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.

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 Effects of management decisions on genetic evaluation of simulated calving records using random regression

2021 ◽  
Author(s):  
M D MacNeil ◽  
J W Buchanan ◽  
M L Spangler ◽  
E Hay
Keyword(s):  
Reproductive Success ◽  
Simulated Data ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Management Decisions ◽  
Third Order ◽  
Data Set ◽  
Binary Phenotype ◽  
Random Regression Model ◽  
Missing Observation

Abstract The objective of this study was to evaluate the effects of various data structures on the genetic evaluation for the binary phenotype of reproductive success. The data were simulated based on an existing pedigree and an underlying fertility phenotype with a heritability of 0.10. A data set of complete observations was generated for all cows. This data set was then modified mimicking the culling of cows when they first failed to reproduce, cows having a missing observation at either their second or fifth opportunity to reproduce as if they had been selected as donors for embryo transfer, and censoring records following the sixth opportunity to reproduce as in a cull-for-age strategy. The data were analyzed using a third order polynomial random regression model. The EBV of interest for each animal was the sum of the age-specific EBV over the first 10 observations (reproductive success at ages 2-11). Thus, the EBV might be interpreted as the genetic expectation of number of calves produced when a female is given ten opportunities to calve. Culling open cows resulted in the EBV for 3 year-old cows being reduced from 8.27 ± 0.03 when open cows were retained to 7.60 ± 0.02 when they were culled. The magnitude of this effect decreased as cows grew older when they first failed to reproduce and were subsequently culled. Cows that did not fail over the 11 years of simulated data had an EBV of 9.43 ± 0.01 and 9.35 ± 0.01 based on analyses of the complete data and the data in which cows that failed to reproduce were culled, respectively. Cows that had a missing observation for their second record had a significantly reduced EBV, but the corresponding effect at the fifth record was negligible. The current study illustrates that culling and management decisions, and particularly those that impact the beginning of the trajectory of sustained reproductive success, can influence both the magnitude and accuracy of resulting EBV.

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.

Repeatability and bias of estimated breeding values for dairy bulls and bull dams calculated from animal model evaluations

1993 ◽  
Vol 57 (2) ◽  
pp. 175-182 ◽  
Author(s):  
P. Uimari ◽  
E. A. Mäntysaari
Keyword(s):  
Animal Model ◽  
Dairy Cow ◽  
Relative Weight ◽  
Breeding Value ◽  
Potential Bias ◽  
Data Set ◽  
Breeding Values ◽  
Dairy Bulls ◽  
Estimated Breeding Values ◽  
Genetic Standard Deviation

AbstractAn animal model and an approximative method for calculating repeatabilities of estimated breeding values are used in Finnish dairy cow evaluation. Changes in estimated breeding values over time as daughters accumulate were studied. Special emphasis was given to the accuracy and potential bias in the pedigree indices of young sires. The data set used was the same as in the national evaluation and the traits investigated were protein yield and somatic cell count. The average repeatability in evaluation of bulls without daughters was 0·37. The empirical repeatability defined as a squared correlation between the pedigree index and the final sire proof was only 0·15. The reduction in the repeatability was attributed to the selection on pedigree index. The upward bias observed in pedigree indices was 5 kg (approx. 0·3 of genetic standard deviation). The bias was caused by the overestimation of bull dams' breeding value. Also the proofs of bull sires increased after the second crop of daughters. The correlation between the evaluations of the same sire calculated from two separate equal size daughter groups was 0·91 when the bull had 10 to 50 daughters and 0·87 with over 100 daughters. This illustrates how the relative weight of the pedigree decreases while more progeny information is accumulated in the evaluation.

scholarly journals Genetic evaluation for length of productive life in Slovak Pinzgau cattle

2008 ◽  
Vol 51 (5) ◽  
pp. 438-448 ◽  
Author(s):  
G. Mészáros ◽  
C. Fuerst ◽  
B. Fuerst-Waltl ◽  
O. Kadlečík ◽  
R. Kasarda ◽  
...  
Keyword(s):  
Milk Production ◽  
Proportional Hazards ◽  
Genetic Evaluation ◽  
Data Set ◽  
Final Model ◽  
Breeding Values ◽  
Age At First Calving ◽  
Clear Tendency ◽  
Productive Life ◽  
Increasing Risk

