54 Genetics Reloaded: Large-scale Collection of Novel Phenotypes in Turkey

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 29-30
Author(s):  
Christine F Baes ◽  
Nienke van Staaveren ◽  
Bayode Makanjuola ◽  
Emily M Leishman ◽  
Emhimad Abdalla ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Large Scale ◽  
Performance Testing ◽  
Reference Population ◽  
Carcass Composition ◽  
Random Regression ◽  
Genomic Information ◽  
Pedigree Data ◽  
High Quality ◽  
Breeding Programs

Abstract Effective application of genomic selection methodologies require genomic information, population-based pedigree data, and high-quality phenotypes. The implementation of such selection programs can increase the accuracy of breeding values, therefore improving the ability to estimate the genetic merit of livestock. In particular, traits with low heritability are amenable to genomic selection. The objective of this presentation is to outline improvement of traits such as livability, disease resistance, fertility, and other health and welfare traits in turkeys, which could considerably advance breeding programs. The aim of this study was to apply different methodologies (ssGBLUP, random regression approaches, etc.) to novel and conventional traits collected in commercial turkey lines (15,000 turkeys genotyped at 65K). This reference population was used to assess the increase in accuracy of selection using genomic information, which ranged from 0 to +0.3, depending on the trait. A further goal of the study was to monitor inbreeding within the different lines. A large number of high-quality phenotypes related to fertility, growth, production, and carcass composition were collected, as well as additional health and behaviour phenotypes related to livability. These traits are being developed for use in performance testing. Furthermore, meat quality (e.g. white striation, water holding capacity, pH, sheer force and colour, etc.) and total carcass composition phenotypes were analysed. With improved methodology, more detailed phenotypic information, and comprehensive data collection and integration, we present more accurate selection of parent stock for application in applied poultry breeding programs.

166 Livestock Resiliency: Concepts and Approaches

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 89-90
Author(s):  
Christine F Baes ◽  
Filippo Miglior ◽  
Flavio S Schenkel ◽  
Ellen Goddard ◽  
Gerrit Kistemaker ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Correlations ◽  
Performance Testing ◽  
Breeding Value ◽  
Estimation Methods ◽  
Genome Wide Association Studies ◽  
Breeding Programs ◽  
Fertility Traits

Abstract Genetic improvement of health, welfare, efficiency, and fertility traits is challenging due to expensive and fuzzy phenotypes, the polygenic nature of traits, antagonistic genetic correlations to production traits and low heritabilities. Nevertheless, many organizations have introduced large-scale genetic evaluations for such traits in routine selection indexes. Medium and high-density arrays can be applied in genomic selection strategies to improve breeding value accuracy, and also in genome-wide association studies (GWAS) to identify causative mutations responsible for economically important traits. Genomic information is particularly helpful when traits have low heritability. The objective here is to provide a framework for including health, welfare, efficiency, and fertility traits taken from large-scale genetic and genomic analyses and identifying areas of potential improvement in terms of trait definition and performance testing. General tendencies between trait groups confirmed that a number of moderate unfavourable correlations (+/-0.20 or higher) exist between economically important trait complexes and health, welfare, and fertility traits. A number of trait complexes were identified in which “closer-to-biology” phenotypes could provide clear improvements to routine genetic and genomic selection programs. Here we outline development of these phenotypes and describe their collection. While conventional variance component estimation methods have underpinned the genomic component of some traits of economic interest, performance testing for health, welfare, efficiency, and fertility traits remains an elusive goal for breeding programs. Although our results are encouraging, there is much to be done in terms of trait definition and obtaining better measures of physiological parameters for wide-scale application in breeding programs. Close collaboration between veterinarians, physiologists, and geneticists is necessary to attain meaningful advancement in such areas. We would like to acknowledge the support and funding from all national and international partners involved in the RDGP project through the Large Scale Applied Research Project program from Genome Canada

scholarly journals Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program

2020 ◽  
Author(s):  
Sikiru Adeniyi Atanda ◽  
Michael Olsen ◽  
Juan Burgueño ◽  
Jose Crossa ◽  
Daniel Dzidzienyo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Primary Objective ◽  
Breeding Program ◽  
Breeding Cycle ◽  
Training Set ◽  
Maize Breeding ◽  
Phenotypic Data ◽  
Breeding Programs

