Predicted Breeding Values Research Articles

Estimation of Genetic Parameters of Type Traits in First Parity Cows of the Autochthonous Cika Cattle in Slovenia.

The aim of this study was to estimate genetic parameters of 26 individual and four composite type traits in first parity Cika cows. An analysis of variance was performed with the generalized linear model procedure of the SAS/STAT statistical package, where the fixed effects of year of recording, cow’s age at recording and days after calving as a linear regression were included in the model. The variance components for the direct additive genetic effect and the herd effect in all type traits were estimated using the REML method in the VCE-6 software package. The estimated heritabilities ranged from 0.42 to 0.67 for the measured body frame traits, from 0.36 to 0.80 for the scored autochthonous traits, from 0.11 to 0.61 for the scored body frame traits, and from 0.20 to 0.47 for the scored udder traits. The estimated heritabilities for the composite traits called “autochthonous characteristics”, “muscularity”, “body frame” and “udder” were 0.55, 0.19, 0.19, and 0.26, respectively. The estimated genetic correlations among the measured body frame traits were positive and high, while the majority of them among the scored body frame traits were low to moderate. The estimated proportions of variance explained by the herd effect for the composite traits “autochthonous characteristics,” “muscularity,” “body frame” and “udder” were 0.09, 0.28, 0.14, and 0.10, respectively. The estimated heritabilities for the type traits of first parity Cika cows were similar to those reported for other breeds where breeding values have been routinely predicted for a long time. All estimated genetic parameters are already used for breeding value prediction in the Cika cattle population.

Genomic prediction of 10 metamorphic traits of captive-bred Japanese eels (Anguilla japonica) using the GRAS-Di genotyping method

The Japanese eel (Anguilla japonica) is one of the most important aquaculture fish species in Eastern Asia. A major bottleneck in the mass production of glass eels is the long larval period that they undergo in captivity. In the present study, 1689 glass eels, originating from a partly factorial cross (43 sires × 32 dams), were used to investigate the potential value of genomic selection for 10 traits related to the timing of metamorphosis and the body size at that time. Genotyping by random amplicon sequencing-direct (GRAS-Di®) sequencing was used to identify and genotype single nucleotide polymorphism (SNP) markers, which were used to assign parentage and as a basis for genomic selection. The genomic heritabilities of the 10 metamorphic traits obtained by genomic best linear unbiased prediction (GBLUP) models were moderate (range 0.21–0.38), and the standard errors were smaller than those determined by the pedigree-based best linear unbiased prediction (ABLUP) models. The genomic prediction of breeding values outperformed the pedigree-based prediction, resulting in a 3%–19% improvement in the prediction accuracy. The impact of reduced SNP densities on the predictive ability of GBLUP was tested using a linkage disequilibrium-based pruning and random thinning method to reduce 17,017 SNPs to 2117 SNPs. An SNP set of 8000 was considered to be sufficient to achieve predictive ability comparable to that obtained using higher SNP densities for both methods. The selective genotyping of the top individuals in the training population led to a severe reduction in the predictive ability compared with the random genotyping of the same number of samples. These results will be useful for decision-making in establishing best-practice breeding programs when applied to genomic selection for metamorphic traits for shortening the larval period and improving seed quality in Japanese eels.

Accuracy of Genomic Prediction for Milk Production Traits in Philippine Dairy Buffaloes.

