scholarly journals 330 A hybrid model for genomic selection using prioritized SNPs based on FST scores in the presence of non-genotyped animals

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 51-51
Author(s):  
Sajjad Toghiani ◽  
Ling-Yun Chang ◽  
El H Hay ◽  
Andrew J Roberts ◽  
Samuel E Aggrey ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Simulated Data ◽  
Snp Markers ◽  
Genomic Relationship Matrix ◽  
Polygenic Effect ◽  
Relationship Matrix ◽  
Continuous Increase ◽  
Missing Genotypes

Abstract The dramatic advancement in genotyping technology has greatly reduced the complexity and cost of genotyping. The continuous increase in the density of marker panels is resulting in little to no improvement in the accuracy of genomic selection. Direct inversion of the genomic relationship matrix is infeasible for some livestock populations due to the excessive computational cost. In addition, most animals in genetic evaluation programs are non-genotyped. Including these animals in a genomic evaluation requires the imputation of the missing genotypes when using regression methods. To overcome these challenges, a hybrid approach is proposed. This approach fits a subset of SNP markers selected based on FST scores and a classical polygenic effect. The method was first tested using only genotyped animals and then extended to accommodate non-genotyped animals. The proposed approach was evaluated using simulated data for a trait with heritability of 0.1 and 0.4 and weaning weight in a crossbred beef cattle population. When all animals were genotyped, the hybrid approach using only 2.5% of prioritized SNPs exceeded the prediction accuracies of BayesB, BayesC, and GBLUP by more than 7%. When non-genotyped animals were incorporated, the proposed approach significantly outperformed ss-GBLUP method in terms of prediction accuracy under both simulated heritability scenarios. Although the results seem to depend on the genetic complexity of the trait, the proposed approach resulted in higher prediction accuracies than current methods. Furthermore, its computational costs in terms of CPU time and peak memory are substantially lower than the current methods.

PSI-B-21 Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 228-229
Author(s):  
Bruna Santana ◽  
Molly Riser ◽  
Breno O Fragomeni
Keyword(s):  
Simulated Data ◽  
Snp Markers ◽  
Single Step ◽  
Breeding Value ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
True Breeding ◽  
Non Linear ◽  
Causal Variants ◽  
True Breeding Value

Abstract This study aimed to evaluate the accuracy of genomic prediction with simulated data, using SNP markers, causal quantitative trait nucleotide (QTN), and the combination of both. The methods used were the best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP), with alternative SNP weights. Data were simulated using the package AlphasimR. Trait heritability of 0.3 was assumed, and genetic variance was fully accounted for by 100 or 1000 QTNs. A population with an effective size of 200 was selected, and 20 generations were simulated. The genomic information mimicked the 29 bovine chromosomes and included 50k SNP markers evenly distributed across the genome. Approximately 16800 genotypes were available from selected sires and dams in generations 16–19, and 2000 animals in generation 20. Phenotypes for young animals were not included in the analysis, as they were used in the validation. For GBLUP, three pseudo-phenotypes were considered: the raw phenotype, the true breeding value, and the true breeding value with noise added. The genomic relationship matrix was weighted using quadratic weights, calculated based on the SNP variance, and non-linear A, following different equation parameters. The scenario with exclusively causal variants presented accuracies close to 1 for 100 QTL, and slightly lower in the 1000 QTL. For the SNP + QTN scenario, quadratic weights promoted higher accuracy gains than the SNPs alone, especially in the 100 QTN trait. Accuracies converged at higher values for both quadratic and non-linear A weights in the 100 QTN scenario. For the 1000 QTN trait, quadratic weights diverged and reduced accuracy, while non-linear A maintained accuracy at their peaks, depending on the equation parameters. Parameters of non-linear A for highest accuracy were different in each scenario and type of analysis. Proportionally, gains in accuracy were more prominent with GBLUP than with ssGBLUP.

scholarly journals PSVIII-27 A weighted genomic relationship matrix based on FST prioritized SNPs for genomic selection