Abstract Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. Abstract The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

scholarly journals Genome-wide association study and low-cost genomic predictions for growth and fillet yield in Nile tilapia (Oreochromis niloticus)

2019 ◽  
Author(s):  
Grazyella M. Yoshida ◽  
Jean P. Lhorente ◽  
Katharina Correa ◽  
Jose Soto ◽  
Diego Salas ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Nile Tilapia ◽  
Cost Effective ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Genomic Information ◽  
Breeding Programs ◽  
Genome Wide ◽  
Fillet Yield ◽  
Genomic Predictions

ABSTRACTFillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in tilapia breeding programs.

scholarly journals 295 Parameter estimation under genomic selection

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 31-32
Author(s):  
Ignacy Misztal
Keyword(s):  
Parameter Estimation ◽  
Genomic Selection ◽  
Sparse Matrix ◽  
Parameters Estimation ◽  
Random Regression ◽  
Genomic Relationship Matrix ◽  
Correlated Response ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Over Time

Abstract Genetic parameters are important in animal breeding for many tasks, including as input to a model for genetic evaluation, to estimate genetic gain due to selection, and to estimate correlated response due to selection on major traits. Before the genomic era, parameter estimation was facilitated by sparse structure of mixed model equations. Methods such as AI REML with sparse matrix inversion or MCMC via Gibbs sampling could estimate parameters for populations exceeding 1 million animals. With genomic selection (GS) and single-step GBLUP, the genomic matrices are mostly dense, and costs of parameter estimation increased dramatically. The estimation with 20K genotyped animals can take many days. Details in matching pedigree and genomic information influence estimated parameters. Estimation without the genomic information when GS is practiced leads to biases due to genomic-preselection. Truncating data to too few generations or to only genotyped animals leads to additional biases by excluding data on which the selection was practiced. Current studies indicate strong declines in heritability due to GS. Regular models for parameter estimation compute parameters only for the base population. Models that trace changes of parameters over time, such as random regression model on year of birth or a multiple trait model treating times slices as separate traits, are very expensive. A good compromise in parameter estimation under GS is to use slices of only 2–3 generations, with genotypes of young animals removed. When complete populations are genotyped, estimations with large number of genotyped animals are possible either with a SNP model or with GBLUP (inversion of genomic relationship matrix by APY algorithm). For simple models, Method R can provide estimates for any data size. An indirect indication of changing parameters over time is reduced predictivity or lower genetic trend despite increased data. Parameter estimation in GS would benefit from new, efficient tools.

scholarly journals Genetic parameters and genomic prediction for feed intake recorded at the group and individual level in different production systems for growing pigs

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Hongding Gao ◽  
Guosheng Su ◽  
Just Jensen ◽  
Per Madsen ◽  
Ole F. Christensen ◽  
...  
Keyword(s):  
Feed Intake ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Genetic Correlations ◽  
Growing Pigs ◽  
Test Period ◽  
Random Regression ◽  
Group Level ◽  
Breeding Programs ◽  
Individual Level

Abstract Background In breeding programs, recording large-scale feed intake (FI) data routinely at the individual level is costly and difficult compared with other production traits. An alternative approach could be to record FI at the group level since animals such as pigs are normally housed in groups and fed by a shared feeder. However, to date there have been few investigations about the difference between group- and individual-level FI recorded in different environments. We hypothesized that group- and individual-level FI are genetically correlated but different traits. This study, based on the experiment undertaken in purebred DanBred Landrace (L) boars, was set out to estimate the genetic variances and correlations between group- and individual-level FI using a bivariate random regression model, and to examine to what extent prediction accuracy can be improved by adding information of individual-level FI to group-level FI for animals recorded in groups. For both bivariate and univariate models, single-step genomic best linear unbiased prediction (ssGBLUP) and pedigree-based BLUP (PBLUP) were implemented and compared. Results The variance components from group-level records and from individual-level records were similar. Heritabilities estimated from group-level FI were lower than those from individual-level FI over the test period. The estimated genetic correlations between group- and individual-level FI based on each test day were on average equal to 0.32 (SD = 0.07), and the estimated genetic correlation for the whole test period was equal to 0.23. Our results demonstrate that by adding information from individual-level FI records to group-level FI records, prediction accuracy increased by 0.018 and 0.032 compared with using group-level FI records only (bivariate vs. univariate model) for PBLUP and ssGBLUP, respectively. Conclusions Based on the current dataset, our findings support the hypothesis that group- and individual-level FI are different traits. Thus, the differences in FI traits under these two feeding systems need to be taken into consideration in pig breeding programs. Overall, adding information from individual records can improve prediction accuracy for animals with group records.