Abstract. The proportional hazards method was used to estimate breeding values for functional length of productive life within the endangered Slovak Pinzgau population. The analyzed data set contained 21,985 cows, daughters of 254 sires. The risk of culling was higher for cows with lower milk production relative to herd average, higher age at first calving and in herds decreasing in size. In the first lactation the culling risk was highest at the beginning, and decreased during lactation. From second lactation onwards an increasing risk was observed. The effect of breed composition was found insignificant, and was not included into final model. A heritability of 0.05 was estimated for functional length of productive life. The average reliability of estimates was 0.25. No clear tendency in average breeding values by year of birth of bulls was observed.

scholarly journals Reliability of breeding values between random regression and 305-day lactation models

2016 ◽  
Vol 51 (11) ◽  
pp. 1848-1856
Author(s):  
Alessandro Haiduck Padilha ◽  
◽  
Jaime Araujo Cobuci ◽  
Darlene dos Santos Daltro ◽  
José Braccini Neto
Keyword(s):  
Legendre Polynomials ◽  
Regression Models ◽  
Protein Yield ◽  
Random Regression ◽  
Holstein Cattle ◽  
Average Increase ◽  
Data Set ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Abstract The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.

A comparison of a linear and proportional hazards approach to analyse discrete longevity data in dairy cows

2000 ◽  
Vol 70 (2) ◽  
pp. 197-206 ◽  
Author(s):  
R. Lubbers ◽  
S. Brotherstone ◽  
V.P. Ducrocq ◽  
P.M. Visscher
Keyword(s):  
Discrete Time ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Weibull Model ◽  
Logarithmic Scale ◽  
Data Set ◽  
Breeding Values ◽  
Hazards Model ◽  
Estimated Breeding Values ◽  
The Uk

AbstractThe objective of this study was to compare two methods for analysis of longevity in dairy cattle. The first method, currently used for routine genetic evaluation in the UK, uses a linear model to analyse lifespan, i.e. the number of lactations a cow has survived or is expected to survive. The second method was based on the concept of proportional hazard, i.e. modelling the conditional survival probability of a cow as a function of time. Comparisons were based on estimated heritabilities, ranking of estimated breeding values of sires, estimated effects of covariates used in the final models, and the distribution of residuals. The same data set, 21497 observations on the number of lactations cows had survived, was used for both analyses, even in the presence of censored observations. Cows in the data were progeny of 487 sires. Heritability estimates for lifespan or survival were approximately 0·06 for both methods, using the definition of heritability on a logarithmic scale for the proportional hazards model. Correlations between breeding values for sires were high, with absolute values ranging from 0·93 to 0·98, depending on the model fitted. It was concluded that it may be justified to use the standard Weibull model even for discrete time measures such as the number of completed lactations, but that more research is needed in the area of discrete time variates.

scholarly journals Genetic evaluation for longevity of Croatian Simmental bulls using a piecewise Weibull model

2013 ◽  
Vol 56 (1) ◽  
pp. 89-101
Author(s):  
S. Jovanovac ◽  
N. Raguž ◽  
J. Sölkner ◽  
G. Mészáros
Keyword(s):  
Milk Production ◽  
Weibull Model ◽  
Fixed Time ◽  
Herd Size ◽  
Genetic Evaluation ◽  
Genetic Trend ◽  
Data Set ◽  
Breeding Values ◽  
Age At First Calving ◽  
Productive Life

Abstract. Genetic evaluation of sires for functional longevity was conducted using survival analysis techniques. The data set consisted of 49 659 Simmental cows with first calving from 1997 to 2008. A piecewise Weibull sire model was used to estimate breeding values of 251 bulls for functional length of productive life of their daughters. The model was stratified by parity i.e. a separate baseline hazard was computed for each stratum. Besides the random sire effect, the model included the fixed time independent effects of age at first calving, herd size and region as well as the time dependent effects of relative milk production and year*season of first calving. The highest impact on longevity was found for relative milk production. Cows with the lowest milk yields were at approximately 2.7 times higher risk of culling compared to cows with average milk production. Effects of age at first calving, herd size and country region had lower impact on longevity. Sire variance was 0.023 which results in a heritability of 0.06 for functional length of productive life. The average approximate reliability of estimated breeding values was 0.49. Genetic trend showed no clear tendency by year of birth of bulls.

A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population

2018 ◽  
Vol 98 (3) ◽  
pp. 565-575 ◽  
Author(s):  
Mario L. Piccoli ◽  
Luiz F. Brito ◽  
José Braccini ◽  
Fernanda V. Brito ◽  
Fernando F. Cardoso ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Production Systems ◽  
Simulated Data ◽  
Single Step ◽  
Breeding Values ◽  
True Breeding ◽  
Blending Method ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
Genomic Predictions

The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.

Sign in / Sign up

Export Citation Format

Share Document