The Experts below are selected from a list of 53142 Experts worldwide ranked by ideXlab platform
M E Goddard - One of the best experts on this subject based on the ideXlab platform.
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hot topic definition and implementation of a Breeding Value for feed efficiency in dairy cows
Journal of Dairy Science, 2015Co-Authors: Ben J. Hayes, G J Nieuwhof, J E Pryce, Oscar Gonzalezrecio, W J Wales, M P Coffey, M E GoddardAbstract:A new Breeding Value that combines the amount of feed saved through improved metabolic efficiency with predicted maintenance requirements is described. The Breeding Value includes a genomic component for residual feed intake (RFI) combined with maintenance requirements calculated from either a genomic or pedigree estimated Breeding Value (EBV) for body weight (BW) predicted using conformation traits. Residual feed intake is only available for genotyped Holsteins; however, BW is available for all breeds. The RFI component of the "feed saved" EBV has 2 parts: Australian calf RFI and Australian lactating cow RFI. Genomic Breeding Values for RFI were estimated from a reference population of 2,036 individuals in a multi-trait analysis including Australian calf RFI (n=843), Australian lactating cow RFI (n=234), and UK and Dutch lactating cow RFI (n=958). In all cases, the RFI phenotypes were deviations from a mean of 0, calculated by correcting dry matter intake for BW, growth, and milk yield (in the case of lactating cows). Single nucleotide polymorphism effects were calculated from the output of genomic BLUP and used to predict Breeding Values of 4,106 Holstein sires that were genotyped but did not have RFI phenotypes themselves. These bulls already had BW Breeding Values calculated from type traits, from which maintenance requirements in kilograms of feed per year were inferred. Finally, RFI and the feed required for maintenance (through BW) were used to calculate a feed saved Breeding Value and expressed as the predicted amount of feed saved per year. Animals that were 1 standard deviation above the mean were predicted to eat 66 kg dry matter less per year at the same level of milk production. In a data set of genotyped Holstein sires, the mean reliability of the feed saved Breeding Value was 0.37. For Holsteins that are not genotyped and for breeds other than Holsteins, feed saved is calculated using BW only. From April 2015, feed saved has been included as part of the Australian national selection index, the Balanced Performance Index (BPI). Selection on the BPI is expected to lead to modest gains in feed efficiency.
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genomic selection prediction of accuracy and maximisation of long term response
Genetica, 2009Co-Authors: M E GoddardAbstract:Genomic selection refers to the use of dense markers covering the whole genome to estimate the Breeding Value of selection candidates for a quantitative trait. This paper considers prediction of Breeding Value based on a linear combination of the markers. In this case the best estimate of each marker’s effect is the expectation of the effect conditional on the data. To calculate this requires a prior distribution of marker effects. If the marker effects are normally distributed with constant variance, BLUP can be used to calculate the estimated effects of the markers and hence the estimated Breeding Value (EBV). In this case the model is equivalent to a conventional animal model in which the relationship matrix among the animals is estimated from the markers instead of the pedigree. The accuracy of the EBV can approach 1.0 but a very large amount of data is required. An alternative model was investigated in which only some markers have non-zero effects and these effects follow a reflected exponential distribution. In this case the expected effect of a marker is a non-linear function of the data such that apparently small effects are regressed back almost to zero and consequently these markers can be deleted from the model. The accuracy in this case is considerably higher than when marker effects are normally distributed. If genomic selection is practiced for several generations the response declines in a manner that can be predicted from the marker allele frequencies. Genomic selection is likely to lead to a more rapid decline in the selection response than phenotypic selection unless new markers are continually added to the prediction of Breeding Value. A method to find the optimum index to maximise long term selection response is derived. This index varies the weight given to a marker according to its frequency such that markers where the favourable allele has low frequency receive more weight in the index.
