Accuracy Of Predicted Breeding Values Research Articles

Developing breeding values for shedding using industry data from New Zealand sheep

ABSTRACT Changes in wool prices, labour costs and animal welfare have sparked interest in several new sheep traits. Shedding, the ability of moulting or seasonal wool loss in sheep, is one of them. Here, we aimed to develop breeding values for shedding using existing data from New Zealand flocks. We used breeder-collected shedding scores, 0 = ‘no shedding on any part of the body’ to 10 = ‘wool shed fully from all the body’, for Wiltshire and Wiltshire-composite lambs born between 2003 and 2022. After QC, 20,653 phenotypic records from 25,066 animals with pedigree information (393 sires and 7439 dams from 12 flocks) were included in the analyses. Genetic parameters and breeding values were obtained using a genetic additive model with pedigree data (BLUP model), assuming shedding scores were quantitative. The genetic model incorporated the following variables: date of birth, sex, birth and rearing rank, age of dam and contemporary group. We found a high heritability for lamb shedding, 0.54 (0.02), and estimated a low-to-moderate prediction accuracy of breeding values (ranging from 0.09 to 0.36) using forward-validation. Our results confirmed that shedding can be subject to selection and that pedigree-based breeding values for this trait can be a useful tool for breeders.

Expected values for the accuracy of predicted breeding values accounting for genetic differences between reference and target populations

BackgroundGenetic merit, or breeding values as referred to in livestock and crop breeding programs, is one of the keys to the successful selection of animals in commercial farming systems. The developments in statistical methods during the twentieth century and single nucleotide polymorphism (SNP) chip technologies in the twenty-first century have revolutionized agricultural production, by allowing highly accurate predictions of breeding values for selection candidates at a very early age. Nonetheless, for many breeding populations, realized accuracies of predicted breeding values (PBV) remain below the theoretical maximum, even when the reference population is sufficiently large, and SNPs included in the model are in sufficient linkage disequilibrium (LD) with the quantitative trait locus (QTL). This is particularly noticeable over generations, as we observe the so-called erosion of the effects of SNPs due to recombinations, accompanied by the erosion of the accuracy of prediction. While accurately quantifying the erosion at the individual SNP level is a difficult and unresolved task, quantifying the erosion of the accuracy of prediction is a more tractable problem. In this paper, we describe a method that uses the relationship between reference and target populations to calculate expected values for the accuracies of predicted breeding values for non-phenotyped individuals accounting for erosion. The accuracy of the expected values was evaluated through simulations, and a further evaluation was performed on real data.ResultsUsing simulations, we empirically confirmed that our expected values for the accuracy of PBV accounting for erosion were able to correctly determine the prediction accuracy of breeding values for non-phenotyped individuals. When comparing the expected to the realized accuracies of PBV with real data, only one out of the four traits evaluated presented accuracies that were significantly higher than the expected, approaching h2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{\ ext{h}}}^{2}}$$\\end{document}.ConclusionsWe defined an index of genetic correlation between reference and target populations, which summarizes the expected overall erosion due to differences in allele frequencies and LD patterns between populations. We used this correlation along with a trait’s heritability to derive expected values for the accuracy (R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}$$\\end{document}) of PBV accounting for the erosion, and demonstrated that our derived ER|erosion\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{E}}\\left[{\ ext{R}}|{\ ext{erosion}}\\right]$$\\end{document} is a reliable metric.

