scholarly journals Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Heredity ◽  
2020 ◽  
Vol 126 (1) ◽  
pp. 206-217
Author(s):  
Xiang Ma ◽  
Ole F. Christensen ◽  
Hongding Gao ◽  
Ruihua Huang ◽  
Bjarne Nielsen ◽  
...  
Keyword(s):  
Egg Production ◽  
Quantitative Traits ◽  
Group Structure ◽  
Genetic Evaluation ◽  
Genomic Information ◽  
Bivariate Model ◽  
Breeding Values ◽  
Lower Accuracy ◽  
Small Group Size ◽  
Estimated Breeding Values

AbstractRecords on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

Practical aspects of a genetic evaluation system using parentage assigned from genetic markers

2005 ◽  
Vol 45 (8) ◽  
pp. 935 ◽  
Author(s):  
K. G. Dodds ◽  
J. A. Sise ◽  
M. L. Tate
Keyword(s):  
Genetic Markers ◽  
Evaluation System ◽  
Fixed Effects ◽  
Genetic Evaluation ◽  
Diagnostic Systems ◽  
Breeding Values ◽  
Mating Group ◽  
Animal Management ◽  
The Mean ◽  
Estimated Breeding Values

Animal breeding values can be calculated when genetic markers have been used to help determine the parentage of some of the animals, but their parentage has been incompletely determined. The pedigree sampling method is 1 computing strategy for calculating these breeding values. This paper describes and discusses methods for dealing with a number of practical issues that arise when implementing such a system for industry use. In particular, diagnostic systems for detecting inadequacies or possible errors in the genotyping systems and the recording of animal management are developed. Also, characteristics of the best assigned pedigrees are calculated according to mating group and used to check for sires missing from these groups. The correlation between breeding values estimated from a single sampled pedigree (using parentage probabilities) and those estimated as the mean from many sampled pedigrees gives a diagnostic to indicate which estimated breeding values are more influenced by uncertainties in relationships. For the analysis of survival traits, a method to enumerate and assign likely parentage to dead offspring which have not been DNA sampled and genotyped is described. When embryo transfer technology is used, the genetic dam needs to be included as a possible dam when considering parentage. If some fixed effects which depend on the parent are missing, these can be sampled similarly to parentage, and this may improve the evaluation if certain assumptions are met. A method to provide a likely list of parents, the ‘fitted pedigree’, which is based on the most likely parents, but modified to reduce the occurrence of unlikely family sets (e.g. very large litters) is also presented. The use of these methods will enhance the practical application of DNA parenting when used in conjunction with genetic evaluation.

scholarly journals Accuracies of univariate and multivariate genomic prediction models in African Cassava

2017 ◽  
Author(s):  
Uche Godfrey Okeke ◽  
Deniz Akdemir ◽  
Ismail Rabbi ◽  
Peter Kulakow ◽  
Jean-Luc Jannink
Keyword(s):  
Genomic Selection ◽  
Mixed Model ◽  
Genetic Evaluation ◽  
Environment Interaction ◽  
Genotype By Environment Interactions ◽  
Breeding Values ◽  
Genotype By Environment ◽  
Step Model ◽  
One Step ◽  
Estimated Breeding Values

List of abbreviationsGSGenomic SelectionBLUPBest Linear Unbiased PredictionEBVsEstimated Breeding ValuesEGVsEstimated genetic ValuesGEBVsGenomic Estimated Breeding ValuesSNPsSingle Nucleotide polymorphismsGxEGenotype-by-environment interactionsGxEGenotype-by-environment interactionsGxGGene-by-gene interactionsGxGxEGene-by-gene-by-environment interactionsuTUnivariate single environment one-step modeluEUnivariate multi environment one-step modelMTMulti-trait single environment one-step modelMEMultivariate single trait multi environment modelAbstractBackgroundGenomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for long cycle crops like cassava. To practically implement GS in cassava breeding, it is useful to evaluate different GS models and to develop suitable models for an optimized breeding pipeline.MethodsWe compared prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for single environment genetic evaluation (Scenario 1) while for multi-environment evaluation accounting for genotype-by-environment interaction (Scenario 2) we compared accuracies from a univariate (uE) and a multivariate (ME) multi-environment mixed model. We used sixteen years of data for six target cassava traits for these analyses. All models for Scenario 1 and Scenario 2 were based on the one-step approach. A 5-fold cross validation scheme with 10-repeat cycles were used to assess model prediction accuracies.ResultsIn Scenario 1, the MT models had higher prediction accuracies than the uT models for most traits and locations analyzed amounting to 32 percent better prediction accuracy on average. However for Scenario 2, we observed that the ME model had on average (across all locations and traits) 12 percent better predictive power than the uE model.ConclusionWe recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

scholarly journals Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population

