll weight (RERW) based on haplotype were investigated and compared for both data sets in Chinese Simmental beef cattle. The accuracies of GP using the entire imputed WGS data were lower than those using the HD chip data in all cases. For DP and MP, the accuracy of GP using haploblock approaches outperformed the individual single nucleotide polymorphism (SNP) approach (GBLUP_In_Block) at specific LD levels. Hotelling’s test confirmed that GP using LD-based haplotypes from WGS data can significantly increase the accuracies of GP for RERW, compared with the individual SNP approach (∼1.4 and 1.9% for GHBLUP and GHBLUP+GBLUP, respectively). We found that the accuracies using haploblock approach varied with different LD thresholds. The LD thresholds (r2 ≥ 0.5) were optimal for most scenarios. Our results suggested that LD-based haploblock approach can improve accuracy of genomic prediction for carcass traits using both HD chip and imputed WGS data under the optimal LD thresholds in Chinese Simmental beef cattle.

scholarly journals Inter- and Intra-Generation Genomic Predictions for Douglas-fir Growth in Unobserved Environments

2019 ◽  
Author(s):  
Blaise Ratcliffe ◽  
Francis Thistlethwaite ◽  
Omnia Gamal El-Dien ◽  
Eduardo P. Cappa ◽  
Ilga Porth ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Environmental Heterogeneity ◽  
Prediction Models ◽  
Reaction Norm ◽  
Douglas Fir ◽  
Single Step ◽  
Exome Capture ◽  
Experimental Population ◽  
Training Population ◽  
The Impact

ABSTRACTConifers are prime candidates for genomic selection (GS) due to their long breeding cycles. Previous studies have shown much reduced prediction accuracies (PA) of breeding values in unobserved environments, which may impede its adoption. The impact of explicit environmental heterogeneity modeling including genotype-by-environment (G×E) interaction effects using environmental covariates (EC) in a reaction-norm genomic prediction model was tested using single-step GBLUP (ssGBLUP). A three-generation coastal Douglas-fir experimental population with 14 genetic trials (n = 13,615) permitted estimation of intra- and inter-generation PA in unobserved environments using 66,969 SNPs derived from exome capture. Intra- and inter-generation PAs ranged from 0.447-0.640 and 0.317-0.538, respectively. The inclusion of ECs in the prediction models explained up to 23% of the phenotypic variation for the fully specified model and resulted in the best model fit. Modeling G×E effects in the training population increased PA up to 6% and 13% over the base model for inter- and intra-generations, respectively. GS-PA can be substantially improved using ECs to explain environmental heterogeneity and G×E effects. The ssGBLUP methodology allows historical genetic trials containing non-genotyped samples to contribute in genomic prediction, and, thus, effectively boosting training population size which is a critical step. Further pheno- and enviro-typing developments may improve GS-PA.

Accuracies of direct genomic breeding values for birth and weaning weights of registered Charolais cattle in Mexico

2020 ◽  
Vol 60 (6) ◽  
pp. 772
Author(s):  
Francisco J. Jahuey-Martínez ◽  
Gaspar M. Parra-Bracamonte ◽  
Dorian J. Garrick ◽  
Nicolás López-Villalobos ◽  
Juan C. Martínez-González ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Reference Population ◽  
Single Step ◽  
Bayesian Regression ◽  
Nucleotide Polymorphisms ◽  
Linear Unbiased Prediction ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Charolais Cattle ◽  
Best Linear Unbiased

Context Genomic prediction is now routinely used in many livestock species to rank individuals based on genomic breeding values (GEBV). Aims This study reports the first assessment aimed to evaluate the accuracy of direct GEBV for birth (BW) and weaning (WW) weights of registered Charolais cattle in Mexico. Methods The population assessed included 823 animals genotyped with an array of 77000 single nucleotide polymorphisms. Genomic prediction used genomic best linear unbiased prediction (GBLUP), Bayes C (BC), and single-step Bayesian regression (SSBR) methods in comparison with a pedigree-based BLUP method. Key results Our results show that the genomic prediction methods provided low and similar accuracies to BLUP. The prediction accuracy of GBLUP and BC were identical at 0.31 for BW and 0.29 for WW, similar to BLUP. Prediction accuracies of SSBR for BW and WW were up to 4% higher than those by BLUP. Conclusions Genomic prediction is feasible under current conditions, and provides a slight improvement using SSBR. Implications Some limitations on reference population size and structure were identified and need to be addressed to obtain more accurate predictions in liveweight traits under the prevalent cattle breeding conditions of Mexico.

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