PSXII-3 Including Non-Additive Genetic Effects in Genomic Prediction and Estimation of Variance Components for Performance and Heat Stress Traits in Pigs

Abstract Non-additive genetic effects may have important roles in the phenotypic expression of performance and adaptation traits in livestock. Therefore, we aimed to evaluate the inclusion of non-additive genetic effects in genomic prediction models and variance component estimation of performance traits in a purebred pig population and heat tolerance indicators in a crossbred pig population. The first dataset consisted of 3,534 individuals with genotypes for 52,843 SNPs and five pre-adjusted phenotypes from a public database of a purebred pig line. Twelve models fitting or not dominance and/or epistasis effects (additive-by-additive, additive-by-dominance, and dominance-by-dominance) and inbreeding were used to estimate variance components. Prediction ability was assessed based on 10-fold cross-validation and the bias, dispersion, and accuracy estimates were computed based on the LR method. We also evaluated the impact of including non-additive genetic effects on the ranking of the animals’ breeding value and in the proportion of commonly selected individuals. The second dataset consisted of records from 1,645 lactating sows (Large White x Landrace cross) genotyped for 50,703 SNPs and traits related to heat stress response: skin temperature (ear, shoulder, rump, and tail), vaginal temperature measured every 10 minutes, and the average of the six records per hour corresponding to 08:00, 12:00, 16:00, and 20:00 hours during four days; respiration rate, panting score (PS; score scale from 0 to 3), and hair density (HD, score scale from 0 to 2). Four models including or not, inbreeding and the effect of dominance and additive-by-additive epistasis were used to estimate variance components. There was no effect on residual variance estimates due to the inclusion of non-additive genetic effects in the models for most traits. However, non-additive genetic effects reduced additive variance estimates, especially when additive-by-additive epistasis was fitted. Including non-additive genetic effects in the model did not improve the prediction accuracy of breeding values for purebreds, but there was a substantial change in the ranking of the animals and in the proportion of commonly selected individuals. For the crossbred traits, small non-additive genetic variance with large standard error estimates were obtained. Nevertheless, PS and HD presented notable additive-by-additive epistatic variance. PS presented h2aa estimates of 0.15 and additive-by-additive epistasis corresponding to 86.76% of the total genetic variance, while HD presented additive-by-additive epistatic heritability (h2aa) estimates from 0.46 to 0.49 and the proportion of total genetic variance explained by additive-by-additive epistasis ranged from 66.91% to 71.87%. In conclusion, including non-additive genetic effects did not improve the accuracy of prediction of breeding values for purebreds, but it changed the ranking of animals and selection decisions. Although PS and HD had large additive-by-additive epistasis effects, most of the traits related to heat stress in the crossbred population did not present relevant non-additive genetic effects.

Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley

BackgroundMetabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method.ResultsFor all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known.ConclusionsMGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species.

The importance of historically popular sires on the accuracy of genomic predictions of young animals in the US Holstein population

The dairy industry is known for its extensive use of artificial insemination, which has resulted in a population where most animals can be traced back to only a few sires. Due to their relatedness to the population, old influential sires could still contribute to the accuracy of genomic predictions. The objective of the study was to identify the impact of historically influential sires on the recent population. This was tested by constructing a genomic relationship matrix using recursion with different sets of sires. Differences in prediction accuracies with different sets are indicative of how important each set is. Recursion coefficients linking young animals to those sets reveal the relative importance of specific sires to the prediction accuracy of recent animals. The data included ∼10 million scores for stature and fore udder attachment (FUA) measured from 1983. Genotypes of 569,404 animals were available. Sire sets included the 100 most popular sires born within different time periods. Computations were with single-step genomic BLUP. In general, the younger sires had higher prediction accuracies than the oldest sires, even though they generally have fewer progeny. The accuracy of evaluation for stature was increased from 0.54 with the most popular sires born before 1981 to 0.69 with sires born from 2001 to 2010, while the accuracy for FUA increased from 0.47 to 0.61. The accuracy achieved using the overall 100 most used sires was 0.66 for stature and 0.58 for FUA. All 100 sires from each period were combined in a subset to determine the importance of each sire relative to all 400 animals in the combined subset. The highest relative impact of a sire that was born within the different time sets was 1.97 for Valiant (before 1981), 1.94 for Blackstar (1981 to 1990), 4.38 for Shottle (1991 to 2000), and 3.09 for Planet (2001 to 2010). The 3 sires among the 400 with the greatest impact were Shottle, Goldwyn (3.73), and Planet. The relative impact of a sire was not strongly related to the number of progeny. For instance, the relative impact of Durham with 34K progeny was 2.29, whereas the impact of O Man with 15K progeny was 3.13. The impact of a sire is also influenced by whether it was used as a sire of sires. Results show that younger sires are more relevant to the accuracy of breeding value prediction in the recent population.