2010 ◽  
Vol 50 (12) ◽  
pp. 1004 ◽  
Author(s):  
H. D. Daetwyler ◽  
J. M. Hickey ◽  
J. M. Henshall ◽  
S. Dominik ◽  
B. Gredler ◽  
...  
Keyword(s):  
Reference Population ◽  
Eye Muscle ◽  
Single Nucleotide ◽  
Sheep Population ◽  
Sheep Breeding ◽  
Breeding Values ◽  
Lower Accuracy ◽  
Australian Sheep ◽  
Estimated Breeding Values ◽  
Breed Sheep

Estimated breeding values for the selection of more profitable sheep for the sheep meat and wool industries are currently based on pedigree and phenotypic records. With the advent of a medium-density DNA marker array, which genotypes ~50 000 ovine single nucleotide polymorphisms, a third source of information has become available. The aim of this paper was to determine whether this genomic information can be used to predict estimated breeding values for wool and meat traits. The effects of all single nucleotide polymorphism markers in a multi-breed sheep reference population of 7180 individuals with phenotypic records were estimated to derive prediction equations for genomic estimated breeding values (GEBV) for greasy fleece weight, fibre diameter, staple strength, breech wrinkle score, weight at ultrasound scanning, scanned eye muscle depth and scanned fat depth. Five hundred and forty industry sires with very accurate Australian sheep breeding values were used as a validation population and the accuracies of GEBV were assessed according to correlations between GEBV and Australian sheep breeding values . The accuracies of GEBV ranged from 0.15 to 0.79 for wool traits in Merino sheep and from –0.07 to 0.57 for meat traits in all breeds studied. Merino industry sires tended to have more accurate GEBV than terminal and maternal breeds because the reference population consisted mainly of Merino haplotypes. The lower accuracy for terminal and maternal breeds suggests that the density of genetic markers used was not high enough for accurate across-breed prediction of marker effects. Our results indicate that an increase in the size of the reference population will increase the accuracy of GEBV.

scholarly journals 63 A comparison of clustering methods for cross-validation of genomic predictors when training on phenotypes or deregressed Estimated Breeding Values

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 34-35
Author(s):  
Johnna Baller ◽  
Jeremy T Howard ◽  
Stephen Kachman ◽  
Matthew L Spangler
Keyword(s):  
Cross Validation ◽  
Relationship Matrix ◽  
Clustering Methods ◽  
Bivariate Model ◽  
Breeding Values ◽  
Numerator Relationship Matrix ◽  
Red Angus ◽  
Estimated Breeding Values ◽  
The Impact ◽  
Selection Of

Abstract The objective of the study was to evaluate the impact of clustering methods for cross-validation on the accuracy of prediction of molecular breeding values (MBV) in Red Angus cattle (n = 9,763) and in simulation. Individuals were clustered using seven methods [k-means, k-medoids, principal component analysis on the numerator relationship matrix (A) and identical-by-state genomic matrix (G) as data and covariance matrices, and random] and two response variables [deregressed Estimated Breeding Values (DEBV) and adjusted phenotypes]. Genotypes were imputed to a 50K reference panel. Using cross-validation and a Bayes C model, MBV were estimated for traits including birth weight (BWT), marbling (MARB), rib-eye area (REA), and yearling weight (YWT) for DEBV and BWT, YWT, and ultrasonically measured intramuscular fat percentage and rib eye area for adjusted phenotypes. A bivariate animal model was used to estimate prediction accuracies calculated using the genetic correlation between estimated MBV and the associated response variable. To quantify the difference between true and estimated accuracies, a simulation mimicking a cattle population was replicated five times. The same clustering methods were used as with the Red Angus data with the addition of forward validation and two genotyping methods (random selection and selection of the top 25% of animals). Predicted accuracies were estimated similarly and true accuracies were estimated using the residual correlation of a bivariate model using MBV and true breeding values (TBV). The Rand index was used to quantify the similarity between clustering methods, showing relationship-based clusters were clearly different from random clusters. In simulation, random genotyping led to higher estimated accuracies than selection of top individuals; however, estimated accuracies over predicted true accuracies with random genotyping but under predicted true accuracies with the selection of top individuals. When forward validation was evaluated within simulation, results suggested DEBV led to less biased estimates of MBV accuracy.

