scholarly journals Theoretical Evaluation of Multi-Breed Genomic Prediction in Chinese Indigenous Cattle

Animals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 789 ◽  
Author(s):  
Xu ◽  
Wang ◽  
Zhu ◽  
Liu ◽  
Li ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Relationships ◽  
Simulated Data ◽  
Indigenous Populations ◽  
Farm Animals ◽  
Small Populations ◽  
Theoretical Evaluation ◽  
Indigenous Cattle ◽  
Multiple Populations ◽  
Genomic Predictions

Genomic selection (GS) has been widely considered as a valuable strategy for enhancing the rate of genetic gain in farm animals. However, the construction of a large reference population is a big challenge for small populations like indigenous cattle. In order to evaluate the potential application of GS for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. Also, we examined the effect of different genetic architecture on prediction accuracy. In this study, we simulated a set of genotype data by a resampling approach which can reflect the realistic linkage disequilibrium pattern for multiple populations. We found within-breed evaluations yielded the highest accuracies ranged from 0.64 to 0.68 for four different simulated genetic architectures. For scenarios using multiple breeds as reference, the predictive accuracies were higher when the reference was comprised of breeds with a close relationship, while the accuracies were low when prediction were carried out among breeds. In addition, the accuracy increased in all scenarios with the heritability increased. Our results suggested that using meta-population as reference can increase accuracy of genomic predictions for small populations. Moreover, multi-breed genomic selection was feasible for Chinese indigenous populations with genetic relationships.

scholarly journals Reliability of Genomic Predictions Across Multiple Populations

Genetics ◽  
2009 ◽  
Vol 183 (4) ◽  
pp. 1545-1553 ◽  
Author(s):  
A. P. W. de Roos ◽  
B. J. Hayes ◽  
M. E. Goddard
Keyword(s):  
Multiple Populations ◽  
Genomic Predictions

scholarly journals 330 A hybrid model for genomic selection using prioritized SNPs based on FST scores in the presence of non-genotyped animals

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 51-51
Author(s):  
Sajjad Toghiani ◽  
Ling-Yun Chang ◽  
El H Hay ◽  
Andrew J Roberts ◽  
Samuel E Aggrey ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Simulated Data ◽  
Snp Markers ◽  
Genomic Relationship Matrix ◽  
Polygenic Effect ◽  
Relationship Matrix ◽  
Continuous Increase ◽  
Missing Genotypes

Abstract The dramatic advancement in genotyping technology has greatly reduced the complexity and cost of genotyping. The continuous increase in the density of marker panels is resulting in little to no improvement in the accuracy of genomic selection. Direct inversion of the genomic relationship matrix is infeasible for some livestock populations due to the excessive computational cost. In addition, most animals in genetic evaluation programs are non-genotyped. Including these animals in a genomic evaluation requires the imputation of the missing genotypes when using regression methods. To overcome these challenges, a hybrid approach is proposed. This approach fits a subset of SNP markers selected based on FST scores and a classical polygenic effect. The method was first tested using only genotyped animals and then extended to accommodate non-genotyped animals. The proposed approach was evaluated using simulated data for a trait with heritability of 0.1 and 0.4 and weaning weight in a crossbred beef cattle population. When all animals were genotyped, the hybrid approach using only 2.5% of prioritized SNPs exceeded the prediction accuracies of BayesB, BayesC, and GBLUP by more than 7%. When non-genotyped animals were incorporated, the proposed approach significantly outperformed ss-GBLUP method in terms of prediction accuracy under both simulated heritability scenarios. Although the results seem to depend on the genetic complexity of the trait, the proposed approach resulted in higher prediction accuracies than current methods. Furthermore, its computational costs in terms of CPU time and peak memory are substantially lower than the current methods.

scholarly journals Beef cattle breeding in Australia with genomics: opportunities and needs

2012 ◽  
Vol 52 (3) ◽  
pp. 100 ◽  
Author(s):  
D. J. Johnston ◽  
B. Tier ◽  
H.-U. Graser
Keyword(s):  
Beef Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Information Needs ◽  
Genomic Information ◽  
Female Reproduction ◽  
Genetic Progress ◽  
Cattle Breeding ◽  
Phenotypic Data ◽  
Genomic Predictions

Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.