Molecular Design-Based Breeding: A Kinship Index-Based Selection Method for Complex Traits in Small Livestock Populations.

Genomic selection (GS) techniques have improved animal breeding by enhancing the prediction accuracy of breeding values, particularly for traits that are difficult to measure and have low heritability, as well as reducing generation intervals. However, the requirement to establish genetic reference populations can limit the application of GS in pig breeds with small populations, especially when small populations make up most of the pig breeds worldwide. We aimed to propose a kinship index based selection (KIS) method, which defines an ideal individual with information on the beneficial genotypes for the target trait. Herein, the metric for assessing selection decisions is a beneficial genotypic similarity between the candidate and the ideal individual; thus, the KIS method can overcome the need for establishing genetic reference groups and continuous phenotype determination. We also performed a robustness test to make the method more aligned with reality. Simulation results revealed that compared to conventional genomic selection methods, the KIS method is feasible, particularly, when the population size is relatively small.

Genetic Control of Pitch Canker Response in Southern Pine and Southern Pine Hybrids

Fusarium circinatum causes pine pitch canker (PPC) disease and associated symptoms such as resinous lesions, mechanical weakness, and crown dieback that may lead to mortality in Pinus and Pseudotsuga spp. There are no ameliorative techniques available for the disease, and the genetic resistance among populations to support commercial plantation deployment has not been well characterized. In this study, we characterize the genetic control of PPC disease tolerance (and/or resistance) and predict the tolerance of families in existing breeding populations: open-pollinated (OP) half-sib and control-pollinated full-sib (FS) slash pine (Pinus elliottii var. elliottii Engelm.), OP loblolly pine (Pinus taeda L.), and advanced-generation OP hybrid slash × P. caribaea (Pinus elliottii var. elliottii Engelm. × Pinus caribaea var. bahamensis, caribaea, and hondurensis) using F. circinatum isolates obtained from three locations in Georgia and FL, USA. We describe a new experimental design that improves the accuracy of breeding value predictions, provides more precise genetic parameter estimates, and facilitates comparisons within and among taxa as well as comparisons among isolates. We found strong evidence for genetic control of the ratio of stem damage by F. circinatum, especially in slash pine and slash × P. caribaea hybrids. Loblolly and slash × P. caribaea hybrids exhibited less damage than slash pine. We observed a spectrum of virulence among F. circinatum isolate sources, which were not equally virulent in different pine taxa.

Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing

BackgroundIn aquaculture, the proportion of edible meat (FY = fillet yield) is of major economic importance, and breeding animals of superior genetic merit for this trait can improve efficiency and profitability. Achieving genetic gains for fillet yield is possible using a pedigree-based best linear unbiased prediction (PBLUP) model with direct and indirect selection. To investigate the feasibility of using genomic selection (GS) to improve FY and body weight (BW) in rainbow trout, the prediction accuracy of GS models was compared to that of PBLUP. In addition, a genome-wide association study (GWAS) was conducted to identify quantitative trait loci (QTL) for the traits. All analyses were performed using a two-trait model with FY and BW, and variance components, heritability, and genetic correlations were estimated without genomic information. The data used included 14,165 fish in the pedigree, of which 2742 and 12,890 had FY and BW phenotypic records, respectively, and 2484 had genotypes from the 57K single nucleotide polymorphism (SNP) array.ResultsThe heritabilities were moderate, at 0.41 and 0.33 for FY and BW, respectively. Both traits were lowly but positively correlated (genetic correlation; r = 0.24), which suggests potential favourable correlated genetic gains. GS models increased prediction accuracy compared to PBLUP by up to 50% for FY and 44% for BW. Evaluations were found to be biased when validation was performed on future performances but not when it was performed on future genomic estimated breeding values.ConclusionsThe low but positive genetic correlation between fillet yield and body weight indicates that some improvement in fillet yield may be achieved through indirect selection for body weight. Genomic information increases the prediction accuracy of breeding values and is an important tool to accelerate genetic progress for fillet yield and growth in the current rainbow trout population. No significant QTL were found for either trait, indicating that both traits are polygenic, and that marker-assisted selection will not be helpful to improve these traits in this population.