The objective of this study was to compare the accuracies of genomic prediction for milk yield, fat yield, and protein yield from Philippine dairy buffaloes using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with the accuracies based on pedigree BLUP (pBLUP). To also assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted breeding values (BVs) was also calculated. Two data sets were analyzed. The GENO data consisting of all female buffaloes that have both phenotypes and genotypes (n = 904 with 1,773,305-days lactation records) were analyzed using pBLUP and GBLUP. The ALL data, consisting of the GENO data plus females with phenotypes but not genotyped (n = 1,975 with 3,821,305-days lactation records), were analyzed using pBLUP and ssGBLUP. Animals were genotyped with the Affymetrix 90k buffalo genotyping array. After quality control, 60,827 single-nucleotide polymorphisms were used for downward analysis. A pedigree file containing 2,642 animals was used for pBLUP and ssGBLUP. Accuracy of prediction was calculated as the correlation between the predicted BVs of the test set and adjusted phenotypes, which were corrected for fixed effects, divided by the square root of the heritability of the trait, corrected for the number of lactations used in the test set. To assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs was also calculated. Results showed that genomic methods (GBLUP and ssGBLUP) provide more accurate predictions compared to pBLUP. Average GBLUP and ssGBLUP accuracies were 0.24 and 0.29, respectively, whereas average pBLUP accuracies (for GENO and ALL data) were 0.21 and 0.22, respectively. Slopes of the two genomic methods were also closer to one, indicating lesser bias, compared to pBLUP. Average GBLUP and ssGBLUP slopes were 0.89 and 0.84, respectively, whereas the average pBLUP (for GENO and ALL data) slopes were 0.80 and 0.54, respectively.

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.

Commercial implementation of genomic selection in Tasmanian Atlantic salmon: Scheme evolution and validation.

Genomic information was included for the first time in the prediction of breeding values for Atlantic salmon within the Australian Salmon Enterprises of Tasmania Pty Ltd selective breeding program in 2016. The process to realize genomic selection in the breeding program begun in 2014 with the scheme finalized and fully implemented for the first time in 2018. The high potential of within family selection to accelerate genetic gain, something not possible using the traditional pedigree‐based approach, provided the impetus for implementation. Efficient and effective genotyping platforms are essential for genomic selection. Genotype data from high density arrays revealed extensive persistence of linkage disequilibrium in the Tasmania Atlantic salmon population, resulting in high accuracies of both imputation and genomic breeding values when using imputed data. Consequently, a low‐density novel genotype‐by‐sequence assay was designed and incorporated into the scheme. Through the use of a static high‐ and dynamic low‐density genotyping platforms, an optimized genotyping scheme was devised and implemented such that all individuals in every year class are genotyped efficiently while maximizing the genetic gains and minimizing costs. The increase in the rates of genetic gain attributed to the implementation of genomic selection is significant across both the breeding programs primary and secondary traits. Substantial improvement in the ability to select parents prior to progeny testing is observed across multiple years. The resultant economic impacts for the industry are considerable based on the increases in genetic gain for traits achieved within the breeding program and the use of genomic selection for commercial production.

49 Predicting Breeding Values of Purebred Pigs for Crossbred Performance Using Crossbred Phenotypes and Genotypes

Abstract Phenotyping a large number of crossbred progeny for the evaluation of purebred animals can be expensive. As genotyping with low-density panels is becoming cheaper, we aimed to evaluate the tradeoff between having different percentages of genotypes and phenotypes for crossbred progeny of candidate boars. We used the linear regression (LR) method to investigate changes in accuracy, bias, and inflation of breeding values for crossbred traits in purebred boars. A total of 304,582 purebred and 147,474 crossbred animals were phenotyped for average daily gain (ADG) and backfat thickness (BF), out of which 46,691 purebred and 13,117 crossbred animals were genotyped. Genomic information consisted of imputed genotypes for 40,247 SNP markers after quality control. A four-trait animal model under single-step GBLUP was used that included phenotypes recorded in purebred and crossbred animals as correlated traits. The LR statistics were calculated based on breeding values of young purebred sires from complete and partial data. The first complete data included genotypes for purebreds and phenotypes for purebreds and crossbreds, whereas the second included also genotypes for crossbreds. The partial data included phenotypes on 50% or none of the progeny of validation sires, with or without genotypes for crossbred animals. When 50% of the progeny has phenotypes, adding genotypes for crossbred progeny marginally increased accuracy of ADG (0.77 vs 0.78) for 47 boars with more than 150 progeny with phenotypes. No increase was observed for BF. A small increase in bias and inflation by adding crossbred genotypes was observed for ADG but not for BF. When no phenotypes were available for crossbred progeny, accuracy for both traits was lower but improved with crossbred genotypes for ADG (0.61 vs 0.64) for boars with more than 150 progeny. The tradeoff between phenotypes and genotypes should be further investigated in larger datasets with more validation boars.

