Parallel Conditional Expectation Iteration Genomic Breeding Values Prediction Based on OpenMP

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

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Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

animal ◽

10.1017/s175173111800085x ◽

2018 ◽

Vol 12 (11) ◽

pp. 2235-2245 ◽

Cited By ~ 1

Author(s):

D.A. Grossi ◽

L.F. Brito ◽

M. Jafarikia ◽

F.S. Schenkel ◽

Z. Feng

Keyword(s):

Genotype Imputation ◽

Low Density ◽

Genomic Breeding ◽

Breeding Values ◽

Snp Panels

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Genomic Breeding Values for Claw Diseases/Disorders in Czech Holstein Cows

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun201967051245 ◽

2019 ◽

Vol 67 (5) ◽

pp. 1245-1251

Author(s):

Ludmila Zavadilová ◽

Eva Kašná ◽

Zuzana Krupová

Keyword(s):

Infectious Diseases ◽

Fixed Effects ◽

Genetic Correlations ◽

Genetic Effect ◽

Holstein Cows ◽

Additive Genetic Effect ◽

Genomic Information ◽

White Line ◽

Genomic Breeding ◽

Breeding Values

Genomic breeding values (GEBV) were predicted for claw diseases/disorders in Holstein cows. The data sets included 6,498, 6,641 and 16,208 cows for the three groups of analysed disorders. The analysed traits were infectious diseases (ID), including digital and interdigital dermatitis and interdigital phlegmon, and non-infectious diseases (NID), including ulcers, white line disease, horn fissures, and double sole and overall claw disease (OCD), comprising all recorded disorders. Claw diseases/disorders were defined as 0/1 occurrence per lactation. Linear animal models were employed for prediction of conventional breeding values (BV) and genomic breeding values (GEBV), including the random additive genetic effect of animal and the permanent environmental effect of cow and fixed effects of parity, herd, year and month of calving. Both high and intermediate weights (80% and 50%, respectively) of genomic information were employed for GEBV50 and GEBV80 prediction. The estimated heritability for ID was 3.47%, whereas that for NID 4.61% and for OCD was 2.29%. Approximate genetic correlations among claw diseases/disorders traits ranged from 19% (ID x NID) to 81% (NID x OCD). The correlations between predicted BV and GEBV50 (84–99%) were higher than those between BV and GEBV80 (70–98%). Reliability of breeding values was low for each claw disease/disorder (on average, 3.7 to 14.8%) and increased with the weight of genomic information employed.

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Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip

BMC Genetics ◽

10.1186/s12863-017-0476-8 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 22

Author(s):

Luiz F. Brito ◽

Shannon M. Clarke ◽

John C. McEwan ◽

Stephen P. Miller ◽

Natalie K. Pickering ◽

...

Keyword(s):

Meat Quality ◽

Quality Traits ◽

Sheep Population ◽

Genomic Breeding ◽

Breeding Values ◽

Snp Chip ◽

Meat Quality Traits ◽

Breed Sheep

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Genomic selection for white spot syndrome virus resistance in whiteleg shrimp boosts survival under an experimental challenge test

Scientific Reports ◽

10.1038/s41598-020-77580-3 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Marie Lillehammer ◽

Rama Bangera ◽

Marcela Salazar ◽

Sergio Vela ◽

Edna C. Erazo ◽

...

Keyword(s):