Deciphering the genetic basis and prediction genomic estimated breeding values of heat tolerance in Zhikong scallop Chlamys farreri

The changes in seawater temperature due to global warming have produced profound influences on the growth, survival and production of aquatic species. Therefore, improving the performance of these species under heat stress is crucial for the sustainable development of aquaculture. Genomic selection, as a method of genetic improvement of economic traits, has great potential and value in cultivation of new strains with adaptation advantages. In present study, 1060 Zhikong scallops Chlamys farreri, an economically important species, were exposed to a gradient heating experiment to obtain individuals with heat tolerance. Among them, 154 individuals died firstly during experiment were considered as heat-sensitive group (HS), and 156 still alive after recovering for 7 days were taken as heat-tolerant group (HT). Then, these 310 individuals were sequenced, and 35,507 single nucleotide polymorphisms (SNPs) of 289 scallops were genotyped for genome wide association study (GWAS) and genomic prediction analysis. The results showed that seven SNPs and two genes (PNC1 and RASD1) were significant associated with heat tolerance trait in C. farreri. In addition, the heat tolerance trait of C. farreri was moderately heritable, and the heritability of survival status and survival time was 0.52 ± 0.04 and 0.24 ± 0.03, respectively. Furthermore, genomic best linear unbiased prediction (GBLUP) and Bayes B models were proved to be more suitable for genomic breeding value predictions of heat tolerance trait in C. farreri. The accuracy of genomic estimated breeding value could be stabilized when the number of markers reached 8 k. Population size could improve the accuracy of breeding value predictions and the accuracy has not reach a plateau yet in our study. Taken together, the findings obtained in this study provide insights into the genetic basis of heat tolerance in C. farreri and contribute to the genetic improvement of scallop and other related species.

Reduced rank analysis of morphometric and functional traits in Campolina horses.

Multitrait models can increase the accuracy of breeding value prediction and reduce bias due to selection by using traits measured before and after it has occurred. However, as the number of traits grows, a similar trend is expected for the number of parameters to be estimated, which directly affects the computing power and the amount of data required. The aim of the present study was to apply reduced rank (principal components model-PCM) and factor analytical models (FAM), to estimate (co)variance components for nineteen traits, jointly evaluated in a single analysis in Campolina horses. A total of 18 morphometric traits (MT) and one gait visual score (GtS), along with genealogical records of 48,806 horses, were analysed under a restricted maximum likelihood framework. Nine PCM, nine FAM and one standard multitrait model (MTM) were fitted to the data and compared to find the best suitable model. Based on Bayesian information criterion, the best model was the FAM option, considering five common factors (FAM5). After performing an intraclass analysis, none of MT were genetically negatively correlated, whereas GtS was negatively related to all MT, except for the genetic correlations among GtS and BLL, and between GtS and BLLBL (0.01 and 0.10 respectively). From all MT, two traits were derived computing ratios involving other traits, those had negative correlations with others MT, but all favourable for selection. Similar patterns were observed between the genetic parameters obtained from MTM and FAM5 respectively. The heritability estimates ranged from 0.09 (head width) to 0.47 (height at withers). Our results indicated that FAM was efficient to reduce the multitrait analysis dimensionality, and therefore, traits can be combined based on the first three eigenvectors from the additive genetic (co)variance matrix. In addition, there was sufficient genetic variation for selection, benefiting its potential implementation in a breeding program.

SsGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca.