Genetic evaluation including intermediate omics features

Genetics ◽  
2021 ◽  
Author(s):  
Ole F Christensen ◽  
Vinzent Börner ◽  
Luis Varona ◽  
Andres Legarra
Keyword(s):  
Evaluation System ◽  
Mixed Model ◽  
Simulated Data ◽  
Genetic Evaluation ◽  
Omics Data ◽  
Estimation Of Parameters ◽  
Data Set ◽  
Breeding Values ◽  
Mixed Model Equation ◽  
Estimated Breeding Values

Abstract In animal and plant breeding and genetics there has been an increasing interest in intermediate omics traits, such as metabolomics and transcriptomics, that mediate the effect of genetics on the phenotype of interest. For inclusion of such intermediate traits into a genetic evaluation system, there is a need for a statistical model that integrates phenotypes, genotypes, pedigree and omics traits, and a need for associated computational methods that provide estimated breeding values. In this paper, a joint model for phenotypes and omics data is presented, and a formula for the breeding values on individuals is derived. For complete omics data, three equivalent methods for best linear unbiased prediction of breeding values are presented. In all three cases, this requires solving two mixed model equation systems. Estimation of parameters using restricted maximum likelihood is also presented. For incomplete omics data, extensions of two of these methods are presented, where in both cases the extension consists of extending an omics related similarity matrix to incorporate individuals without omics data. The methods are illustrated using a simulated data set.

scholarly journals Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken

2015 ◽  
Vol 47 (1) ◽  
Author(s):  
Daniela A. L. Lourenco ◽  
Breno O. Fragomeni ◽  
Shogo Tsuruta ◽  
Ignacio Aguilar ◽  
Birgit Zumbach ◽  
...  
Keyword(s):  
Broiler Chicken ◽  
Genomic Information ◽  
Breeding Values ◽  
Estimated Breeding Values

scholarly journals Effektivität der Zuchtwertschätzung mit transformierten Daten, überprüft mit Mutter-Töchter-Paaren eines Wachtelexperimentes

2000 ◽  
Vol 43 (3) ◽  
pp. 299-310
Author(s):  
N. Mielenz ◽  
E. Dittmar ◽  
L. Schüler
Keyword(s):  
Egg Production ◽  
Original Data ◽  
Genetic Evaluation ◽  
Biological Data ◽  
Production Traits ◽  
Multiple Trait ◽  
Breeding Values ◽  
Normal Distributions ◽  
Laying Performance ◽  
Estimation Of Variance

Abstract. Title of the paper: Effectiveness of genetic evaluation with transformed data by using dam-daughter pairs of Japanese quails In estimation of variance components with REML it has been assumed that the data were normally distributed. Egg production traits of poultry have been shown to exhibit markedly non-normal distributions. In this study, six traits from an unselected quail line were analysed. The original data were transformed using the well-established power transformation to approach normality. The genetic evaluation was carried out with a multipletrait animal model, based on transformed and untransformed data, respectively. Two traits of laying performance, of egg weight and body weight were analysed simultaneously. To compare the efficiency of breeding values the method of simulated selection with biological data (dam-daughter pairs) was used. To select the dams with intensities between 10% and 90% we used individual records and BLUP-breeding values, estimated with transformed and untransformed data. The response of such a selection was estimated using the corresponding daughter records. Only for the trait laying Performance up to 200 days of life we could indicate an advantage of the transformation. It was shown, that by changing from one trait to multiple-trait genetic evaluation non-normality could be compensate. For 10% intensity the selection for individual laying performance provided an unexpected high response in comparing with the BLUP-method.

Preliminary genetic analysis of beef carcass data

2009 ◽  
Vol 2009 ◽  
pp. 58-58
Author(s):  
M P Coffey ◽  
E Wall ◽  
G Banos ◽  
R Roehe
Keyword(s):  
Genetic Analysis ◽  
Beef Cattle ◽  
Genetic Improvement ◽  
Selective Breeding ◽  
Cost Effective ◽  
Genetic Evaluation ◽  
Carcass Weight ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Farm Livestock

Selective breeding of farm livestock is one of the most cost-effective ways of improving the performance and efficiency of livestock enterprises. Genetic improvement of British beef cattle over a ten year period was recently estimated to be worth approximately £23, and the benefits continue to rise (Amer et al., 2007). While these returns are impressive, they could be improved by increasing the rate of improvement in the purebred population, for example by increasing the relevance of estimated breeding values (EBVs) to beef production by using final carcass weight and grading information. This study will examine the feasibility carcass weights and classifications from UK commercial abattoirs for the genetic evaluation of cattle for carcass weight, carcass fatness class, and carcass conformation class.