33 Impact of Including the Cause of Missing Records on Genetic Evaluations for Growth in Commercial Pigs

Abstract It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.

Heritability Estimate for Antibody Response to Vaccination and Survival to a Newcastle Disease Infection of Native chicken in a Low-Input Production System

The Newcastle disease virus (NDV) is the deadliest chicken pathogen in low-input village poultry, and selecting for NDV resistance has been recommended as a sustainable strategy in backyard poultry production systems. However, selecting for disease resistance needs precision data from either a big population sample size or on many generations with good pedigree records for effective prediction of heritability (h2) and breeding values of the foundation stock. Such conditions are almost impossible to meet in low-input backyard production systems. This study aimed at proposing a realistic method for estimating the heritability of the immune response to vaccination and survival of NDV infection in village poultry production to inform a breeding strategy for ND resistance in Cameroon. A 1 and 3% selection intensity of cocks and hens for higher antibody (ab) response (ABR) to vaccination followed by progeny selection of chickens who survived an experimental NDV infection was conducted from an initial population of 1,702 chickens. The selection induced an increase of 1012.47units/ml (p<0.01) of the NDV antibody of the progeny as well as an effective survival rate (ESR) increase of 11.75%. Three methods were used to estimate the heritability () of NDV antibody response to vaccination. was low irrespective of the method with estimates of 0.2227, 0.2442, and 0.2839 for the breeder’s equation method, the graphical method, and the full-sib/half-sib nested design, respectively. The mortality rate of infected chickens was high (86%). The antibody response to selection was not influenced by sex and genetic type even though the opposite was observed (p<0.05) for the ESR to NDV infection with naked neck chickens recording an ESR of 14% against 2.25% for the normal feather type. A very low heritability (0.0891) was observed for the survival against NDV infection. We confirm the evidence of disease resistance and the effect of selection for antibody response to vaccination on the improvement of the survival against NDV disease. Although the full sib/half sib nested design is more appropriate in case of availability of pedigree information, the direct methods are still useful in case of unavailability of full pedigree information. It is recommended that gene expression analysis should be prioritized for disease-resistance assessment and selection of native breeds of poultry.

Use of Random regression model for modeling growth trajectory in Jamunapari goat in the semi-arid region of India

The objective of the study was to plot the trajectory of the growth curve in Jamunapari goat using B-spline (BS) function and compare it with the Legendre polynomial (LP) function of the random regression model (RRM) and also to assess the genotype and environment interaction for growth traits. The data was collected from 8763 descendants of 1845 dams and 339 sires. The RRM used direct genetic, direct maternal, permanent environment for animal and maternal permanent environmental effects in different orders of fit. The B-spline model (BSL6H5) was more robust compared to LP-RRM. The heritability (h2) estimates were 0.23 ± 0.03, 0.26 ± 0.02, 0.22 ± 0.02, 0.19 ± 0.02, 0.22 ± 0.02, respectively for birth weight (BW), three-month weight (3MW), six-month weight (6MW), nine-month weight (9MW) and twelve-month weight (12MW) from the best model. The h2 estimate indicated the further scope of genetic improvement through selection. The maternal proportion of variance was accounting for 3 to 8% variance across growth trajectory indicating the low influence of maternal effects that reduced significantly post-weaning. However, we observed that the animal permanent environment accounted for 45 to 69% of variance across growth trajectory. The genetic correlations between 6MW, 9MW, and 12MW were high and positive indicating scope for early selection. The genetic trends at 3, 6, 9, and 12 months of age were 0.065 kg, 0.082 kg, 0.098 kg, and 0.121 kg per year, respectively, indicating a favorable impact of selection for growth traits. The ranks of the sires at different time points based on breeding values through B-spline RRM were more robust as compared with estimates from the conventional animal model and LP-RRM, where the presence of genotype by environment (GxE) interaction resulted in significant re-ranking of sires for 9MW and 12MW. The B-spline RRM approach resulted in a pragmatic solution for the problem of GxE interaction across growth trajectories. We conclude that the B-spline RRM should be used for genetic evaluation of Jamunapari goats in the future for unbiased, accurate, and consistent prediction of breeding values.