Simple SummaryAlpaca breeding takes place in the most entrenched areas of the Andes, where the conditions to implement genetic improvement programs are very difficult. Likewise, taking phenotypic records is limited in its ability to predict genetic merit accurately. For this reason, genomic information is shown as an alternative that helps to predict the genetic values of fiber traits more precisely. This study showed how genomic information increased precision by 2.623% for the fiber diameter, 6.442% for the standard deviation of the fiber diameter, and 1.471% for the percentage of medullation compared to traditional methods for predicting genetic merit, suggesting that adding genomic data in prediction models could be beneficial for alpaca breeding programs in the future.Improving textile characteristics is the main objective of alpaca breeding. A recently developed SNP chip for alpacas could potentially be used to implement genomic selection and accelerate genetic progress. Therefore, this study aimed to compare the increase in prediction accuracy of three important fiber traits: fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) in Huacaya alpacas. The data contains a total pedigree of 12,431 animals, 24,169 records for FD and SD, and 8386 records for PM and 60,624 SNP markers for each of the 431 genotyped animals of the Pacomarca Genetic Center. Prediction accuracy of breeding values was compared between a classical BLUP and a single-step Genomic BLUP (ssGBLUP). Deregressed phenotypes were predicted. The accuracies of the genetic and genomic values were calculated using the correlation between the predicted breeding values and the deregressed values of 100 randomly selected animals from the genotyped ones. Fifty replicates were carried out. Accuracies with ssGBLUP improved by 2.623%, 6.442%, and 1.471% on average for FD, SD, and PM, respectively, compared to the BLUP method. The increase in accuracy was relevant, suggesting that adding genomic data could benefit alpaca breeding programs.

Genomic Selection and Genome-wide Association Study for Feed-Efficiency Traits in a Farmed Nile Tilapia (Oreochromis niloticus) Population.

Nile tilapia is a key aquaculture species with one of the highest production volumes globally. Genetic improvement of feed efficiency via selective breeding is an important goal, and genomic selection may expedite this process. The aims of this study were to 1) dissect the genetic architecture of feed-efficiency traits in a Nile tilapia breeding population, 2) map the genomic regions associated with these traits and identify candidate genes, 3) evaluate the accuracy of breeding value prediction using genomic data, and 4) assess the impact of the genetic marker density on genomic prediction accuracies. Using an experimental video recording trial, feed conversion ratio (FCR), body weight gain (BWG), residual feed intake (RFI) and feed intake (FI) traits were recorded in 40 full-sibling families from the GIFT (Genetically Improved Farmed Tilapia) Nile tilapia breeding population. Fish were genotyped with a ThermoFisher Axiom 65 K Nile tilapia SNP array. Significant heritabilities, ranging from 0.12 to 0.22, were estimated for all the assessed traits using the genomic relationship matrix. A negative but favourable genetic correlation was found between BWG and the feed-efficiency related traits; −0.60 and −0.63 for FCR and RFI, respectively. While the genome-wide association analyses suggested a polygenic genetic architecture for all the measured traits, there were significant QTL identified for BWG and FI on chromosomes seven and five respectively. Candidate genes previously found to be associated with feed-efficiency traits were located in these QTL regions, including ntrk3a, ghrh and eif4e3. The accuracy of breeding value prediction using the genomic data was up to 34% higher than using pedigree records. A SNP density of approximately 5,000 SNPs was sufficient to achieve similar prediction accuracy as the full genotype data set. Our results highlight the potential of genomic selection to improve feed efficiency traits in Nile tilapia breeding programmes.