scholarly journals Optimized grouping to increase accuracy of prediction of breeding values based on group records in genomic selection breeding programs

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Thinh T. Chu ◽  
John W. M. Bastiaansen ◽  
Peer Berg ◽  
Hans Komen
Keyword(s):  
Genomic Selection ◽  
Egg Production ◽  
Genomic Information ◽  
Breeding Programs ◽  
Breeding Values ◽  
Supervised Clustering ◽  
Phenotypic Group ◽  
Group Members ◽  
Accuracy Of Prediction ◽  
Selection Breeding

Abstract Background Phenotypic records of group means or group sums are a good alternative to individual records for some difficult to measure, but economically important traits such as feed efficiency or egg production. Accuracy of predicted breeding values based on group records increases with increasing relationships between group members. The classical way to form groups with more closely-related animals is based on pedigree information. When genotyping information is available before phenotyping, its use to form groups may further increase the accuracy of prediction from group records. This study analyzed two grouping methods based on genomic information: (1) unsupervised clustering implemented in the STRUCTURE software and (2) supervised clustering that models genomic relationships. Results Using genomic best linear unbiased prediction (GBLUP) models, estimates of the genetic variance based on group records were consistent with those based on individual records. When genomic information was available to constitute the groups, genomic relationship coefficients between group members were higher than when random grouping of paternal half-sibs and of full-sibs was applied. Grouping methods that are based on genomic information resulted in higher accuracy of genomic estimated breeding values (GEBV) prediction compared to random grouping. The increase was ~ 1.5% for full-sibs and ~ 11.5% for paternal half-sibs. In addition, grouping methods that are based on genomic information led to lower coancestry coefficients between the top animals ranked by GEBV. Of the two proposed methods, supervised clustering was superior in terms of accuracy, computation requirements and applicability. By adding surplus genotyped offspring (more genotyped offspring than required to fill the groups), the advantage of supervised clustering increased by up to 4.5% compared to random grouping of full-sibs, and by 14.7% compared to random grouping of paternal half-sibs. This advantage also increased with increasing family sizes or decreasing genome sizes. Conclusions The use of genotyping information for grouping animals increases the accuracy of selection when phenotypic group records are used in genomic selection breeding programs.

Impact of young ewe fertility rate on risk and genetic gain in sheep-breeding programs using genomic selection

2017 ◽  
Vol 57 (8) ◽  
pp. 1653
Author(s):  
J. E. Newton ◽  
D. J. Brown ◽  
S. Dominik ◽  
J. H. J. van der Werf
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Fertility Rate ◽  
Breeding Program ◽  
Management Decision ◽  
Fertility Level ◽  
Fertility Rates ◽  
Genomic Information ◽  
Breeding Programs ◽  
Sheep Breeding

Genomic selection could be useful in sheep-breeding programs, especially if rams and ewes are first mated at an earlier age than is the current industry practice. However, young-ewe (1 year old) fertility rates are known to be lower and more variable than those of mature ewes. The aim of the present study was to evaluate how young-ewe fertility rate affects risk and expected genetic gain in Australian sheep-breeding programs that use genomic information and select ewes and rams at different ages. The study used stochastic simulation to model different flock age structures and young-ewe fertility levels with and without genomic information for Merino and maternal sheep-breeding programs. The results from 10 years of selection were used to compare breeding programs on the basis of the mean and variation in genetic gain. Ram and ewe age, availability of genomic information on males and young-ewe fertility level all significantly (P < 0.05) affected expected genetic gain. Higher young-ewe fertility rates significantly increased expected genetic gain. Low fertility rate of young ewes (10%) resulted in net genetic gain similar to not selecting ewes until they were 19 months old and did not increase breeding-program risk, as the likelihood of genetic gain being lower than the range of possible solutions from a breeding program with late selection of both sexes was zero. Genomic information was of significantly (P < 0.05) more value for 1-year-old rams than for 2-year-old rams. Unless genomic information was available, early mating of rams offered no greater gain in Merino breeding programs and increased breeding-program risk. It is concluded that genomic information decreases the risk associated with selecting replacements at 7 months of age. Genetic progress is unlikely to be adversely affected if fertility levels above 10% can be achieved. Whether the joining of young ewes is a viable management decision for a breeder will depend on the fertility level that can be achieved in their young ewes and on other costs associated with the early mating of ewes.