Genomic Selection ◽

Genetic Improvement ◽

White Spot Syndrome Virus ◽

Random Mating ◽

Challenge Test ◽

White Spot ◽

Breeding Value ◽

Genomic Breeding ◽

Breeding Values ◽

White Spot Syndrome

AbstractWhite spot syndrome virus (WSSV) causes major worldwide losses in shrimp aquaculture. The development of resistant shrimp populations is an attractive option for management of the disease. However, heritability for WSSV resistance is generally low and genetic improvement by conventional selection has been slow. This study was designed to determine the power and accuracy of genomic selection to improve WSSV resistance in Litopenaeus vannamei. Shrimp were experimentally challenged with WSSV and resistance was evaluated as dead or alive (DOA) 23 days after infestation. All shrimp in the challenge test were genotyped for 18,643 single nucleotide polymorphisms. Breeding candidates (G0) were ranked on genomic breeding values for WSSV resistance. Two G1 populations were produced, one from G0 breeders with high and the other with low estimated breeding values. A third population was produced from “random” mating of parent stock. The average survival was 25% in the low, 38% in the random and 51% in the high-genomic breeding value groups. Genomic heritability for DOA (0.41 in G1) was high for this type of trait. The realised genetic gain and high heritability clearly demonstrates large potential for further genetic improvement of WSSV resistance in the evaluated L. vannamei population using genomic selection.

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A study of Genomic Prediction across Generations of Two Korean Pig Populations

Animals ◽

10.3390/ani9090672 ◽

2019 ◽

Vol 9 (9) ◽

pp. 672 ◽

Cited By ~ 1

Author(s):

Beatriz Castro Dias Castro Dias Cuyabano ◽

Hanna Wackel ◽

Donghyun Shin ◽

Cedric Gondro

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Reference Population ◽

Relevant Information ◽

Production Traits ◽

Genomic Evaluation ◽

Genomic Breeding ◽

Breeding Values ◽

The Relationship ◽

Dense Marker

Genomic models that incorporate dense marker information have been widely used for predicting genomic breeding values since they were first introduced, and it is known that the relationship between individuals in the reference population and selection candidates affects the prediction accuracy. When genomic evaluation is performed over generations of the same population, prediction accuracy is expected to decay if the reference population is not updated. Therefore, the reference population must be updated in each generation, but little is known about the optimal way to do it. This study presents an empirical assessment of the prediction accuracy of genomic breeding values of production traits, across five generations in two Korean pig breeds. We verified the decay in prediction accuracy over time when the reference population was not updated. Additionally we compared the prediction accuracy using only the previous generation as the reference population, as opposed to using all previous generations as the reference population. Overall, the results suggested that, although there is a clear need to continuously update the reference population, it may not be necessary to keep all ancestral genotypes. Finally, comprehending how the accuracy of genomic prediction evolves over generations within a population adds relevant information to improve the performance of genomic selection.

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PREDICTION OF PHENOTYPIC AND GENOTYPIC VALUES BY BLUP/GWS AND NEURAL NETWORKS

Revista Caatinga ◽

10.1590/1983-21252018v31n301rc ◽

2018 ◽

Vol 31 (3) ◽

pp. 532-540 ◽

Cited By ~ 2

Author(s):

ALISSON ESDRAS COUTINHO ◽

DIOGO GONÇALVES NEDER ◽

MAIRYKON COÊLHO DA SILVA ◽

ELIANE CRISTINA ARCELINO ◽

SILVAN GOMES DE BRITO ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Breeding Cycle ◽

Prediction Methods ◽

Best Linear Unbiased Predictor ◽

Entire Genome ◽

Genomic Breeding ◽

Breeding Values ◽

Genome Wide ◽

Best Linear Unbiased

ABSTRACT Genome-wide selection (GWS) uses simultaneously the effect of the thousands markers covering the entire genome to predict genomic breeding values for individuals under selection. The possible benefits of GWS are the reduction of the breeding cycle, increase in gains per unit of time, and decrease of costs. However, the success of the GWS is dependent on the choice of the method to predict the effects of markers. Thus, the objective of this work was to predict genomic breeding values (GEBV) through artificial neural networks (ANN), based on the estimation of the effect of the markers, compared to the Ridge Regression-Best Linear Unbiased Predictor/Genome Wide Selection (RR-BLUP/GWS). Simulations were performed by software R to provide correlations concerning ANN and RR-BLUP/GWS. The prediction methods were evaluated using correlations between phenotypic and genotypic values and predicted GEBV. The results showed the superiority of the ANN in predicting GEBV in simulations with higher and lower marker densities, with higher levels of linkage disequilibrium and heritability.

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