scholarly journals The Accuracy of Genomic Selection in Norwegian Red Cattle Assessed by Cross-Validation

Genetics ◽  
2009 ◽  
Vol 183 (3) ◽  
pp. 1119-1126 ◽  
Author(s):  
Tu Luan ◽  
John A. Woolliams ◽  
Sigbjørn Lien ◽  
Matthew Kent ◽  
Morten Svendsen ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Cross Validation ◽  
Strong Relationship ◽  
Breeding Value ◽  
Production Traits ◽  
Phenotypic Data ◽  
Mixture Model Approach ◽  
High Heritability ◽  
Best Linear Unbiased ◽  
Similar Accuracy

Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed.

scholarly journals Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

Animals ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 136
Author(s):  
Menghua Zhang ◽  
Hanpeng Luo ◽  
Lei Xu ◽  
Yuangang Shi ◽  
Jinghang Zhou ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Bayesian Methods ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Linear Unbiased Prediction ◽  
Brown Cattle ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Better Than

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

scholarly journals Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens

Animals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 419
Author(s):  
Jin Zhang ◽  
Jie Wang ◽  
Qinghe Li ◽  
Qiao Wang ◽  
Jie Wen ◽  
...  
Keyword(s):  
Antibody Response ◽  
Best Linear Unbiased Prediction ◽  
Spleen Weight ◽  
Poultry Production ◽  
Phenotypic Data ◽  
Linear Unbiased Prediction ◽  
High Heritability ◽  
Best Linear Unbiased ◽  
Immune Traits ◽  
Unbiased Prediction

Poultry diseases pose a large threat to poultry production. Selection to improve immune traits is a feasible way to prevent and control avian diseases. The objective of this study was to investigate the efficiency of estimation of genetic parameters for antibody response to avian influenza virus (Ab-AIV), antibody response to Newcastle disease virus (Ab-NDV), sheep red blood cell antibody titer (SRBC), the ratio of heterophils to lymphocytes (H/L), immunoglobulin G (IgG), the spleen immune index (SII), thymus immune index (TII), thymus weight at 100 d (TW) and the spleen weight at 100 d (SW) in Beijing oil chickens, by using the best linear unbiased prediction (BLUP) method and genomic best linear unbiased prediction (GBLUP) method. The phenotypic data used in the two methods were the same and were from 519 individuals. With the BLUP model, Ab-AIV, Ab-NDV, SRBC, H/L, IgG, TII, and TW had low heritability ranging from 0.000 to 0.281, whereas SII and SW had high heritability of 0.631 and 0.573. With the GBLUP model, all individuals were genotyped with Illumina 60K SNP chips, and Ab-AIV, Ab-NDV, SRBC, H/L and IgG had low heritability ranging from 0.000 to 0.266, whereas SII, TII, TW and SW had moderate heritability ranging from 0.300 to 0.472. We compared the prediction accuracy obtained from BLUP and GBLUP through 50 time 5-fold cross-validation (CV), and the results indicated that BLUP provided a slightly higher accuracy of prediction than GBLUP in this population.

scholarly journals The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

2020 ◽  
Vol 33 (3) ◽  
pp. 382-389 ◽  
Author(s):  
Yun-Mi Lee ◽  
Chang-Gwon Dang ◽  
Mohammad Z. Alam ◽  
You-Sam Kim ◽  
Kwang-Hyeon Cho ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Milk Production ◽  
Genetic Gain ◽  
Milk Fat ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Data Set ◽  
Selection For ◽  
Estimated Breeding Value

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population.Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction.Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L<sub>SB</sub>) and that for the production of cows (L<sub>SC</sub>) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L<sub>DB</sub>) and cows (L<sub>DC</sub>) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records.Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

scholarly journals Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 479 ◽  
Author(s):  
Larkin ◽  
Lozada ◽  
Mason
Keyword(s):  
Genomic Selection ◽  
Wheat Yield ◽  
Wheat Breeding ◽  
Breeding Value ◽  
Selection Marker ◽  
Successful Implementation ◽  
Head Blight ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
A Genome

In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs.