Accuracy of breeding values for production traits in turkeys (Meleagris gallopavo) using recursive models with or without genomics

BackgroundKnowledge about potential functional relationships among traits of interest offers a unique opportunity to understand causal mechanisms and to optimize breeding goals, management practices, and prediction accuracy. In this study, we inferred the phenotypic causal networks among five traits in a turkey population and assessed the effect of the use of such causal structures on the accuracy of predictions of breeding values.MethodsPhenotypic data on feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking score in addition to genotype data from a commercial breeding population were used. Causal links between the traits were detected using the inductive causation algorithm based on the joint distribution of genetic effects obtained from a standard Bayesian multiple trait model. Then, a structural equation model was implemented to infer the magnitude of causal structure coefficients among the phenotypes. Accuracies of predictions of breeding values derived using pedigree- and blending-based multiple trait models were compared to those obtained with the pedigree- and blending-based structural equation models.ResultsIn contrast to the two unconditioned traits (i.e., feed conversion ratio and breast meat yield) in the causal structures, the three conditioned traits (i.e., residual feed intake, body weight, and walking score) showed noticeable changes in estimates of genetic and residual variances between the structural equation model and the multiple trait model. The analysis revealed interesting functional associations and indirect genetic effects. For example, the structural coefficient for the path from body weight to walking score indicated that a 1-unit genetic improvement in body weight is expected to result in a 0.27-unit decline in walking score. Both structural equation models outperformed their counterpart multiple trait models for the conditioned traits. Applying the causal structures led to an increase in accuracy of estimated breeding values of approximately 7, 6, and 20% for residual feed intake, body weight, and walking score, respectively, and different rankings of selection candidates for the conditioned traits.ConclusionsOur results suggest that structural equation models can improve genetic selection decisions and increase the prediction accuracy of breeding values of selection candidates. The identified causal relationships between the studied traits should be carefully considered in future turkey breeding programs.

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

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.

9 A comparison of different genomic prediction methods in the Canadian Landrace swine population

Abstract Increasing the accuracy of breeding value prediction can lead to more profitability through accelerating genetic progress for economic traits. The objective of this study was to assess the predictive abilities and unbiasedness of best linear unbiased prediction (BLUP) and popular genomic prediction methods of BayesC, BayesC(π = 0.99), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP). Genotypic information (50K and 60K) of 4,890 performance tested Landrace pigs before February 2019 and 471 validation Landrace pigs that both had phenotypic information on backfat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) from two Canadian pig breeding companies (AlphaGene and Alliance Genetics Canada) were used. The de-regressed breeding values (DEBV) were employed in GBLUP and Bayesian methods. A total number of 48,580 single nucleotide polymorphisms remained after quality control and imputation steps. The prediction accuracies were calculated using the correlation between predicted breeding values before performance test and DEBVs after performance test. All employed genomic prediction methods showed higher prediction accuracies for BFT (50.80–52.68%), ADG (26.61–34.47%), and LMD (18.25–25.08%) compared to BLUP method (BFT = 28.54%, ADG = 16.41%, LMD = 17.15%). The highest prediction accuracies for BFT and ADG were obtained using ssGBLUP method, and for LMD it was obtained using BayesC(π = 0.99). The BayesC(π = 0.99) showed also the lowest prediction biases across the studied traits (+0.05 for BFT, 0.00 for AGD, and -0.10 for LMD). In conclusion, our results revealed the superiority of ssGBLUP (for BFT and ADG) and BayesC(π = 0.99) (for LMD) over other tested methods in this study. However, the prediction accuracies from the tested genomic prediction methods were not significantly different from each other. Thus, employing these methods can be helpful for accelerating the genetic improvement of BFT, ADG, and LMD in the moderate population size of Canadian Landrace.

Assessing Accuracy of Genomic Predictions for Resistance to Infectious Hematopoietic Necrosis Virus With Progeny Testing of Selection Candidates in a Commercial Rainbow Trout Breeding Population.

