scholarly journals Erratum to: Comparison of prediction accuracy for genomic estimated breeding value using the reference pig population of single-breed and admixed-breed

2020 ◽  
Vol 62 (6) ◽  
pp. 960-960
Author(s):  
Soo Hyun Lee ◽  
Dongwon Seo ◽  
Doo Ho Lee ◽  
Ji Min Kang ◽  
Yeong Kuk Kim ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Breeding Value ◽  
Estimated Breeding Value

scholarly journals Comparison of prediction accuracy for genomic estimated breeding value using the reference pig population of single-breed and admixed-breed

2020 ◽  
Vol 62 (4) ◽  
pp. 438-448 ◽  
Author(s):  
Soo Hyun Lee ◽  
Dongwon Seo ◽  
Doo Ho Lee ◽  
Ji Min Kang ◽  
Yeong Kuk Kim ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Breeding Value ◽  
Estimated Breeding Value

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals 45 A permutation test for validation of genomic estimated breeding values

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 8-9
Author(s):  
Zahra Karimi ◽  
Brian Sullivan ◽  
Mohsen Jafarikia
Keyword(s):  
Permutation Test ◽  
Breeding Value ◽  
Small Data ◽  
Data Sets ◽  
Type I ◽  
Future Performance ◽  
Small Data Sets ◽  
Estimated Breeding Value ◽  
Estimated Breeding Values ◽  
Top 40

Abstract Previous studies have shown that the accuracy of Genomic Estimated Breeding Value (GEBV) as a predictor of future performance is higher than the traditional Estimated Breeding Value (EBV). The purpose of this study was to estimate the potential advantage of selection on GEBV for litter size (LS) compared to selection on EBV in the Canadian swine dam line breeds. The study included 236 Landrace and 210 Yorkshire gilts born in 2017 which had their first farrowing after 2017. GEBV and EBV for LS were calculated with data that was available at the end of 2017 (GEBV2017 and EBV2017, respectively). De-regressed EBV for LS in July 2019 (dEBV2019) was used as an adjusted phenotype. The average dEBV2019 for the top 40% of sows based on GEBV2017 was compared to the average dEBV2019 for the top 40% of sows based on EBV2017. The standard error of the estimated difference for each breed was estimated by comparing the average dEBV2019 for repeated random samples of two sets of 40% of the gilts. In comparison to the top 40% ranked based on EBV2017, ranking based on GEBV2017 resulted in an extra 0.45 (±0.29) and 0.37 (±0.25) piglets born per litter in Landrace and Yorkshire replacement gilts, respectively. The estimated Type I errors of the GEBV2017 gain over EBV2017 were 6% and 7% in Landrace and Yorkshire, respectively. Considering selection of both replacement boars and replacement gilts using GEBV instead of EBV can translate into increased annual genetic gain of 0.3 extra piglets per litter, which would more than double the rate of gain observed from typical EBV based selection. The permutation test for validation used in this study appears effective with relatively small data sets and could be applied to other traits, other species and other prediction methods.

A method for reducing inbreeding with Best Linear Unbiased Prediction

1993 ◽  
Vol 1993 ◽  
pp. 33-33
Author(s):  
B Grundy ◽  
WG Hill
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Selection Decisions ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Commercial Environment ◽  
Unbiased Prediction

An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.

scholarly journals Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3372
Author(s):  
Cesar A. Medina ◽  
Harpreet Kaur ◽  
Ian Ray ◽  
Long-Xi Yu
Keyword(s):  
Salt Stress ◽  
Abiotic Stress ◽  
Medicago Sativa ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Breeding Value ◽  
Phenotypic Traits ◽  
Genome Wide ◽  
Medicago Sativa L

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

scholarly journals Application of selection index method to combine two generations of estimated breeding value in pigs.

1989 ◽  
Vol 26 (3) ◽  
pp. 188-196
Author(s):  
Hiroshi TAKAHASHI ◽  
Takashige SUGIMOTO ◽  
Akio NIBE ◽  
Allan SCHINCKEL ◽  
Yasuo AMEMIYA
Keyword(s):  
Selection Index ◽  
Breeding Value ◽  
Index Method ◽  
Two Generations ◽  
Estimated Breeding Value

Genomic prediction of weight and wool traits in a multi-breed sheep population

2014 ◽  
Vol 54 (5) ◽  
pp. 544 ◽  
Author(s):  
N. Moghaddar ◽  
A. A. Swan ◽  
J. H. J. van der Werf
Keyword(s):  
Genomic Prediction ◽  
Pearson Correlation ◽  
Reference Population ◽  
Breeding Value ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Sheep Population ◽  
Australian Sheep ◽  
Estimated Breeding Value ◽  
Breed Sheep