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.

Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.

Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine- and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine- and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0-28.5 and -0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine- and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine- and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine- and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.

High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production.

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.

Simultaneous Bayesian estimation of genetic parameters for curves of weight, feed intake, and residual feed intake in beef cattle.

Rates of gain and feed efficiency are important traits in most breeding programs for growing farm animals. The rate of gain (GAIN) is usually expressed over a certain age period and feed efficiency is often expressed as residual feed intake (RFI), defined as observed feed intake (FI) minus expected feed intake based on live weight (WGT) and GAIN. However, the basic traits recorded are always WGT and FI and other traits are derived from these basic records. The aim of this study was to develop a procedure for simultaneous analysis of the basic records and then derive linear traits related to feed efficiency without retorting to any approximation. A bivariate longitudinal random regression model was employed on 13,791 individual longitudinal records of WGT and FI from 2,827 bulls of six different beef breeds tested for their own performance in the period from 7 to 13 mo of age. Genetic and permanent environmental covariance functions for curves of WGT and FI were estimated using Gibbs sampling. Genetic and permanent covariance functions for curves of GAIN were estimated from the first derivative of the function for WGT and finally the covariance functions were extended to curves for RFI, based on the conditional distribution of FI given WGT and GAIN. Furthermore, the covariance functions were extended to include GAIN and RFI defined over different periods of the performance test. These periods included the whole test period as normally used when predicting breeding values for GAIN and RFI for beef bulls. Based on the presented method, breeding values and genetic parameters for derived traits such as GAIN and RFI defined longitudinally or integrated over (parts of) of the test period can be obtained from a joint analysis of the basic records. The resulting covariance functions for WGT, FI, GAIN, and RFI are usually singular but the method presented here does not suffer from the estimation problems associated with defining these traits individually before the genetic analysis. All the results are thus estimated simultaneously, and the set of parameters is consistent.

Genetic evaluation including intermediate omics features.

In animal and plant breeding and genetics, there has been an increasing interest in intermediate omics traits, such as metabolomics and transcriptomics, which mediate the effect of genetics on the phenotype of interest. For inclusion of such intermediate traits into a genetic evaluation system, there is a need for a statistical model that integrates phenotypes, genotypes, pedigree, and omics traits, and a need for associated computational methods that provide estimated breeding values. In this paper, a joint model for phenotypes and omics data is presented, and a formula for the breeding values on individuals is derived. For complete omics data, three equivalent methods for best linear unbiased prediction of breeding values are presented. In all three cases, this requires solving two mixed model equation systems. Estimation of parameters using restricted maximum likelihood is also presented. For incomplete omics data, extensions of two of these methods are presented, where in both cases, the extension consists of extending an omics-related similarity matrix to incorporate individuals without omics data. The methods are illustrated using a simulated data set.

The utility of genomic prediction models in evolutionary genetics.

Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.

Impact of including the cause of missing records on genetic evaluations for growth in commercial pigs.

It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.