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artificial selection and maintenance of genetic variance in the global dairy cow population
Philosophical Transactions of the Royal Society B, 2005Co-Authors: Sue Brotherstone, M E GoddardAbstract:Genetic improvement of dairy cows, which has increased the milk yield of cows in the UK by 1200 kg per lactation in 12 years, is an excellent example of the application of quantitative genetics to agriculture. The most important traits of dairy cattle are expressed only in females, but the main opportunity for selection is in males. Despite this, genetic improvement was achieved by the invention of a new statistical methodology, called ‘best linear unbiased prediction’ to estimate the Breeding Value of bulls. Intense selection of the best bulls, combined with the worldwide use of these bulls through artificial insemination and frozen semen, has created a global population and caused concern that the genetic variation available in the future will be reduced. Maintenance of genetic variation and long-term genetic gains would be aided by rational payment systems, use of crossBreeding where profitable, inclusion of all economically important traits in the Breeding objective, recognition of genotype by environment interactions and the use of selection algorithms that balance estimated Breeding Value against the average relationship among the selected animals. Fortunately, all of these things are happening to some degree.
M P L Calus - One of the best experts on this subject based on the ideXlab platform.
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effect of predictor traits on accuracy of genomic Breeding Values for feed intake based on a limited cow reference population
Animal, 2013Co-Authors: M J Pszczola, R F Veerkamp, Y De Haas, E Wall, T Strabel, M P L CalusAbstract:The genomic Breeding Value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the Breeding Value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic Breeding Value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat-protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and Breeding Values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding Value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI Breeding Value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI Breeding Value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) Breeding Values of scarcely recorded traits such as feed intake.
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genomic Breeding Value prediction methods and procedures
Animal, 2010Co-Authors: M P L CalusAbstract:Animal Breeding faces one of the most significant changes of the past decades – the implementation of genomic selection. Genomic selection uses dense marker maps to predict the Breeding Value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, Breeding Values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated Breeding Values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations.
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the effect of missing marker genotypes on the accuracy of gene assisted Breeding Value estimation a comparison of methods
Animal, 2010Co-Authors: H.a. Mulder, T H E Meuwissen, M P L Calus, R F VeerkampAbstract:In livestock populations, missing genotypes on a large proportion of the animals is a major problem when implementing geneassisted Breeding Value estimation for genes with known effect. The objective of this study was to compare different methods to deal with missing genotypes on accuracy of gene-assisted Breeding Value estimation for identified bi-allelic genes using Monte Carlo simulation. A nested full-sib half-sib structure was simulated with a mixed inheritance model with one bi-allelic quantitative trait loci (QTL) and a polygenic effect due to infinite number of polygenes. The effect of the QTL was included in gene-assisted BLUP either by random regression on predicted gene content, i.e. the number of positive alleles, or including haplotype effects in the model with an inverse IBD matrix to account for identity-by-descent relationships between haplotypes using linkage analysis information (IBD–LA). The inverse IBD matrix was constructed using segregation indicator probabilities obtained from multiple marker iterative peeling. Gene contents for unknown genotypes were predicted using either multiple marker iterative peeling or mixed model methodology. For both methods, gene-assisted Breeding Value estimation increased accuracies of total estimated Breeding Value (EBV) with 0% to 22% for genotyped animals in comparison to conventional Breeding Value estimation. For animals that were not genotyped, the increase in accuracy was much lower (0% to 5%), but still substantial when the heritability was 0.1 and when the QTL explained at least 15% of the genetic variance. Regression on predicted gene content yielded higher accuracies than IBD–LA. Allele substitution effects were, however, overestimated, especially when only sires and males in the last generation were genotyped. For juveniles without phenotypic records and traits measured only on females, the superiority of regression on gene content over IBD–LA was larger than when all animals had phenotypes. Missing gene contents were predicted with higher accuracy using multiple-marker iterative peeling than with using mixed model methodology, but the difference in accuracy of total EBV was negligible and mixed model methodology was computationally much faster than multiple iterative peeling. For large livestock populations it can be concluded that gene-assisted Breeding Value estimation can be practically best performed by regression on gene contents, using mixed model methodology to predict missing marker genotypes, combining phenotypic information of genotyped and ungenotyped animals in one evaluation. This technique would be, in principle, also feasible for genomic selection. It is expected that genomic selection for ungenotyped animals using predicted single nucleotide polymorphism gene contents might be beneficial especially for low heritable traits.
