Accuracy of genomic breeding values and predictive ability for postweaning liveweight and age at first calving in a Nellore cattle population with missing sire information

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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PSXII-22 Genomic prediction accuracy for feed efficiency related traits using different pseudo-phenotypes, prediction and validation methods in Nellore cattle

Journal of Animal Science ◽

10.1093/jas/skaa278.446 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 245-246

Author(s):

Cláudio U Magnabosco ◽

Fernando Lopes ◽

Valentina Magnabosco ◽

Raysildo Lobo ◽

Leticia Pereira ◽

...

Keyword(s):

Body Weight ◽

Weight Gain ◽

Genomic Prediction ◽

Feed Efficiency ◽

Prediction Accuracy ◽

Body Weight Gain ◽

Prediction Methods ◽

Genomic Breeding ◽

Validation Population ◽

Nellore Cattle

Abstract The aim of the study was to evaluate prediction methods, validation approaches and pseudo-phenotypes for the prediction of the genomic breeding values of feed efficiency related traits in Nellore cattle. It used the phenotypic and genotypic information of 4,329 and 3,594 animals, respectively, which were tested for residual feed intake (RFI), dry matter intake (DMI), feed efficiency (FE), feed conversion ratio (FCR), residual body weight gain (RG), and residual intake and body weight gain (RIG). Six prediction methods were used: ssGBLUP, BayesA, BayesB, BayesCπ, BLASSO, and BayesR. Three validation approaches were used: 1) random: where the data was randomly divided into ten subsets and the validation was done in each subset at a time; 2) age: the division into the training (2010 to 2016) and validation population (2017) were based on the year of birth; 3) genetic breeding value (EBV) accuracy: the data was split in the training population being animals with accuracy above 0.45; and validation population those below 0.45. We checked the accuracy and bias of genomic value (GEBV). The results showed that the GEBV accuracy was the highest when the prediction is obtained with ssGBLUP (0.05 to 0.31) (Figure 1). The low heritability obtained, mainly for FE (0.07 ± 0.03) and FCR (0.09 ± 0.03), limited the GEBVs accuracy, which ranged from low to moderate. The regression coefficient estimates were close to 1, and similar between the prediction methods, validation approaches, and pseudo-phenotypes. The cross-validation presented the most accurate predictions ranging from 0.07 to 0.037. The prediction accuracy was higher for phenotype adjusted for fixed effects than for EBV and EBV deregressed (30.0 and 34.3%, respectively). Genomic prediction can provide a reliable estimate of genomic breeding values for RFI, DMI, RG and RGI, as to even say that those traits may have higher genetic gain than FE and FCR.

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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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