Prediction in High-Dimensional Linear Models and Application to Genomic Selection Under Imperfect Linkage Disequilibrium

Abstract Genomic selection (GS) consists in predicting breeding values of selection candidates, using a large number of genetic markers. An important question in GS is to determine the number of markers required for a good prediction. For this purpose, we introduce new proxies for the accuracy of the prediction. These proxies are suitable under sparse genetic map where it is likely to observe some imperfect linkage disequilibrium, that is, the situation where the alleles at a gene location and at a marker located nearby vary. Moreover, our suggested proxies are helpful for designing cost-effective SNP chips based on a moderate density of markers. We analyse rice data from Los Banos, Philippines and focus on the flowering time collected during the dry season 2012. Using different densities of markers, we show that at least 1553 markers are required to implement GS. Finding the optimal number of markers is crucial in order to optimize the breeding program.

Simulation studies to optimize genomic selection in honey bees

BackgroundWith the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees.MethodsStochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ.ResultsIncluding the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required.ConclusionsThis study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

Reliabilities of Genomic Prediction for Young Stock Survival Traits Using 54K SNP Chip Augmented With Additional Single-Nucleotide Polymorphisms Selected From Imputed Whole-Genome Sequencing Data.

This study investigated effects of integrating single-nucleotide polymorphisms (SNPs) selected based on previous genome-wide association studies (GWASs), from imputed whole-genome sequencing (WGS) data, in the conventional 54K chip on genomic prediction reliability of young stock survival (YSS) traits in dairy cattle. The WGS SNPs included two groups of SNP sets that were selected based on GWAS in the Danish Holstein for YSS index (YSS_SNPs, n = 98) and SNPs chosen as peaks of quantitative trait loci for the traits of Nordic total merit index in Denmark–Finland–Sweden dairy cattle populations (DFS_SNPs, n = 1,541). Additionally, the study also investigated the possibility of improving genomic prediction reliability for survival traits by modeling the SNPs within recessive lethal haplotypes (LET_SNP, n = 130) detected from the 54K chip in the Nordic Holstein. De-regressed proofs (DRPs) were obtained from 6,558 Danish Holstein bulls genotyped with either 54K chip or customized LD chip that includes SNPs in the standard LD chip and some of the selected WGS SNPs. The chip data were subsequently imputed to 54K SNP together with the selected WGS SNPs. Genomic best linear unbiased prediction (GBLUP) models were implemented to predict breeding values through either pooling the 54K and selected WGS SNPs together as one genetic component (a one-component model) or considering 54K SNPs and selected WGS SNPs as two separate genetic components (a two-component model). Across all the traits, inclusion of each of the selected WGS SNP sets led to negligible improvements in prediction accuracies (0.17 percentage points on average) compared to prediction using only 54K. Similarly, marginal improvement in prediction reliability was obtained when all the selected WGS SNPs were included (0.22 percentage points). No further improvement in prediction reliability was observed when considering random regression on genotype code of recessive lethal alleles in the model including both groups of the WGS SNPs. Additionally, there was no difference in prediction reliability from integrating the selected WGS SNP sets through the two-component model compared to the one-component GBLUP.

Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill.

Single-step GBLUP (HBLUP) efficiently combines genomic, pedigree, and phenotypic information for holistic genetic analyses of disjunct breeding populations. We combined data from two independent multigenerational Eucalyptus globulus breeding populations to provide direct comparisons across the programs and indirect predictions in environments where pedigreed families had not been evaluated. Despite few known pedigree connections between the programs, genomic relationships provided the connectivity required to create a unified relationship matrix, H, which was used to compare pedigree-based and HBLUP models. Stem volume data from 48 sites spread across three regions of southern Australia and wood quality data across 20 sites provided comparisons of model accuracy. Genotyping proved valuable for correcting pedigree errors and HBLUP more precisely defines relationships within and among populations, with relationships among the genotyped individuals used to connect the pedigrees of the two programs. Cryptic relationships among the native range populations provided evidence of population structure and evidence of the origin of landrace populations. HBLUP across programs improved the prediction accuracy of parents and genotyped individuals and enabled breeding value predictions to be directly compared and inferred in regions where little to no testing has been undertaken. The impact of incorporating genetic groups in the estimation of H will further align traditional genetic evaluation pipelines with approaches that incorporate marker-derived relationships into prediction models.