Literature DB >> 24210491

On the limited increase in validation reliability using high-density genotypes in genomic best linear unbiased prediction: observations from Fleckvieh cattle.

J Ertl1, C Edel2, R Emmerling2, H Pausch3, R Fries3, K-U Götz2.   

Abstract

This study investigated reliability of genomic predictions using medium-density (40,089; 50K) or high-density (HD; 388,951) marker sets. We developed an approximate method to test differences in validation reliability for significance. Model-based reliability and the effect of HD genotypes on inflation of predictions were analyzed additionally. Genomic breeding values were predicted for at least 1,321 validation bulls based on phenotypes and genotypes of at least 5,324 calibration bulls by means of a linear model in milk, fat, and protein yield; somatic cell score; milkability; muscling; udder, feet, and legs score as well as stature. In total, 1,485 bulls were actually HD genotyped and HD genotypes of the other animals were imputed from 50K genotypes using FImpute software. Validation reliability was measured as the coefficient of determination of the weighted regression of daughter yield deviations on predicted breeding values divided by the reliability of daughter yield deviations and inflation was evaluated by the slope of this regression. Model-based reliability was calculated from the model. Distributions for validation reliability of 50K markers were derived by repeated sampling of 50,000-marker samples from HD to test differences in validation reliability statistically. Additionally, the benefit of HD genotypes in validation reliability was tested by repeated sampling of validation groups and calculation of the difference in validation reliability between HD and 50K genotypes for the sampled groups of bulls. The mean benefit in validation reliability of HD genotypes was 0.015 compared with real 50K genotypes and 0.028 compared with 50K samples from HD affected by imputation error and was significant for all traits. The model-based reliability was, on average, 0.036 lower and the regression coefficient was 0.036 closer to the expected value with HD genotypes. The observed gain in validation reliability with HD genotypes was similar to expectations based on the number of markers and the effective number of segregating chromosome segments. Sampling error in the marker-based relationship coefficients causing overestimation of the model-based reliability was smaller with HD genotypes. Inflation of the genomic predictions was reduced with HD genotypes, accordingly. Similar effects on model-based reliability and inflation, but not on the validation reliability, were obtained by shrinkage estimation of the realized relationship matrix from 50K genotypes.
Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  genomic evaluation; imputation; marker density

Mesh:

Year:  2013        PMID: 24210491     DOI: 10.3168/jds.2013-6855

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  9 in total

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Journal:  Front Genet       Date:  2015-05-13       Impact factor: 4.599

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3.  Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle.

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Journal:  Genet Sel Evol       Date:  2017-02-21       Impact factor: 4.297

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7.  A frameshift mutation in GON4L is associated with proportionate dwarfism in Fleckvieh cattle.

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8.  Systematic genotyping of groups of cows to improve genomic estimated breeding values of selection candidates.

Authors:  Laura Plieschke; Christian Edel; Eduardo C G Pimentel; Reiner Emmerling; Jörn Bennewitz; Kay-Uwe Götz
Journal:  Genet Sel Evol       Date:  2016-09-28       Impact factor: 4.297

9.  Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel.

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Journal:  Animals (Basel)       Date:  2021-06-25       Impact factor: 2.752

  9 in total

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