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 262-262
Author(s):  
Ling-Yun Chang ◽  
Sajjad Toghiani ◽  
E L Hamidi Hay ◽  
Samuel E Aggrey ◽  
Romdhane Rekaya
Keyword(s):  
Genomic Selection ◽  
Mixed Model ◽  
Relative Weight ◽  
Snp Markers ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Improve Accuracy ◽  
Increase In Accuracy ◽  
Generation Sequencing

Abstract Using low to moderate density SNP marker panels, a substantial increase in accuracy was achieved. The dramatic increase in the number of identified variants due to advances in next generation sequencing was expected to significantly increase the accuracy of genomic selection (GS). Unfortunately, little to no improvement was observed. For mixed model-based approaches, using all SNPs in the panel to compute the observed relationship matrix (G) will not increase accuracy as the additive relationships between individuals can be accurately estimated using a much smaller number of markers. Due to these limitations, variant prioritization has become a necessity to improve accuracy. Further, it has been shown that weighting SNPs when calculating G could be effective in improving the accuracy of GS. FST as a measure population differential has been successfully used to identify genome segments under selection pressure. Consequently, FST could be used to both prioritize SNPs and to derive their relative weight in the calculation of the genomic relationship matrix. A population of 15,000 animals genotyped for 400K SNP markers uniformly-distributed along 10 chromosomes was simulated. A trait with heritability 0.3 genetically controlled by two hundred QTL was generated. The top 20K SNPs based on their FST scores were used either alone or with the remaining 380K SNPs to compute G with or without weighting. When only the top 20K SNPs were used to compute G, two scenarios were considered: 1) equal weights for all SNPs or 2) weights proportional to the SNP FST scores. When all 400K SNP markers were used, different weighting scenarios were evaluated. The results clearly showed that prioritizing SNP markers based on their FST score and using the latter to compute relative weights has increased the genetic similarity between training and validations animals and resulted in more than 5% improvement in the accuracy of GS.

scholarly journals On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Theo Meuwissen ◽  
Irene van den Berg ◽  
Mike Goddard
Keyword(s):  
Variable Selection ◽  
Genome Sequence ◽  
Genomic Prediction ◽  
Milk Fat ◽  
Genotype Imputation ◽  
Whole Genome Sequence ◽  
Genomic Relationship Matrix ◽  
Polygenic Effect ◽  
Relationship Matrix ◽  
Whole Genome

Abstract Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

scholarly journals The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

2011 ◽  
Vol 93 (3) ◽  
pp. 203-219 ◽  
Author(s):  
KATHRYN E. KEMPER ◽  
DAVID L. EMERY ◽  
STEPHEN C. BISHOP ◽  
HUTTON ODDY ◽  
BENJAMIN J. HAYES ◽  
...  
Keyword(s):  
Complex Traits ◽  
Sampling Error ◽  
Snp Markers ◽  
Snp Marker ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Genetic Merit ◽  
Independent Population

SummaryGenetic resistance to gastrointestinal worms is a complex trait of great importance in both livestock and humans. In order to gain insights into the genetic architecture of this trait, a mixed breed population of sheep was artificially infected with Trichostrongylus colubriformis (n=3326) and then Haemonchus contortus (n=2669) to measure faecal worm egg count (WEC). The population was genotyped with the Illumina OvineSNP50 BeadChip and 48 640 single nucleotide polymorphism (SNP) markers passed the quality controls. An independent population of 316 sires of mixed breeds with accurate estimated breeding values for WEC were genotyped for the same SNP to assess the results obtained from the first population. We used principal components from the genomic relationship matrix among genotyped individuals to account for population stratification, and a novel approach to directly account for the sampling error associated with each SNP marker regression. The largest marker effects were estimated to explain an average of 0·48% (T. colubriformis) or 0·08% (H. contortus) of the phenotypic variance in WEC. These effects are small but consistent with results from other complex traits. We also demonstrated that methods which use all markers simultaneously can successfully predict genetic merit for resistance to worms, despite the small effects of individual markers. Correlations of genomic predictions with breeding values of the industry sires reached a maximum of 0·32. We estimate that effective across-breed predictions of genetic merit with multi-breed populations will require an average marker spacing of approximately 10 kbp.