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estimation of prediction error variances via monte carlo sampling methods using different formulations of the prediction error variance
Genetics Selection Evolution, 2009Co-Authors: John M Hickey, H.a. Mulder, R F Veerkamp, M P L Calus, R ThompsonAbstract:Calculation of the exact prediction error variance covariance matrix is often computationally too demanding, which limits its application in REML algorithms, the calculation of accuracies of estimated Breeding Values and the control of variance of response to selection. Alternatively Monte Carlo sampling can be used to calculate approximations of the prediction error variance, which converge to the true Values if enough samples are used. However, in practical situations the number of samples, which are computationally feasible, is limited. The objective of this study was to compare the convergence rate of different formulations of the prediction error variance calculated using Monte Carlo sampling. Four of these formulations were published, four were corresponding alternative versions, and two were derived as part of this study. The different formulations had different convergence rates and these were shown to depend on the number of samples and on the level of prediction error variance. Four formulations were competitive and these made use of information on either the variance of the estimated Breeding Value and on the variance of the true Breeding Value minus the estimated Breeding Value or on the covariance between the true and estimated Breeding Values.
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accuracy of Breeding Values when using and ignoring the polygenic effect in genomic Breeding Value estimation with a marker density of one snp per cm
Journal of Animal Breeding and Genetics, 2007Co-Authors: M P L Calus, R F VeerkampAbstract:Genomic selection is based on Breeding Values that are estimated using genome-wide dense marker maps. The objective of this paper was to investigate the effect of including or ignoring the polygenic effect on the accuracy of total genomic Breeding Values, when there is coverage of the genome with approximately one SNP per cM. The importance of the polygenic effect might differ for high and low heritability traits, and might depend on the design of the reference dataset. Hence, different scenarios were evaluated using stochastic simulation. Accuracies of the total Breeding Value of juvenile selection candidates depended on the number of animals included in the reference data. When excluding polygenic effects, those accuracies ranged from 0.38 to 0.55 and from 0.73 to 0.79 for traits with heritabilities of 10 and 50%, respectively. Accuracies were improved by including a polygenic effect in the model for the low heritability trait, when the LD-measure r2 between adjacent markers became smaller than approximately 0.10, while for the high heritability trait there was already a small improvement at r2 between adjacent markers of 0.14. In all situations, the estimated total genetic variance was underestimated, particularly when the polygenic effect was excluded from the model. The haplotype variance was less underestimated when more animals were added in the reference dataset.
Susanne Dreisigacker - One of the best experts on this subject based on the ideXlab platform.
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regularized selection indices for Breeding Value prediction using hyper spectral image data
Scientific Reports, 2020Co-Authors: Marco Lopezcruz, Eric L Olson, G Rovere, Jose Crossa, Susanne Dreisigacker, Suchismita Mondal, Ravi P Singh, Gustavo De Los CamposAbstract:High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in Breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYT's (International Maize and Wheat Improvement Center) wheat Breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by standard SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.
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regularized selection indices for Breeding Value prediction using hyper spectral image data
bioRxiv, 2020Co-Authors: Marco Lopezcruz, Eric L Olson, G Rovere, Jose Crossa, Susanne Dreisigacker, Suchismita Mondal, Ravi P Singh, Gustavo De Los CamposAbstract:High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in Breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYT9s (International Maize and Wheat Improvement Center) wheat Breeding program we show that image-based regularized SIs offer consistently higher accuracy for grain yield than those achieved by canonical SIs and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.