Infectious hematopoietic necrosis (IHN) is an economically important disease of salmonid fish caused by the IHN virus (IHNV). Under industrial aquaculture settings, IHNV can cause substantial mortality and losses. Actually, there is no confirmed and cost-effective method for IHNV control. Clear Springs Foods, Inc. has been performing family-based selective breeding to increase genetic resistance to IHNV in their rainbow trout breeding program. In an earlier study, we used siblings cross-validation to estimate the accuracy of genomic prediction (GP) for IHNV resistance in this breeding population. In the present report, we used empirical progeny testing data to evaluate whether genomic selection (GS) can improve the accuracy of breeding value predictions over traditional pedigree-based best linear unbiased predictions (PBLUP). We found that the GP accuracy with single-step GBLUP (ssGBLUP) outperformed PBLUP by 15% (from 0.33 to 0.38). Furthermore, we found that ssGBLUP had higher GP accuracy than weighted ssGBLUP (wssGBLUP) and single-step Bayesian multiple regression (ssBMR) models with BayesB and BayesC priors which supports our previous findings that the underlying liability of genetic resistance against IHNV in this breeding population might be polygenic. Our results show that GS can be more effective than either the traditional pedigree-based PBLUP model or the marker-assisted selection approach for improving genetic resistance against IHNV in this commercial rainbow trout population.

Genomic prediction of yield and root development in wheat under changing water availability

BackgroundDeeper roots help plants take up available resources in deep soil ensuring better growth and higher yields under conditions of drought. A large-scale semi-field root phenotyping facility was developed to allow a water availability gradient and detect potential interaction of genotype by water availability gradient. Genotyped winter wheat lines were grown as rows in four beds of this facility, where indirect genetic effects from neighbors could be important to trait variation. The objective was to explore the possibility of genomic prediction for grain-related traits and deep root traits collected via images taken in a minirhizotron tube under each row of winter wheat measured.ResultsThe analysis comprised four grain-related traits: grain yield, thousand-kernel weight, protein concentration, and total nitrogen content measured on each half row that were harvested separately. Two root traits, total root length between 1.2 and 2 m depth and root length in four intervals on each tube were also analyzed. Two sets of models with or without the effects of neighbors from both sides of each row were applied. No interaction between genotypes and changing water availability were detected for any trait. Estimated genomic heritabilities ranged from 0.263 to 0.680 for grain-related traits and from 0.030 to 0.055 for root traits. The coefficients of genetic variation were similar for grain-related and root traits. The prediction accuracy of breeding values ranged from 0.440 to 0.598 for grain-related traits and from 0.264 to 0.334 for root traits. Including neighbor effects in the model generally increased the estimated genomic heritabilities and accuracy of predicted breeding values for grain yield and nitrogen content.ConclusionsSimilar relative amounts of additive genetic variance were found for both yield traits and root traits but no interaction between genotypes and water availability were detected. It is possible to obtain accurate genomic prediction of breeding values for grain-related traits and reasonably accurate predicted breeding values for deep root traits using records from the semi-field facility. Including neighbor effects increased the estimated additive genetic variance of grain-related traits and accuracy of predicting breeding values. High prediction accuracy can be obtained although heritability is low.

Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data.

Genomic selection has been extensively implemented in plant breeding schemes. Genomic selection incorporates dense genome-wide markers to predict the breeding values for important traits based on information from genotype and phenotype records on traits of interest in a reference population. To date, most relevant investigations have been performed using single trait genomic prediction models (STGP). However, records for several traits at once are usually documented for breeding lines in commercial breeding programs. By incorporating benefits from genetic characterizations of correlated phenotypes, multiple trait genomic prediction (MTGP) may be a useful tool for improving prediction accuracy in genetic evaluations. The objective of this study was to test whether the use of MTGP and including proper modeling of spatial effects can improve the prediction accuracy of breeding values in commercial barley and wheat breeding lines. We genotyped 1,317 spring barley and 1,325 winter wheat lines from a commercial breeding program with the Illumina 9K barley and 15K wheat SNP-chip (respectively) and phenotyped them across multiple years and locations. Results showed that the MTGP approach increased correlations between future performance and estimated breeding value of yields by 7% in barley and by 57% in wheat relative to using the STGP approach for each trait individually. Analyses combining genomic data, pedigree information, and proper modeling of spatial effects further increased the prediction accuracy by 4% in barley and 3% in wheat relative to the model using genomic relationships only. The prediction accuracy for yield in wheat and barley yield trait breeding, were improved by combining MTGP and spatial effects in the model.