scholarly journals Optimized grouping to increase accuracy of prediction of breeding values based on group records in genomic selection breeding programs

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Thinh T. Chu ◽  
John W. M. Bastiaansen ◽  
Peer Berg ◽  
Hans Komen
Keyword(s):  
Genomic Selection ◽  
Egg Production ◽  
Genomic Information ◽  
Breeding Programs ◽  
Breeding Values ◽  
Supervised Clustering ◽  
Phenotypic Group ◽  
Group Members ◽  
Accuracy Of Prediction ◽  
Selection Breeding

Abstract Background Phenotypic records of group means or group sums are a good alternative to individual records for some difficult to measure, but economically important traits such as feed efficiency or egg production. Accuracy of predicted breeding values based on group records increases with increasing relationships between group members. The classical way to form groups with more closely-related animals is based on pedigree information. When genotyping information is available before phenotyping, its use to form groups may further increase the accuracy of prediction from group records. This study analyzed two grouping methods based on genomic information: (1) unsupervised clustering implemented in the STRUCTURE software and (2) supervised clustering that models genomic relationships. Results Using genomic best linear unbiased prediction (GBLUP) models, estimates of the genetic variance based on group records were consistent with those based on individual records. When genomic information was available to constitute the groups, genomic relationship coefficients between group members were higher than when random grouping of paternal half-sibs and of full-sibs was applied. Grouping methods that are based on genomic information resulted in higher accuracy of genomic estimated breeding values (GEBV) prediction compared to random grouping. The increase was ~ 1.5% for full-sibs and ~ 11.5% for paternal half-sibs. In addition, grouping methods that are based on genomic information led to lower coancestry coefficients between the top animals ranked by GEBV. Of the two proposed methods, supervised clustering was superior in terms of accuracy, computation requirements and applicability. By adding surplus genotyped offspring (more genotyped offspring than required to fill the groups), the advantage of supervised clustering increased by up to 4.5% compared to random grouping of full-sibs, and by 14.7% compared to random grouping of paternal half-sibs. This advantage also increased with increasing family sizes or decreasing genome sizes. Conclusions The use of genotyping information for grouping animals increases the accuracy of selection when phenotypic group records are used in genomic selection breeding programs.

scholarly journals Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population

2021 ◽  
pp. 3119-3125
Author(s):  
Piriyaporn Sungkhapreecha ◽  
Ignacy Misztal ◽  
Jorge Hidalgo ◽  
Daniela Lourenco ◽  
Sayan Buaban ◽  
...  
Keyword(s):  
Linear Regression ◽  
Milk Yield ◽  
Heat Tolerance ◽  
Selection Response ◽  
Single Step ◽  
Linear Regression Method ◽  
Genomic Information ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Estimated Breeding Values

Background and Aim: Genomic selection improves accuracy and decreases the generation interval, increasing the selection response. This study was conducted to assess the benefits of using single-step genomic best linear unbiased prediction (ssGBLUP) for genomic evaluations of milk yield and heat tolerance in Thai-Holstein cows and to test the value of old phenotypic data to maintain the accuracy of predictions. Materials and Methods: The dataset included 104,150 milk yield records collected from 1999 to 2018 from 15,380 cows. The pedigree contained 33,799 animals born between 1944 and 2016, of which 882 were genotyped. Analyses were performed with and without genomic information using ssGBLUP and BLUP, respectively. Statistics for bias, dispersion, the ratio of accuracies, and the accuracy of estimated breeding values were calculated using the linear regression (LR) method. A partial dataset excluded the phenotypes of the last generation, and 66 bulls were identified as validation individuals. Results: Bias was considerable for BLUP (0.44) but negligible (–0.04) for ssGBLUP; dispersion was similar for both techniques (0.84 vs. 1.06 for BLUP and ssGBLUP, respectively). The ratio of accuracies was 0.33 for BLUP and 0.97 for ssGBLUP, indicating more stable predictions for ssGBLUP. The accuracy of predictions was 0.18 for BLUP and 0.36 for ssGBLUP. Excluding the first 10 years of phenotypic data (i.e., 1999-2008) decreased the accuracy to 0.09 for BLUP and 0.32 for ssGBLUP. Genomic information doubled the accuracy and increased the persistence of genomic estimated breeding values when old phenotypes were removed. Conclusion: The LR method is useful for estimating accuracies and bias in complex models. When the population size is small, old data are useful, and even a small amount of genomic information can substantially improve the accuracy. The effect of heat stress on first parity milk yield is small.

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