Dairy sire evaluation fitting some of the herd-year-season effects as random

Chauhan, V. P. S.

doi:10.1016/0301-6226(87)90014-5

Cited by 21 publications

(14 citation statements)

References 12 publications

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“…This is common, e.g., in genetic evaluation of dairy cattle. A common strategy treats these effects as random by viewing levels as a random sample from a population (e.g., [21,22]). In this study, we preadjusted phenotypes for systematic effects by using a random effects model so that the model fitted was

y_{i}^{R} = μ + g_{i} + e

where

y_{i}^{R}

represents precorrected phenotypes using the random model.…”

Section: Methodsmentioning

confidence: 99%

Genome-enabled prediction of quantitative traits in chickens using genomic annotation

et al. 2014

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BackgroundGenome-wide association studies have been deemed successful for identifying statistically associated genetic variants of large effects on complex traits. Past studies have found enrichment of trait-associated SNPs in functionally annotated regions, while depletion was reported for intergenic regions (IGR). However, no systematic examination of connections between genomic regions and predictive ability of complex phenotypes has been carried out.ResultsIn this study, we partitioned SNPs based on their annotation to characterize genomic regions that deliver low and high predictive power for three broiler traits in chickens using a whole-genome approach. Additive genomic relationship kernels were constructed for each of the genic regions considered, and a kernel-based Bayesian ridge regression was employed as prediction machine. We found that the predictive performance for ultrasound area of breast meat from using genic regions marked by SNPs was consistently better than that from SNPs in IGR, while IGR tagged by SNPs were better than the genic regions for body weight and hen house egg production. We also noted that predictive ability delivered by the whole battery of markers was close to the best prediction achieved by one of the genomic regions.ConclusionsWhole-genome regression methods use all available quality filtered SNPs into a model, contrary to accommodating only validated SNPs from exonic or coding regions. Our results suggest that, while differences among genomic regions in terms of predictive ability were observed, the whole-genome approach remains as a promising tool if interest is on prediction of complex traits.

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y_{i}^{R} = μ + g_{i} + e

where

y_{i}^{R}

represents precorrected phenotypes using the random model.…”

Section: Methodsmentioning

confidence: 99%

Genome-enabled prediction of quantitative traits in chickens using genomic annotation

et al. 2014

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show abstract

“…The most important work dealing with statistical treatment of contemporary groups of animals has been performed in the genetic evaluation of dairy cattle. In this case, contemporary groups with some animals showed an important loss of information when they are treated as fixed ( Chauhan 1987; W ood et al 1991; Vissher and Goddard 1993 ; Bebber et al 1997). This loss of information can be avoided with the random treatment ( Van Vleck 1987; Visscher and Goddard 1993), but biased predictors can be obtained if there is some dependence between genetic and environmental effects, which can occur when there is an improvement in selection and management.…”

Section: Introductionmentioning

confidence: 99%

Fixed or random contemporary groups in genetic evaluation for litter size in pigs using a single trait repeatability animal model

Babot

Noguera

Alfonso

et al. 2003

J Animal Breeding Genetics

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Summary The importance of using fixed or random contemporary groups in the genetic evaluation of litter size in pigs was analysed by using farm and simulated data. Farm data were from four Spanish pig breeding populations, two Landrace (13 084 and 13 619 records) and two Large White (2762 and 8455 records). A simulated population (200 sows and 10 boars) selected for litter size, in which litter size was simulated using a repeatability animal model with random herd–year–season (HYS), was used to obtain simulated data. With farm data, the goodness‐of‐fit and the predictive ability of a repeatability animal model were calculated under several definitions of the HYS effect. A residual maximum likelihood estimator of the HYS variance in each population was obtained as well. In this sense, HYS was considered as either fixed or random with different number of individuals per level. Results from farm data showed that HYS variance was small in relation to the total variance (ranging from 0.01 to 0.04). The treatment of HYS effect as fixed, reduced the residual variance but the size of HYS levels does not explain by itself the goodness‐of‐fit of the model. The results obtained by simulation showed that the predictive ability of the model is better for random than for fixed HYS models. However, the improvement of predictive ability does not lead to a significant increase of the genetic response. Finally, results showed that random HYS models biased the estimates of genetic response when there is an environmental trend.

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“…Previous workers using sire models (C unningham 1965; H enderson 1973 and 1975; M iller et al 1968; C hauhan 1987) have also reported smaller prediction error variances when environmental factors were fitted as random. However, the extent of improvement in these studies and in the present, was variable.…”

Section: Resultsmentioning

confidence: 91%

Comparison of alternative methods of handling contemporary group effects in animal model prediction

Sivarajasingam¹

1993

J Animal Breeding Genetics

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Methods to associate animals between periods (grouping of records within a calving season into a 60-day interval starting from the date of the first calf born for 400-day weight analysis) within the contemporary group classification on sexmanagement group-date of weighing, using an animal model were compared. The data were derived from ten Angus herds. Assigning animals to more than one period and/or treating period as random or introducing days from start of calving period as a co-variate (linear and quadratic) did little to improve herd mean square error compared to the basic model where there were no associations between periods. The reduction in the standard errors of predictions were significant (p < 0.05) only when period within sex-management group-date of weighing was treated as random. However, the use of a similarity matrix S (which associates periods within the fixed contemporary group classification) in the mixed model equations reduced mean square error by as much as 7%. Using simulated observations for the ten herds, highest correlations between true breeding values and estimated breeding values were obtained only for the model using the similarity matrix. ZUSAMMENFASSUNG: Vergleich alternativer Methoden der Behandlung von Wirkungen kontemporärer Gruppen bei Tiermodellvoraussagen Methoden zur Verbindung zwischen Tieren verschiedener Perioden (Leistungsabschlüsse in 60-Tage-Intervallen einer Abkalbesaison für 400 Tagegewichte ab dem ersten geborenen Kalb) innerhalb kontemporärer Gruppenklassifikation und Geschlechts-Managementgruppen bei Tiermodellen wurden verglichen. Die Daten stammten von 10 Angusherden. Zuordnung von Tieren an mehr als eine Periode als zufällig oder Einbeziehung von Tagen von Beginn der Abkalbeperiode als Kovariable (linear und quadratisch) half wenig zur Verbesserung des Herdenfehlermittelquadrates im Vergleich zu dem Grundmodell, wo keine Verbindungen zwischen Perioden unterstellt wurden. Die Verminderung des Standardfehlers der Voraussage war signifikant (p < als 0,05) nur wenn Perioden innerhalb Geschlechts-Managementgruppen als zufällig betrachtet worden sind. Allerdings hat die Verwendung der Ähnlichkeitsmatrix S (welche Perioden innerhalb einer fixen kontemporären Gruppenklassifikation assoziiert) in der gemischten Modellgleichungen den Mittelquadratfehler um bis zu 7% reduziert. Unter Verwendung simulierter Beobachtungen für 10 Herden haben sich die höchsten Korrelationen zwischen wahren Zuchtwerten und geschätzten nur bei dem Modell ergeben, das die Ähnlichkeitsmatrix verwendet hat.

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Dairy sire evaluation fitting some of the herd-year-season effects as random

Cited by 21 publications

References 12 publications

Genome-enabled prediction of quantitative traits in chickens using genomic annotation

Genome-enabled prediction of quantitative traits in chickens using genomic annotation

Fixed or random contemporary groups in genetic evaluation for litter size in pigs using a single trait repeatability animal model

Comparison of alternative methods of handling contemporary group effects in animal model prediction

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