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Breeding Value of primary synthetic wheat genotypes for grain yield
PLOS ONE, 2016Co-Authors: Jafar Jafarzadeh, Jean-luc Jannink, Susanne Dreisigacker, David Bonnett, Deniz Akdemir, Mark E SorrellsAbstract:To introduce new genetic diversity into the bread wheat gene pool from its progenitor, Aegilops tauschii (Coss.) Schmalh, 33 primary synthetic hexaploid wheat genotypes (SYN) were crossed to 20 spring bread wheat (BW) cultivars at the International Wheat and Maize Improvement Center. Modified single seed descent was used to develop 97 populations with 50 individuals per population using first back-cross, biparental, and three-way crosses. Individuals from each cross were selected for short stature, early heading, flowering and maturity, minimal lodging, and free threshing. Yield trials were conducted under irrigated, drought, and heat-stress conditions from 2011 to 2014 in Ciudad Obregon, Mexico. Genomic estimated Breeding Values (GEBVs) of parents and synthetic derived lines (SDLs) were estimated using a genomic best linear unbiased prediction (GBLUP) model with markers in each trial. In each environment, there were SDLs that had higher GEBVs than their recurrent BW parent for yield. The GEBVs of BW parents for yield ranged from -0.32 in heat to 1.40 in irrigated trials. The range of the SYN parent GEBVs for yield was from -2.69 in the irrigated to 0.26 in the heat trials and were mostly negative across environments. The contribution of the SYN parents to improved grain yield of the SDLs was highest under heat stress, with an average GEBV for the top 10% of the SDLs of 0.55 while the weighted average GEBV of their corresponding recurrent BW parents was 0.26. Using the pedigree-based model, the accuracy of genomic prediction for yield was 0.42, 0.43, and 0.49 in the drought, heat and irrigated trials, respectively, while for the marker-based model these Values were 0.43, 0.44, and 0.55. The SYN parents introduced novel diversity into the wheat gene pool. Higher GEBVs of progenies were due to introgression and retention of some positive alleles from SYN parents.
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Breeding Value of Primary Synthetic Wheat Genotypes for Grain Yield
2016Co-Authors: Jafar Jafarzadeh, Jean-luc Jannink, Susanne Dreisigacker, David Bonnett, Deniz Akdemir, Mark E SorrellsAbstract:To introduce new genetic diversity into the bread wheat gene pool from its progenitor, Aegilops tauschii (Coss.) Schmalh, 33 primary synthetic hexaploid wheat genotypes (SYN) were crossed to 20 spring bread wheat (BW) cultivars at the International Wheat and Maize Improvement Center. Modified single seed descent was used to develop 97 populations with 50 individuals per population using first back-cross, biparental, and three-way crosses. Individuals from each cross were selected for short stature, early heading, flowering and maturity, minimal lodging, and free threshing. Yield trials were conducted under irrigated, drought, and heat-stress conditions from 2011 to 2014 in Ciudad Obregon, Mexico. Genomic estimated Breeding Values (GEBVs) of parents and synthetic derived lines (SDLs) were estimated using a genomic best linear unbiased prediction (GBLUP) model with markers in each trial. In each environment, there were SDLs that had higher GEBVs than their recurrent BW parent for yield. The GEBVs of BW parents for yield ranged from -0.32 in heat to 1.40 in irrigated trials. The range of the SYN parent GEBVs for yield was from -2.69 in the irrigated to 0.26 in the heat trials and were mostly negative across environments. The contribution of the SYN parents to improved grain yield of the SDLs was highest under heat stress, with an average GEBV for the top 10% of the SDLs of 0.55 while the weighted average GEBV of their corresponding recurrent BW parents was 0.26. Using the pedigree-based model, the accuracy of genomic prediction for yield was 0.42, 0.43, and 0.49 in the drought, heat and irrigated trials, respectively, while for the marker-based model these Values were 0.43, 0.44, and 0.55. The SYN parents introduced novel diversity into the wheat gene pool. Higher GEBVs of progenies were due to introgression and retention of some positive alleles from SYN parents.
R F Veerkamp - One of the best experts on this subject based on the ideXlab platform.
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effect of predictor traits on accuracy of genomic Breeding Values for feed intake based on a limited cow reference population
Animal, 2013Co-Authors: M J Pszczola, R F Veerkamp, Y De Haas, E Wall, T Strabel, M P L CalusAbstract:The genomic Breeding Value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the Breeding Value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic Breeding Value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat-protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and Breeding Values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding Value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI Breeding Value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI Breeding Value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) Breeding Values of scarcely recorded traits such as feed intake.