scholarly journals A Weighted Genomic Relationship Matrix Based on Fixation Index (FST) Prioritized SNPs for Genomic Selection

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 922
Author(s):  
Ling-Yun Chang ◽  
Sajjad Toghiani ◽  
El Hamidi Hay ◽  
Samuel E. Aggrey ◽  
Romdhane Rekaya
Keyword(s):  
Genomic Selection ◽  
Statistical Power ◽  
Fixation Index ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Nucleotide Polymorphisms ◽  
Genomic Relationship ◽  
Single Nucleotide ◽  
Relative Contribution ◽  
Estimation Of Variance

A dramatic increase in the density of marker panels has been expected to increase the accuracy of genomic selection (GS), unfortunately, little to no improvement has been observed. By including all variants in the association model, the dimensionality of the problem should be dramatically increased, and it could undoubtedly reduce the statistical power. Using all Single nucleotide polymorphisms (SNPs) to compute the genomic relationship matrix (G) does not necessarily increase accuracy as the additive relationships can be accurately estimated using a much smaller number of markers. Due to these limitations, variant prioritization has become a necessity to improve accuracy. The fixation index (FST) as a measure of population differentiation has been used to identify genome segments and variants under selection pressure. Using prioritized variants has increased the accuracy of GS. Additionally, FST can be used to weight the relative contribution of prioritized SNPs in computing G. In this study, relative weights based on FST scores were developed and incorporated into the calculation of G and their impact on the estimation of variance components and accuracy was assessed. The results showed that prioritizing SNPs based on their FST scores resulted in an increase in the genetic similarity between training and validation animals and improved the accuracy of GS by more than 5%.

scholarly journals 335 Genomic predictions with a multi-breed genomic relationship matrix

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 49-50
Author(s):  
Yvette Steyn ◽  
Daniela Lourenco ◽  
Ignacy Misztal
Keyword(s):  
Prediction Accuracy ◽  
Negative Impact ◽  
Reference Population ◽  
Single Step ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Effective Population ◽  
Specific Allele ◽  
Missing Genotypes

Abstract Multi-breed evaluations have the advantage of increasing the size of the reference population for genomic evaluations and are quite simple; however, combining breeds usually have a negative impact on prediction accuracy. The aim of this study was to evaluate the use of a multi-breed genomic relationship matrix (G), where SNP for each breed are non-shared. The multi-breed G is set assuming known genotypes for one breed and missing genotypes for the remaining breeds. This setup may avoid spurious IBS relationships between breeds and considers breed-specific allele frequencies. This scenario was contrasted to multi-breed evaluations where all SNP are shared, i.e., the same SNP, and to single-breed evaluations. Different SNP densities, namely 9k and 45k, and different effective population sizes (Ne) were tested. Five breeds mimicking recent beef cattle populations that diverged from the same historical population were simulated using different selection criteria. It was assumed that QTL effects were the same over all breeds. For the recent population, generations 1 to 9 had approximately half of the animals genotyped, whereas all 1200 animals were genotyped in generation 10. Genotyped animals in generation 10 were set as validation; therefore, each breed had a validation set. Analysis were performed using single-step GBLUP (ssGBLUP). Prediction accuracy was calculated as correlation between true (T) and genomic estimated (GE) BV. Accuracies of GEBV were lower for the larger Ne and low SNP density. All three scenarios using 45K resulted in similar accuracies, suggesting that the marker density is high enough to account for relationships and linkage disequilibrium with QTL. A shared multi-breed evaluation using 9K resulted in a decrease of accuracy of 0.08 for a smaller Ne and 0.11 for a larger Ne. This loss was mostly avoided when markers were treated as non-shared within the same genomic relationship matrix.

scholarly journals 295 Parameter estimation under genomic selection

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 31-32
Author(s):  
Ignacy Misztal
Keyword(s):  
Parameter Estimation ◽  
Genomic Selection ◽  
Sparse Matrix ◽  
Parameters Estimation ◽  
Random Regression ◽  
Genomic Relationship Matrix ◽  
Correlated Response ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Over Time