The objective of this study was to predict the accuracy of genomic prediction for 26 traits, including weight, muscle, fat, and wool quantity and quality traits, in Australian sheep based on a large, multi-breed reference population. The reference population consisted of two research flocks, with the main breeds being Merino, Border Leicester (BL), Poll Dorset (PD), and White Suffolk (WS). The genomic estimated breeding value (GEBV) was based on GBLUP (genomic best linear unbiased prediction), applying a genomic relationship matrix calculated from the 50K Ovine SNP chip marker genotypes. The accuracy of GEBV was evaluated as the Pearson correlation coefficient between GEBV and accurate estimated breeding value based on progeny records in a set of genotyped industry animals. The accuracies of weight traits were relatively low to moderate in PD and WS breeds (0.11–0.27) and moderate to relatively high in BL and Merino (0.25–0.63). The accuracy of muscle and fat traits was moderate to relatively high across all breeds (between 0.21 and 0.55). The accuracy of GEBV of yearling and adult wool traits in Merino was, on average, high (0.33–0.75). The results showed the accuracy of genomic prediction depends on trait heritability and the effective size of the reference population, whereas the observed GEBV accuracies were more related to the breed proportions in the multi-breed reference population. No extra gain in within-breed GEBV accuracy was observed based on across breed information. More investigations are required to determine the precise effect of across-breed information on within-breed genomic prediction.

Effects of growth path, sire type, calving time and sex on growth and carcass characteristics of beef cattle in the agricultural area of Western Australia

2009 ◽  
Vol 49 (6) ◽  
pp. 504 ◽  
Author(s):  
B. L. McIntyre ◽  
G. D. Tudor ◽  
D. Read ◽  
W. Smart ◽  
T. J. Della Bosca ◽  
...  
Keyword(s):  
Slow Growth ◽  
Carcass Characteristics ◽  
Fast Growth ◽  
Carcass Weight ◽  
Breeding Value ◽  
Growth Path ◽  
High Quality ◽  
Fat Thickness ◽  
Estimated Breeding Value ◽  
The Imf

Growth, carcass characteristics and meat quality of the steer and heifer progeny of autumn (AC: March–April) and winter (WC: June–July) calving cows following weaning in January in each of 3 years (2003–05) were measured. The cows were mated to sires with a high estimated breeding value for either retail beef yield (RBY), intramuscular fat (IMF) or both RBY and IMF. After weaning, the progeny entered one of three growth paths until slaughter at an average steer liveweight of 500 kg: (i) fast – fast growth from weaning on a high concentrate feedlot diet; (ii) slow – slow growth from weaning (~0.6 kg/day) to 400 kg liveweight followed by growth at over 1 kg/day on high quality pasture; or (iii) comp. – 10% weaning weight loss, immediately after weaning followed by compensatory or rapid growth of over 1 kg/day on high quality pasture. Steers on the fast growth path had higher (P < 0.001) P8 fat thickness than those on the slow or comp. growth paths whereas heifers on the fast growth path only had higher (P < 0.001) P8 fat thickness than those on the slow growth path. Animals on the fast growth treatment had higher (P < 0.001) levels of IMF% than the slow animals which were higher (P < 0.001) than the comp. growth treatment. AUS-MEAT and US marbling scores were not different among growth paths. Animals finished on the fast growth path had a lower (P < 0.001) RBY% than those on either the slow or comp. growth paths. The RBY-sired progeny had higher (P < 0.001) finishing liveweight and hot standard carcass weight than either RBY and IMF or IMF-sired animals. IMF-sired progeny had higher (P < 0.01) rib fat thickness than either RBY or RBY- and IMF-sired animals. There was also a similar trend for P8 fat thickness but the effects were not significant. The RBY-sired animals had lower AUS-MEAT marbling scores (P < 0.01), US marbling scores (P < 0.001) and levels of IMF% (P < 0.01) than either of the other two sire treatments. RBY-sired animals also had higher (P < 0.001) estimated RBY% than those from the IMF sires while those by RBY and IMF sires were intermediate and not significantly different from either. Calving time had little influence on most carcass characteristics. However, WC animals tended to be fatter and have higher marbling scores than AC animals. The IMF% was higher (P < 0.01) in WC animals from RBY and IMF sires than in the corresponding AC animals. Heifers had lighter slaughter liveweight, carcass weight, were fatter and had higher marbling scores than steers. Heifers also had lower (P < 0.001) RBY% than the steers. Ossification scores for heifers were higher (P < 0.001) than for steers by ~30 units in AC calves and by 20 units in WC calves. The results of this experiment confirm the effectiveness of using sires with high estimated breeding value for the required characteristics in producing the desired improvements in the progeny. The absence of any interactions of sire type with growth path indicates that differences between sire types will be similar regardless of environmental conditions. Animals raised on a faster growth path after weaning produce carcasses with more fat and more IMF% than those grown on slower growth paths.

Sign in / Sign up

Export Citation Format

Share Document