Genomic selection for female reproduction in Australian tropically adapted beef cattle

2014 ◽  
Vol 54 (1) ◽  
pp. 16 ◽  
Author(s):  
Y. D. Zhang ◽  
D. J. Johnston ◽  
S. Bolormaa ◽  
R. J. Hawken ◽  
B. Tier
Keyword(s):  
Genomic Selection ◽  
Cross Validation ◽  
Population Data ◽  
Northern Australia ◽  
Female Reproduction ◽  
Genetic Complexity ◽  
Reproduction Traits ◽  
Experimental Populations ◽  
Snp Panels ◽  
Genomic Predictions

The usefulness of genomic selection was assessed for female reproduction in tropically adapted breeds in northern Australia. Records from experimental populations of Brahman (996) and Tropical Composite (1097) cattle that had had six calving opportunities were used to derive genomic predictions for several measures of female fertility. These measures included age at first corpus luteum (AGECL), at first calving and subsequent postpartum anoestrous interval and measures of early and lifetime numbers of calves born or weaned. In a second population, data on pregnancy and following status (anoestrous or pregnancy) were collected from 27 commercial herds from northern Australia to validate genomic predictions. Cows were genotyped with a variety of single nucleotide polymorphism (SNP) panels and, where necessary, genotypes imputed to the highest density (729 068 SNPs). Genetic parameters of subsets of the complete data were estimated. These subsets were used to validate genomic predictions using genomic best linear unbiased prediction using both univariate cross-validation and bivariate analyses. Estimated heritability ranged from 0.56 for AGECL to 0.03 for lifetime average calving rate in the experimental cows, and from 0.09 to 0.25 for early life reproduction traits in the commercial cows. Accuracies of predictions were generally low, reflecting the limited number of data in the experimental populations. For AGECL and postpartum anoestrous interval, the highest accuracy was 0.35 for experimental Brahman cows using five-fold univariate cross-validation. Greater genetic complexity in the Tropical Composite cows resulted in the corresponding accuracy of 0.23 for AGECL. Similar level of accuracies (from univariate and bivariate analyses) were found for some of the early measures of female reproduction in commercial cows, indicating that there is potential for genomic selection but it is limited by the number of animals with phenotypes.

scholarly journals Current state and problems of poultry breeding in Russia (review)

2020 ◽  
Vol 21 (3) ◽  
pp. 217-232
Author(s):  
E. S. Fedorova ◽  
O. I. Stanishevskaya ◽  
N. V. Dementieva
Keyword(s):  
Genomic Selection ◽  
Global Scale ◽  
Farm Animals ◽  
Breeding Value ◽  
Main Task ◽  
Homogeneous Population ◽  
Snp Data ◽  
Poultry Breeding ◽  
Genetic Value ◽  
High Level