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the effect of missing marker genotypes on the accuracy of gene assisted Breeding Value estimation a comparison of methods
Animal, 2010Co-Authors: H.a. Mulder, T H E Meuwissen, M P L Calus, R F VeerkampAbstract:In livestock populations, missing genotypes on a large proportion of the animals is a major problem when implementing geneassisted Breeding Value estimation for genes with known effect. The objective of this study was to compare different methods to deal with missing genotypes on accuracy of gene-assisted Breeding Value estimation for identified bi-allelic genes using Monte Carlo simulation. A nested full-sib half-sib structure was simulated with a mixed inheritance model with one bi-allelic quantitative trait loci (QTL) and a polygenic effect due to infinite number of polygenes. The effect of the QTL was included in gene-assisted BLUP either by random regression on predicted gene content, i.e. the number of positive alleles, or including haplotype effects in the model with an inverse IBD matrix to account for identity-by-descent relationships between haplotypes using linkage analysis information (IBD–LA). The inverse IBD matrix was constructed using segregation indicator probabilities obtained from multiple marker iterative peeling. Gene contents for unknown genotypes were predicted using either multiple marker iterative peeling or mixed model methodology. For both methods, gene-assisted Breeding Value estimation increased accuracies of total estimated Breeding Value (EBV) with 0% to 22% for genotyped animals in comparison to conventional Breeding Value estimation. For animals that were not genotyped, the increase in accuracy was much lower (0% to 5%), but still substantial when the heritability was 0.1 and when the QTL explained at least 15% of the genetic variance. Regression on predicted gene content yielded higher accuracies than IBD–LA. Allele substitution effects were, however, overestimated, especially when only sires and males in the last generation were genotyped. For juveniles without phenotypic records and traits measured only on females, the superiority of regression on gene content over IBD–LA was larger than when all animals had phenotypes. Missing gene contents were predicted with higher accuracy using multiple-marker iterative peeling than with using mixed model methodology, but the difference in accuracy of total EBV was negligible and mixed model methodology was computationally much faster than multiple iterative peeling. For large livestock populations it can be concluded that gene-assisted Breeding Value estimation can be practically best performed by regression on gene contents, using mixed model methodology to predict missing marker genotypes, combining phenotypic information of genotyped and ungenotyped animals in one evaluation. This technique would be, in principle, also feasible for genomic selection. It is expected that genomic selection for ungenotyped animals using predicted single nucleotide polymorphism gene contents might be beneficial especially for low heritable traits.
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influence of management and genetic merit for milk yield on the oxidative status of plasma in heifers
Livestock Science, 2009Co-Authors: B Beerda, R F Veerkamp, N Wullepit, Katleen Raes, Dirk Fremaut, S De SmetAbstract:Abstract This study was part of a larger study that addressed whether milk production levels affect health risks in dairy cows as influenced by genetic merit for milk yield and management factors. Plasma samples were collected from 80 Holstein Friesian heifers at 2 weeks pre-partum and at 4 and 8 weeks post-partum in a 2 × 2 × 2 factorial arrangement design with the factors Breeding Value for milk production (high or low), milking frequency (2 or 3 times a day) and feed energy density (high or low). The following parameters indicative of the oxidative status were measured in plasma: ferric reducing ability of plasma (FRAP), α-tocopherol level, glutathione peroxidase activity (GSH-Px), superoxide dismutase activity (SOD), thiobarbituric acid reactive substances (TBARS) concentration and paraoxonase activity (PON). There was a significant effect of time for FRAP, α-tocopherol, GSH-Px and SOD, indicating changes in the plasma oxidative status around parturition. A high merit for milk yield, three-times-daily milking and especially feeding the high energy feed all stimulated milk production level. However, the influence on oxidative status parameters was minimal. Only the FRAP Value was significantly lower in the high compared to the low energy group. Cows seem to undergo some oxidative stress around parturition, which can make them more susceptible to disorders. However, there were no clear indications that Breeding Value, milk frequency or feed energy level jeopardizes heifers in a way oxidative stress became critical.
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estimation of prediction error variances via monte carlo sampling methods using different formulations of the prediction error variance
Genetics Selection Evolution, 2009Co-Authors: John M Hickey, H.a. Mulder, R F Veerkamp, M P L Calus, R ThompsonAbstract:Calculation of the exact prediction error variance covariance matrix is often computationally too demanding, which limits its application in REML algorithms, the calculation of accuracies of estimated Breeding Values and the control of variance of response to selection. Alternatively Monte Carlo sampling can be used to calculate approximations of the prediction error variance, which converge to the true Values if enough samples are used. However, in practical situations the number of samples, which are computationally feasible, is limited. The objective of this study was to compare the convergence rate of different formulations of the prediction error variance calculated using Monte Carlo sampling. Four of these formulations were published, four were corresponding alternative versions, and two were derived as part of this study. The different formulations had different convergence rates and these were shown to depend on the number of samples and on the level of prediction error variance. Four formulations were competitive and these made use of information on either the variance of the estimated Breeding Value and on the variance of the true Breeding Value minus the estimated Breeding Value or on the covariance between the true and estimated Breeding Values.