scholarly journals Application of genomic selection in agriculture

2018 ◽  
Vol 33 (4) ◽  
Author(s):  
Neeraj Budhlakoti ◽  
D. C. Mishra ◽  
Devendra Arora ◽  
Rajeev Ranjan Kumar
Keyword(s):  
Genomic Selection ◽  
Marker Assisted Selection ◽  
Genetic Improvement ◽  
Breeding Value ◽  
Slow Time ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Sequencing Technologies ◽  
Breeding Technique ◽  
Density Marker

Traditional breeding technique for genetic improvement of crops are based on, information on phenotypes and pedigrees to predict breeding values, has been found very successful. But, genetic gain through this technique is found to be very slow, time consuming. However, Due to availability of latest DNA sequencing technologies, now it is possible to estimate breeding values more accurately by using information on variation in DNA sequence. Lots of research has been done in direction of marker assisted selection, still it has some limitation on its implementation. Genomic selection (GS) is proposed to overcome such limitation. GS is a form of marker-assisted selection in which genetic markers covering the whole genome are used. GS predicts breeding value using information available on phenotype and high density marker. Several techniques has been developed for selection and prediction of genotype, these techniques are based on analysis of genotypic and phenotypic data.

scholarly journals Genomic selection through single-step genomic best linear unbiased prediction improves the accuracy of evaluation in Hanwoo cattle

2020 ◽  
Vol 33 (10) ◽  
pp. 1544-1557
Author(s):  
Mi Na Park ◽  
Mahboob Alam ◽  
Sidong Kim ◽  
Byoungho Park ◽  
Seung Hwan Lee ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Population Level ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Breeding Value ◽  
Eye Muscle ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Increase In Accuracy ◽  
Unbiased Prediction

Objective: Genomic selection (GS) is becoming popular in animals’ genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method.Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson’s correlation of genomic estimated breeding value (GEBV) with EBV of all animals (rM1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (rM2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls).Results: The rM1 estimates in the study were between 0.90 and 0.96 among five traits. The rM1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average rM2 estimates were much smaller than rM1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, rM2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of rM2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the rM2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based rM2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%).Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo provenbull evaluation program.

EFFICIENCY AND COMPARISON OF THE TEST-DAY MODEL AND BLUP METHOD MODIFICATIONS FOR ASSESSING BULLS BREEDING VALUE IN SIMMENTAL CATTLE BREED

2020 ◽  
Author(s):  
А.И. МАМОНТОВА ◽  
С.А. НИКИТИН ◽  
Е.Е. МЕЛЬНИКОВА ◽  
А.А. СЕРМЯГИН
Keyword(s):  
Animal Model ◽  
Milk Yield ◽  
Cattle Breed ◽  
Breeding Value ◽  
Am1 Method ◽  
Production Traits ◽  
Genetic Trend ◽  
Sire Model ◽  
The Russian Federation ◽  
Simmental Cattle

Целью проведенных исследований являлась отработка и адаптация применения методик BLUP AM (Animal Model — модель животного) и TDM (Test-Day Model — модель тестового дня) для прогнозирования племенной ценности быков-производителей и оценки селекционно-генетических параметров на популяции скота симментальской породы четырех регионов РФ. Проведен сравнительный анализ указанных методов с более ранним методом BLUP SM (Sire Model — модель отца). Рассчитана племенная ценность быков и коров симментальской породы по признакам молочной продуктивности: удой за 305 дней, выход молочного жира, выход молочного белка. Анализ полученных средних значений достоверности оценок быков-производителей, рассчитанных на основе сопоставляемых методов, свидетельствует, что достоверность для признака «удой за 305 дней» при переходе от метода SM1 к AM1 увеличивается на 2,4%, а при переходе от SM1 к TDM1 — на 7,8%. Даны варианты генетического тренда по удою с использованием различных уравнений моделей расчета племенной ценности. На основании полученных данных можно сделать вывод о том, что модель тестового дня позволяет не только повысить точность оценок быков, но и более рельефно выявить их ранги, а также несколько уменьшить срок получения достоверных оценок производителей по качеству потомства по продуктивным признакам. The purpose of this research was to develop and adapt the application of BLUP AM (Animal Model) and TDM (Test-Day Model) methods for predicting the sires breeding value and evaluating genetic parameters for Simmental cattle population in four regions of the Russian Federation. A comparative analysis of these methods with the earlier BLUP SM (Sire Model) method is performed. The breeding value for sires and cows of Simmental breed was calculated by milk production traits: milk yield for 305 days; milk fatyield; milk proteinyield. The sires reliability of average breeding value calculated by different methods reveal that milk yield for 305 days when switching from the SM1 to AM1 method increases by 2.4%, and when switching from SM1 to TDM1 — by 7.8%.The variants of the genetic trend for milk yield are given using various equations of BLUP and TDM. Based on the obtained data, it can be concluded that the Test-Day model allows increasing the accuracy of bull’s evaluation and also more clearly identifying their ranks, as well as slightly reducing the time for obtaining reliable estimates of bulls by offspring for production traits.