Evaluation of the utility of genomic information to improve genetic evaluation of feed efficiency traits of the Pacific white shrimp Litopenaeus vannamei

This study evaluated the utility of genomic information to improve genetic evaluation of feed efficiency traits of the Pacific white shrimp Litopenaeus vannamei. Three methods including the pedigree-based best linear unbiased prediction (BLUP), genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) were compared for the prediction accuracy of feed efficiency ratio (FER) and residual feed intake (RFI), as well as a growth trait, average daily weight gain (ADG). The dataset used for analysis consisted of 474 offspring from 34 full-sib families, with a pedigree of three generations, and all the offspring were genotyped on 35,869 SNPs. Two univariate animal models were used, one with the common environmental effect (Model 1) and one without (Model 2). The genomic and pedigree-genomic relationship matrices based heritability estimates of RFI, FER and ADG by Model 1 ranged from 0.187 ± 0.156 to 0.388 ± 0.167, with the common environmental effect in the range of 0.057 ± 0.080 to 0.283 ± 0.108, but it was unable to estimate both additive genetic and common environmental effects simultaneously using the pedigree-based relationship matrix. Across the three matrices, heritability estimates of RFI, FER and ADG by Model 2 ranged from 0.476 ± 0.101 to 0.796 ± 0.146, higher than the corresponding ones using Model 1. Cross-validation was used to evaluate the prediction accuracy of breeding values. For GBLUP and ssGBLUP, genomic prediction accuracy using Model 1 was significantly increased by 28.6–29.0% for RFI, 11.6–12.5% for FER and 62.2–69.4% for ADG, compared with using Model 2, indicative of the advantage of Model 1 over Model 2. When using Model 2, genomic predictions were more accurate than the pedigree-based predictions, with an increase of accuracy ranging from 8.9% to 13.3% across the three traits. Furthermore, accuracy of GBLUP was slightly higher than that of ssGBLUP. The results indicate that the genomic-based methods, especially GBLUP, combined with an animal model containing the common environmental effect, have obvious advantage in estimating variance components and predicting breeding values for feed efficiency traits as well as growth of L.vannamei compared with traditional BLUP.

Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: an example from white spruce

Genomic selection (GS) has a large potential for improving the prediction accuracy of breeding values and significantly reducing the length of breeding cycles. In this context, the choice of mating designs becomes critical to improve the efficiency of breeding operations and to obtain the largest genetic gains per time unit. Polycross mating designs have been traditionally used in tree and plant breeding to perform backward selection of the female parents. The possibility to use genetic markers for paternity identification and for building genomic prediction models should allow for a broader use of polycross tests in forward selection schemes. We compared the accuracies of genomic predictions of offspring’s breeding values from a polycross and a full-sib (partial diallel) mating design with similar genetic background in white spruce (Picea glauca). Trees were phenotyped for growth and wood quality traits, and genotyped for 4092 SNPs representing as many gene loci distributed across the 12 spruce chromosomes. For the polycross progeny test, heritability estimates were smaller, but more precise using the genomic BLUP (GBLUP) model as compared with pedigree-based models accounting for the maternal pedigree or for the reconstructed full pedigree. Cross-validations showed that GBLUP predictions were 22–52% more accurate than predictions based on the maternal pedigree, and 5–7% more accurate than predictions using the reconstructed full pedigree. The accuracies of GBLUP predictions were high and in the same range for most traits between the polycross (0.61–0.70) and full-sib progeny tests (0.61–0.74). However, higher genetic gains per time unit were expected from the polycross mating design given the shorter time needed to conduct crosses. Considering the operational advantages of the polycross design in terms of easier handling of crosses and lower associated costs for test establishment, we believe that this mating scheme offers great opportunities for the development and operational application of forward GS.