Tiered Sampling

2021 ◽  
Vol 15 (5) ◽  
pp. 1-52
Author(s):  
Lorenzo De Stefani ◽  
Erisa Terolli ◽  
Eli Upfal
Keyword(s):  
Large Scale ◽  
Analysis Of Algorithms ◽  
Base Layer ◽  
Single Edge ◽  
Real World Data ◽  
High Quality ◽  
Large Graphs ◽  
Massive Graphs ◽  
Variance Estimate ◽  
Low Probability

We introduce Tiered Sampling , a novel technique for estimating the count of sparse motifs in massive graphs whose edges are observed in a stream. Our technique requires only a single pass on the data and uses a memory of fixed size M , which can be magnitudes smaller than the number of edges. Our methods address the challenging task of counting sparse motifs—sub-graph patterns—that have a low probability of appearing in a sample of M edges in the graph, which is the maximum amount of data available to the algorithms in each step. To obtain an unbiased and low variance estimate of the count, we partition the available memory into tiers (layers) of reservoir samples. While the base layer is a standard reservoir sample of edges, other layers are reservoir samples of sub-structures of the desired motif. By storing more frequent sub-structures of the motif, we increase the probability of detecting an occurrence of the sparse motif we are counting, thus decreasing the variance and error of the estimate. While we focus on the designing and analysis of algorithms for counting 4-cliques, we present a method which allows generalizing Tiered Sampling to obtain high-quality estimates for the number of occurrence of any sub-graph of interest, while reducing the analysis effort due to specific properties of the pattern of interest. We present a complete analytical analysis and extensive experimental evaluation of our proposed method using both synthetic and real-world data. Our results demonstrate the advantage of our method in obtaining high-quality approximations for the number of 4 and 5-cliques for large graphs using a very limited amount of memory, significantly outperforming the single edge sample approach for counting sparse motifs in large scale graphs.

scholarly journals Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 210
Author(s):  
Sang V. Vu ◽  
Cedric Gondro ◽  
Ngoc T. H. Nguyen ◽  
Arthur R. Gilmour ◽  
Rick Tearle ◽  
...  
Keyword(s):  
Moisture Content ◽  
Genomic Selection ◽  
Shell Length ◽  
Prediction Accuracy ◽  
Genomic Information ◽  
Morphometric Traits ◽  
Shell Weight ◽  
Shell Width ◽  
Crassostrea Angulata ◽  
Selection For

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

scholarly journals Emerging issues in genomic selection

2021 ◽  
Author(s):  
I Misztal ◽  
I Aguilar ◽  
D Lourenco ◽  
L Ma ◽  
J Steibel ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Sparse Matrices ◽  
Large Datasets ◽  
Single Step ◽  
Genomic Information ◽  
Long Term Effects ◽  
Additive Variance ◽  
Recent Developments ◽  
Emerging Issues ◽  
Using Data

Abstract Genomic selection is now practiced successfully across many species. However, many questions remain such as long-term effects, estimations of genomic parameters, robustness of GWAS with small and large datasets, and stability of genomic predictions. This study summarizes presentations from at the 2020 ASAS symposium. The focus of many studies until now is on linkage disequilibrium (LD) between two loci. Ignoring higher level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWAS studies using small genomic datasets frequently find many marker-trait associations whereas studies using much bigger datasets find only a few. Most current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit computation of p-values from GBLUP, where models can be arbitrarily complex but restricted to genotyped animals only, and to single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as one SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. While many issues in genomic selection have been solved, many new issues that require additional research continue to surface.

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