scholarly journals Recent genetic advances on boar taint reduction as an alternative to castration: a review

2021 ◽  
Author(s):  
Darlene Ana Souza Duarte ◽  
Martine Schroyen ◽  
Rodrigo Reis Mota ◽  
Sylvie Vanderick ◽  
Nicolas Gengler
Keyword(s):  
Genetic Selection ◽  
Boar Taint ◽  
Production Traits ◽  
Fat Tissue ◽  
Reproduction Traits ◽  
Unpleasant Odor ◽  
High Heritability ◽  
Pig Meat ◽  
Or Genes ◽  
Synthesis And Degradation

AbstractBoar taint is an unpleasant odor in male pig meat, mainly caused by androstenone, skatole, and indole, which are deposited in the fat tissue. Piglet castration is the most common practice to prevent boar taint. However, castration is likely to be banished in a few years due to animal welfare concerns. Alternatives to castration, such as genetic selection, have been assessed. Androstenone and skatole have moderate to high heritability, which makes it feasible to select against these compounds. This review presents the latest results obtained on genetic selection against boar taint, on correlation with other traits, on differences in breeds, and on candidate genes related to boar taint. QTLs for androstenone and skatole have been reported mainly on chromosomes 6, 7, and 14. These chromosomes were reported to contain genes responsible for synthesis and degradation of androstenone and skatole. A myriad of work has been done to find markers or genes that can be used to select animals with lower boar taint. The selection against boar taint could decrease performance of some reproduction traits. However, a favorable response on production traits has been observed by selecting against boar taint. Selection results have shown that it is possible to reduce boar taint in few generations. In addition, modifications in diet and environment conditions could be associated with genetic selection to reduce boar taint. Nevertheless, costs to measure and select against boar taint should be rewarded with incentives from the market; otherwise, it would be difficult to implement genetic selection.

scholarly journals Genetic analysis of three maize husk traits by QTL mapping in a maize-teosinte population

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiaolei Zhang ◽  
Ming Lu ◽  
Aiai Xia ◽  
Tao Xu ◽  
Zhenhai Cui ◽  
...  
Keyword(s):  
Phenotypic Variation ◽  
Genetic Basis ◽  
Molecular Breeding ◽  
Best Linear Unbiased Predictor ◽  
Maize Line ◽  
Maize Varieties ◽  
Outer Covering ◽  
High Heritability ◽  
Best Linear Unbiased ◽  
Variation Explained

Abstract Background The maize husk consists of numerous leafy layers and plays vital roles in protecting the ear from pathogen infection and dehydration. Teosinte, the wild ancestor of maize, has about three layers of small husk outer covering the ear. Although several quantitative trait loci (QTL) underlying husk morphology variation have been reported, the genetic basis of husk traits between teosinte and maize remains unclear. Results A linkage population including 191 BC2F8 inbred lines generated from the maize line Mo17 and the teosinte line X26–4 was used to identify QTL associated with three husk traits: i.e., husk length (HL), husk width (HW) and the number of husk layers (HN). The best linear unbiased predictor (BLUP) depicted wide phenotypic variation and high heritability of all three traits. The HL exhibited greater correlation with HW than HN. A total of 4 QTLs were identified including 1, 1, 2, which are associated with HL, HW and HN, respectively. The proportion of phenotypic variation explained by these QTLs was 9.6, 8.9 and 8.1% for HL, HN and HW, respectively. Conclusions The QTLs identified in this study will pave a path to explore candidate genes regulating husk growth and development, and benefit the molecular breeding program based on molecular marker-assisted selection to cultivate maize varieties with an ideal husk morphology.

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.

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