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accuracy of Breeding Values when using and ignoring the polygenic effect in genomic Breeding Value estimation with a marker density of one snp per cm
Journal of Animal Breeding and Genetics, 2007Co-Authors: M P L Calus, R F VeerkampAbstract:Genomic selection is based on Breeding Values that are estimated using genome-wide dense marker maps. The objective of this paper was to investigate the effect of including or ignoring the polygenic effect on the accuracy of total genomic Breeding Values, when there is coverage of the genome with approximately one SNP per cM. The importance of the polygenic effect might differ for high and low heritability traits, and might depend on the design of the reference dataset. Hence, different scenarios were evaluated using stochastic simulation. Accuracies of the total Breeding Value of juvenile selection candidates depended on the number of animals included in the reference data. When excluding polygenic effects, those accuracies ranged from 0.38 to 0.55 and from 0.73 to 0.79 for traits with heritabilities of 10 and 50%, respectively. Accuracies were improved by including a polygenic effect in the model for the low heritability trait, when the LD-measure r2 between adjacent markers became smaller than approximately 0.10, while for the high heritability trait there was already a small improvement at r2 between adjacent markers of 0.14. In all situations, the estimated total genetic variance was underestimated, particularly when the polygenic effect was excluded from the model. The haplotype variance was less underestimated when more animals were added in the reference dataset.
Jack C M Dekkers - One of the best experts on this subject based on the ideXlab platform.
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genomic Breeding Value prediction and qtl mapping of qtlmas2011 data using bayesian and gblup methods
BMC Proceedings, 2012Co-Authors: Jian Zeng, D J Garrick, Anna Wolc, Rohan L Fernando, M J Pszczola, T Strabel, Jack C M DekkersAbstract:Background The goal of this study was to apply Bayesian and GBLUP methods to predict genomic Breeding Values (GEBV), map QTL positions and explore the genetic architecture of the trait simulated for the 15th QTL-MAS workshop.
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genomic Breeding Value prediction and qtl mapping of qtlmas2010 data using bayesian methods
BMC Proceedings, 2011Co-Authors: Xiaochen Sun, D J Garrick, David Habier, Rohan L Fernando, Jack C M DekkersAbstract:Bayesian methods allow prediction of genomic Breeding Values (GEBVs) using high-density single nucleotide polymorphisms (SNPs) covering the whole genome with effective shrinkage of SNP effects using appropriate priors. In this study we applied a modification of the well-known BayesA and BayesB methods to estimate the proportion of SNPs with zero effects (π) and a common variance for non-zero effects. The method, termed BayesCπ, was used to predict the GEBVs of the last generation of the QTLMAS2010 data. The accuracy of GEBVs from various methods was estimated by the correlation with phenotypes in the last generation. The methods were BayesCPi and BayesB with different π Values, both with and without polygenic effects, and best linear unbiased prediction using an animal model with a genomic or numerator relationship matrix. Positions of quantitative trait loci (QTLs) were identified based on the variances of GEBVs for windows of 10 consecutive SNPs. We also proposed a novel approach to set significance thresholds for claiming QTL in this specific case by using pedigree-based simulation of genotypes. All analyses were focused on detecting and evaluating QTL with additive effects. The accuracy of GEBVs was highest for BayesCπ, but the accuracy of BayesB with π equal to 0.99 was similar to that of BayesCπ. The accuracy of BayesB dropped with a decrease in π. Including polygenic effects into the model only had marginal effects on accuracy and bias of predictions. The number of QTL identified was 15 when based on a stringent 10% chromosome-wise threshold and increased to 21 when a 20% chromosome-wise threshold was used. The BayesCπ method without polygenic effects was identified to be the best method for the QTLMAS2010 dataset, because it had highest accuracy and least bias. The significance criterion based on variance of 10-SNP windows allowed detection of more than half of the QTL, with few false positives.