Modern poultry breeding in Russia is one of the fastest growing sectors of agriculture, but the prosperity of the industry is almost entirely dependent on supplies of breeding material from abroad. Russia practically has no its own breeding base in both egg- and meat-type commercial crosses of chickens. Most of the domestic commercial crosses that had occupied leading positions in Russian poultry breeding have been lost now. More than 90 % of commercial lines of breeding stocks in Russia are imported. Foreign poultry breeding companies merge into transnational holdings engaged in multi-species breeding of farm animals, which allows them not to depend on market conditions in the industry. The reverse side of such a consolidation on a global scale is a decrease in the genetic diversity of poultry and a high level of inbreeding in commercial chicken lines. In these circumstances, there is a real biological danger for the preservation of these lines due to the potential susceptibility of “monocultures” to new diseases, which can eliminate the genetically homogeneous population. Any selection system is based on an assessment of the breeding value of potential parents. Its purpose is to obtain, as far as possible, the most accurate forecast of the genetic value of an individual and the productive qualities of its progeny. These requirements are optimally met by the BLUP methodology, in which molecular genetics (SNP) data can be successfully integrated, which allows supplementing the statistical analysis with genomic selection technologies. This is especially true for traits that cannot be measured, or can only be measured in one sex, or only at the end of the productive period. The inclusion of genomic selection methods in breeding programs makes it possible significantly increase the selection efficiency for the main economical traits of chickens. The main task in the Russian breeding poultry industry is the creation of its own competitive breeding base, not inferior to Western commercial crosses in terms of productivity. To create poultry breeding centers and grandparents/parents-breeding farms the state support is needed. It is also necessary to develop and implement innovative methods in the field of genomic selection, as well as software and information systems and specialized selection computer programs for processing and analyzing meta-data.

scholarly journals Blood protein polymorphism and differentiation of selected indigenous cattle breeds in Nigeria

2020 ◽  
Vol 44 (4) ◽  
pp. 11-18
Author(s):  
A. D. Oladepo ◽  
A. E. Salako
Keyword(s):  
Genetic Distance ◽  
Genetic Variants ◽  
Standard Procedure ◽  
Population Characteristics ◽  
Protein Polymorphism ◽  
Farm Animals ◽  
Blood Protein ◽  
Cellulose Acetate Electrophoresis ◽  
Indigenous Cattle ◽  
Cattle Breeds

Genetic variation is the basis of effective improvement in farm animals. Population differentiation is used for objective choice of parental genotypes that constitutes new hybrids in crossbreeding. In Nigeria, population characteristics of selected indigenous cattle breeds have not been fully documented. Therefore, blood protein electrophoretic patterns of selected indigenous cattle breeds in Nigeria were assessed. Blood samples (5mL) were taken underneath the tail by venipuncture from 40 cattle randomly selected from each of the five breeds. The samples were subjected to cellulose acetate electrophoresis to determine the genetic variants of haemoglobin (Hb), carbonic anhydrase (CA) and transferrin (Tf) following standard procedure. Data were analysed using descriptive statistics, cluster analysis and Euclidean genetic distance. Allele frequencies ranged between 0.10 (Hb ) and 0.90 (Hb ), 0.11 (CA ) and 0.89 (CA ) and 0.02 (Tf ) and 0.49 (Tf ) across the breed.Two main clusters from the dendrogram were observed for each of Hb, CA and Tf. Euclidean genetic distance at the blood protein polymorphism level between WF and SG, WF and RB, WF and BK, WF and Muturu were 29, 30, 80 and 93, respectively. Genetic variants of transferring were largest within breed which indicated potential for selection

Comparison of combined, crossbred, and purebred reference populations for genomic selection in small populations

2020 ◽  
Vol 190 ◽  
pp. 106171
Author(s):  
Saeideh Hosseini ◽  
Saheb Foroutanifar ◽  
Alireza Abdolmohammadi
Keyword(s):  
Genomic Selection ◽  
Small Populations

Accuracy of genomic prediction using mixed low-density marker panels

2020 ◽  
Vol 60 (8) ◽  
pp. 999
Author(s):  
Lianjie Hou ◽  
Wenshuai Liang ◽  
Guli Xu ◽  
Bo Huang ◽  
Xiquan Zhang ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Imputation Accuracy ◽  
Simulated Data ◽  
Real Data ◽  
Substantial Effect ◽  
Low Density ◽  
Snp Panel ◽  
Snp Panels ◽  
The Cost ◽  
Better Than

