scholarly journals Application of single-step genomic evaluation for crossbred performance in pig1

2016 ◽  
Vol 94 (3) ◽  
pp. 936-948 ◽  
Author(s):  
T. Xiang ◽  
B. Nielsen ◽  
G. Su ◽  
A. Legarra ◽  
O. F. Christensen
Keyword(s):  
Single Step ◽  
Genomic Evaluation

scholarly journals Sparse single-step method for genomic evaluation in pigs

2016 ◽  
Vol 48 (1) ◽  
Author(s):  
Tage Ostersen ◽  
Ole F. Christensen ◽  
Per Madsen ◽  
Mark Henryon
Keyword(s):  
Single Step ◽  
Genomic Evaluation ◽  
Step Method

A single-step genomic evaluation for milk production in Egyptian buffalo

2020 ◽  
Vol 234 ◽  
pp. 103977 ◽  
Author(s):  
Hamdy Abdel-Shafy ◽  
Mohamed A.A. Awad ◽  
Hussein El-Regalaty ◽  
Ahmed Ismael ◽  
Salah El-Din El-Assal ◽  
...  
Keyword(s):  
Milk Production ◽  
Single Step ◽  
Genomic Evaluation ◽  
Egyptian Buffalo

scholarly journals 209 Genomic selection for multiple maternal and growth traits in large white pigs using Single-Step GBLUP

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 42-42
Author(s):  
Breno Fragomeni ◽  
Zulma Vitezica ◽  
Justine Liu ◽  
Yijian Huang ◽  
Kent Gray ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Growth Traits ◽  
Computing Time ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Single Step ◽  
Genomic Information ◽  
Genomic Evaluation ◽  
Large White ◽  
Daily Gain

Abstract The objective of this study was to implement a multi-trait genomic evaluation for maternal and growth traits in a swine population. Phenotypes for preweaning mortality, litter size, weaning weight, and average daily gain were available for 282K Large White pigs. The pedigree included 314k individuals, of which 35,731 were genotyped for 45K SNPs. Variance components were estimated in a multi-trait animal model without genomic information by AIREMLF90. Genomic breeding values were estimated using the genomic information by single-step GBLUP. The algorithm for proven and young (APY) was used to reduce computing time. Genetic correlation between proportion and the total number of preweaning deaths was 0.95. A strong, positive genetic correlation was also observed between weaning weight and average daily gain (r = 0.94). Conversely, the genetic correlations between mortality and growth traits were negative, with an average of -0.7. To avoid computations by expensive threshold models, preweaning mortality was transformed from a binary trait to two linear dam traits: proportion and a total number of piglets dead before weaning. Because of the high genetic correlations within groups of traits, inclusion of only one growth and one mortality trait in the model decreases computing time and allows for the inclusion of other traits. Reduction in computing time for the evaluation using APY was up to 20x, and no differences in EPD ranking were observed. The algorithm for proven and young improves the efficiency of genomic evaluation in swine without harming the quality of predictions. For this population, a binary trait of mortality can be replaced by a linear trait of the dam, resulting in a similar ranking for the selection candidates.

scholarly journals Alternative methods improve the accuracy of genomic prediction using information from a causal point mutation in a dairy sheep model

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Claire Oget ◽  
Marc Teissier ◽  
Jean-Michel Astruc ◽  
Gwenola Tosser-Klopp ◽  
Rachel Rupp
Keyword(s):  
Major Gene ◽  
Single Step ◽  
Gene Content ◽  
Alternative Methods ◽  
Dairy Sheep ◽  
Genomic Evaluation ◽  
Multiple Trait ◽  
Sheep Model ◽  
Breeding Values ◽  
Polygenic Component

Abstract Background Genomic evaluation is usually based on a set of markers assumed to be linked with causal mutations. Selection and precise management of major genes and the remaining polygenic component might be improved by including causal polymorphisms in the evaluation models. In this study, various methods involving a known mutation were used to estimate prediction accuracy. The SOCS2 gene, which influences body growth, milk production and somatic cell scores, a proxy for mastitis, was studied as an example in dairy sheep. Methods The data comprised 1,503,148 phenotypes and 9844 54K SNPs genotypes. The SOCS2 SNP was genotyped for 4297 animals and imputed in the above 9844 animals. Breeding values and their accuracies were estimated for each of nine traits by using single-step approaches. Pedigree-based BLUP, single-step genomic BLUP (ssGBLUP) involving the 54K ovine SNPs chip, and four weighted ssGBLUP (WssGBLUP) methods were compared. In WssGBLUP methods, weights are assigned to SNPs depending on their effect on the trait. The ssGBLUP and WssGBLUP methods were again tested after including the SOCS2 causal mutation as a SNP. Finally, the Gene Content approach was tested, which uses a multiple-trait model that considers the SOCS2 genotype as a trait. Results EBV accuracies were increased by 14.03% between the pedigree-based BLUP and ssGBLUP methods and by 3.99% between ssGBLUP and WssGBLUP. Adding the SOCS2 SNP to ssGBLUP methods led to an average gain of 0.26%. Construction of the kinship matrix and estimation of breeding values was generally improved by placing emphasis on SNPs in regions with a strong effect on traits. In the absence of chip data, the Gene Content method, compared to pedigree-based BLUP, efficiently accounted for partial genotyping information on SOCS2 as accuracy was increased by 6.25%. This method also allowed dissociation of the genetic component due to the major gene from the remaining polygenic component. Conclusions Causal mutations with a moderate to strong effect can be captured with conventional SNP chips by applying appropriate genomic evaluation methods. The Gene Content method provides an efficient way to account for causal mutations in populations lacking genome-wide genotyping.

scholarly journals Single-step methods for genomic evaluation in pigs

animal ◽  
2012 ◽  
Vol 6 (10) ◽  
pp. 1565-1571 ◽  
Author(s):  
O.F. Christensen ◽  
P. Madsen ◽  
B. Nielsen ◽  
T. Ostersen ◽  
G. Su
Keyword(s):  
Single Step ◽  
Genomic Evaluation

scholarly journals Single‐step genomic evaluation of Russian dairy cattle using internal and external information

2021 ◽  
Author(s):  
Andrei A. Kudinov ◽  
Esa A. Mäntysaari ◽  
Timo J. Pitkänen ◽  
Ekaterina I. Saksa ◽  
Gert P. Aamand ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Single Step ◽  
External Information ◽  
Genomic Evaluation
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