Abstract Genetic parameters are important in animal breeding for many tasks, including as input to a model for genetic evaluation, to estimate genetic gain due to selection, and to estimate correlated response due to selection on major traits. Before the genomic era, parameter estimation was facilitated by sparse structure of mixed model equations. Methods such as AI REML with sparse matrix inversion or MCMC via Gibbs sampling could estimate parameters for populations exceeding 1 million animals. With genomic selection (GS) and single-step GBLUP, the genomic matrices are mostly dense, and costs of parameter estimation increased dramatically. The estimation with 20K genotyped animals can take many days. Details in matching pedigree and genomic information influence estimated parameters. Estimation without the genomic information when GS is practiced leads to biases due to genomic-preselection. Truncating data to too few generations or to only genotyped animals leads to additional biases by excluding data on which the selection was practiced. Current studies indicate strong declines in heritability due to GS. Regular models for parameter estimation compute parameters only for the base population. Models that trace changes of parameters over time, such as random regression model on year of birth or a multiple trait model treating times slices as separate traits, are very expensive. A good compromise in parameter estimation under GS is to use slices of only 2–3 generations, with genotypes of young animals removed. When complete populations are genotyped, estimations with large number of genotyped animals are possible either with a SNP model or with GBLUP (inversion of genomic relationship matrix by APY algorithm). For simple models, Method R can provide estimates for any data size. An indirect indication of changing parameters over time is reduced predictivity or lower genetic trend despite increased data. Parameter estimation in GS would benefit from new, efficient tools.

scholarly journals Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: a simulation study

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Ivan Pocrnic ◽  
Daniela A. L. Lourenco ◽  
Yutaka Masuda ◽  
Ignacy Misztal
Keyword(s):  
Genomic Selection ◽  
Large Fraction ◽  
Genomic Variation ◽  
Eigenvalue Decomposition ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Information ◽  
Phenotypic Information ◽  
Largest Eigenvalues

Abstract Background The dimensionality of genomic information is limited by the number of independent chromosome segments (Me), which is a function of the effective population size. This dimensionality can be determined approximately by singular value decomposition of the gene content matrix, by eigenvalue decomposition of the genomic relationship matrix (GRM), or by the number of core animals in the algorithm for proven and young (APY) that maximizes the accuracy of genomic prediction. In the latter, core animals act as proxies to linear combinations of Me. Field studies indicate that a moderate accuracy of genomic selection is achieved with a small dataset, but that further improvement of the accuracy requires much more data. When only one quarter of the optimal number of core animals are used in the APY algorithm, the accuracy of genomic selection is only slightly below the optimal value. This suggests that genomic selection works on clusters of Me. Results The simulation included datasets with different population sizes and amounts of phenotypic information. Computations were done by genomic best linear unbiased prediction (GBLUP) with selected eigenvalues and corresponding eigenvectors of the GRM set to zero. About four eigenvalues in the GRM explained 10% of the genomic variation, and less than 2% of the total eigenvalues explained 50% of the genomic variation. With limited phenotypic information, the accuracy of GBLUP was close to the peak where most of the smallest eigenvalues were set to zero. With a large amount of phenotypic information, accuracy increased as smaller eigenvalues were added. Conclusions A small amount of phenotypic data is sufficient to estimate only the effects of the largest eigenvalues and the associated eigenvectors that contain a large fraction of the genomic information, and a very large amount of data is required to estimate the remaining eigenvalues that account for a limited amount of genomic information. Core animals in the APY algorithm act as proxies of almost the same number of eigenvalues. By using an eigenvalues-based approach, it was possible to explain why the moderate accuracy of genomic selection based on small datasets only increases slowly as more data are added.

scholarly journals Simulation studies to optimize genomic selection in honey bees

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Richard Bernstein ◽  
Manuel Du ◽  
Andreas Hoppe ◽  
Kaspar Bienefeld
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Honey Bees ◽  
Reference Population ◽  
Breeding Program ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Breeding ◽  
Breeding Values

Abstract Background With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Methods Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Results Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. Conclusions This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

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