Low-density single-nucleotide polymorphism (LD-SNP) panel is one effective way to reduce the cost of genomic selection in animal breeding. The present study proposes a new type of LD-SNP panel called mixed low-density (MLD) panel, which considers SNPs with a substantial effect estimated by Bayes method B (BayesB) from many traits and evenly spaced distribution simultaneously. Simulated and real data were used to compare the imputation accuracy and genomic-selection accuracy of two types of LD-SNP panels. The result of genotyping imputation for simulated data showed that the number of quantitative trait loci (QTL) had limited influence on the imputation accuracy only for MLD panels. Evenly spaced (ELD) panel was not affected by QTL. For real data, ELD performed slightly better than did MLD when panel contained 500 and 1000 SNP. However, this advantage vanished quickly as the density increased. The result of genomic selection for simulated data using BayesB showed that MLD performed much better than did ELD when QTL was 100. For real data, MLD also outperformed ELD in growth and carcass traits when using BayesB. In conclusion, the MLD strategy is superior to ELD in genomic selection under most situations.

scholarly journals Changes in genetic parameters for fitness and growth traits in pigs under genomic selection

2020 ◽  
Vol 98 (2) ◽  
Author(s):  
Jorge Hidalgo ◽  
Shogo Tsuruta ◽  
Daniela Lourenco ◽  
Yutaka Masuda ◽  
Yijian Huang ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Genetic Relationships ◽  
Genetic Correlations ◽  
Genomic Analysis ◽  
Data Set ◽  
Genetic Changes ◽  
Fitness Traits ◽  
Bayesian Implementation

Abstract Genomic selection increases accuracy and decreases generation interval, speeding up genetic changes in the populations. However, intensive changes caused by selection can reduce the genetic variation and can strengthen undesirable genetic correlations. The purpose of this study was to investigate changes in genetic parameters for fitness traits related with prolificacy (FT1) and litter survival (FT2 and FT3), and for growth (GT1 and GT2) traits in pigs over time. The data set contained 21,269 (FT1), 23,246 (FT2), 23,246 (FT3), 150,492 (GT1), and 150,493 (GT2) phenotypic records obtained from 2009 to 2018. The pedigree file included 369,776 animals born between 2001 and 2018, of which 39,103 were genotyped. Genetic parameters were estimated with bivariate models (FT1-GT1, FT1-GT2, FT2-GT1, FT2-GT2, FT3-GT1, and FT3-GT2) using 3-yr sliding subsets. With a Bayesian implementation using the GIBBS3F90 program computations were performed as genomic analysis (GEN) or pedigree-based analysis (PED), that is, with or without genotypes, respectively. For GEN (PED), the changes in heritability from the first to the last year interval, that is, from 2009–2011 to 2015–2018 were 8.6 to 5.6 (7.9 to 8.8) for FT1, 7.8 to 7.2 (7.7 to 10.8) for FT2, 11.4 to 7.6 (10.1 to 7.5) for FT3, 35.1 to 16.5 (32.5 to 23.7) for GT1, and 35.9 to 16.5 (32.6 to 24.1) for GT2. Differences were also observed for genetic correlations as they changed from −0.31 to −0.58 (−0.28 to −0.73) for FT1-GT1, −0.32 to −0.50 (−0.29 to −0.74) for FT1-GT2, −0.27 to −0.45 (−0.30 to −0.65) for FT2-GT1, −0.28 to −0.45 (−0.32 to −0.66) for FT2-GT2, 0.14 to 0.17 (0.11 to 0.04) for FT3-GT1, and 0.14 to 0.18 (0.11 to 0.05) for FT3-GT2. Strong selection in pigs reduced heritabilities and emphasized the antagonistic genetic relationships between fitness and growth traits. With genotypes considered, heritability estimates were smaller and genetic correlations were greater than estimates with only pedigree and phenotypes. When selection is based on genomic information, genetic parameters estimated without this information can be biased because preselection is not accounted for by the model.

Sign in / Sign up

Export Citation Format

Share Document