Accuracy Of Genomic Estimated Breeding Values Research Articles

Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.)

BackgroundGenomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively.ResultsLarge phenotypic variations for the fruit traits were found in these collections and the 51K Axiom™ SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594–0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780–0.835), fruit width (0.791–0.865), and pericarp thickness (0.643–0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768−12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets.ConclusionOur study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars.

Genomic selection for target traits in the Australian lentil breeding program.

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.

Effect of the number of QTL of carcass weight on the accuracy of genomic estimated breeding values in Hanwoo

The objective of this study was to investigate the relationship between the number of QTL (Quantitative Trait Loci) affecting the carcass weight of Hanwoo and the accuracy of GEBV (Genomic Estimated Breeding Values). The study was performed simulation study to generate three populations comprising 100, 500 and 1,000 QTL. Each population consisted of 13,500 individuals which were selected from the 6th to 10th generations of all 10 generations, while keeping litter size constant (N=1) in population parameters. The genetic parameters for these populations were set at 0.45 trait heritability, 0.3 QTL heritability and 48,578 total markers. In order to confirm the number of significant QTL under the threshold of Bonferroni correction, both GWAS (Genome-wide association study) and GWAS based on Random Forest (RF) were performed. In each population, there were 33, 50 and 32 QTL identified by only GWAS, with 100, 500 and 1,000 QTL. Additionally, RF-based GWAS detected 65, 260 and 491. These results showed that the number of significant QTL is not related to the overall number of QTL in the population. Furthermore, machine learning based GWAS is more accurate than solely using GWAS. To estimate the accuracy of GEBV (Genomic Best Linear Unbiased Prediction) in each population, 5-fold cross-validation was employed using SNP effect of the QTL over p-value. In the result of the estimation, GEBV rose with an increase in the number of QTL in the population, and the correlation between TBV (True Breeding Values) followed the same tendency. As a result, this study suggested that the number of QTL related to carcass weight in Hanwoo could exert a significant influence on genetic improvement.

Effect of population structure on the accuracy of genomic prediction for carcass traits in Hanwoo cattle: a simulation study

Improvement of quantitative traits in livestock is an essential goal of animal breeding. For the achievement of this goal, an accurate breeding value estimation is needed. The genetic relationship between reference and test groups is a key factor in determining the accuracy of genomic estimated breeding value (GEBV) thus, the structure of the reference population is crucial for efficient genomic selection. By the number of sharing parents, the population structure can be divided into half-sibling and full-sibling families. Also, the population structure of Hanwoo cattle primarily consists of half-sibling families because of the production system. Therefore, comparing half-sibling and full-sibling families is challenging in the Hanwoo cattle population, yet an important issue in the direction of breeding strategy in Korea. The objective of this study was to compare the accuracy of GEBV between different family structures and investigate efficient family size in the reference population using simulated data. 6 different populations were simulated using QMSim software, and the individuals in the last generations were separated into reference and test groups. The GEBV was calculated using BLUPF90 software. Practical accuracy was between 0.36-0.52 in half-sibling families and 0.55-0.77 in full-sibling families. The increase rate of accuracy was highest at the sibling size of 20, with practical accuracy of 0.52 in half-sibling families and 0.77 in full-sibling families. As a result, the most efficient population structure for genomic prediction was a sibling size of 20 in a full-sibling family.

Using genomic selection to improve the accuracy of genomic prediction for multi-populations in pigs

The size of the reference group is among the most critical determinants of genomic estimated breeding values (GEBVs) accuracy. However, small- and medium-sized pig farms often need help accumulating adequate reference data, posing significant challenges to breeding programs. To solve this problem, exploring the potential benefits of combining reference groups of different sizes is necessary to improve GEBV accuracy. The primary objective of this investigation was to assess a more effective statistical model for combined multi-populations and its potential to enhance the accuracy of GEBVs for small and medium populations. Three populations were simulated using the QMSim software, each consisting of different sizes (300, 600, and 1 500, respectively). To assess the impact of heritability on the accuracy of GEBVs, four different levels of heritability (0.05, 0.15, 0.35, and 0.5) were simulated. Simultaneously, to investigate the impact of kinship on multi-populations, the study created four distinct scenarios for the three sizes of populations. These scenarios included: (1) the three groups are all independent, (2) the large group and the small group with a familial connection (n = 1 800), a middle group (n = 600) acting independently with no kinship, (3) the large group with a familial connection to the middle group (n = 2 100) but no connection to the small group (n = 300), and (4) the small group with a familial connection to the middle group (n = 900), while the large group (n = 1 500) acted independently with no kinship. This study evaluates and compares the accuracy of predicting breeding values using four different methods, including genomic best linear unbiased prediction (GBLUP), single-stepGBLUP (ssGBLUP), and two Bayesian models (Bayes A and Bayes B), with varying sizes of reference groups. In each scenario, three different prediction strategies were compared: (1) Merging all three different sizes of populations for predicting, (2) predicting each independent population separately, and (3) the other two populations predict the population. Our findings reveal that combining populations enhances the Bayesian models, with Bayes B yielding the highest accuracy. In independent populations, the best linear unbiased prediction (BLUP) models demonstrated the highest accuracy. However, in cases where populations were related and the heritability was high, the Bayes B model exhibited the highest overall accuracy (slightly higher than BLUP models) in the independent population. Our results underscore the importance of considering population combinations when using genetic models to predict breeding values, particularly for pig farmers with limited resources.

Imputation strategies for genomic prediction using nanopore sequencing

BackgroundGenomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies’ (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance.ResultsSNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays.ConclusionsHere we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.

Wagyu Feeder Check: A genomic-based tool to identify performance differences of Australian Wagyu and Wagyu crossed cattle

Context Wagyu Feeder Check is a genomic-based tool designed to provide genomic estimated breeding values (GEBV) for five feedlot growth and carcase traits. At present, Wagyu Feeder Check is based on a reference population of 8316 genotyped and phenotyped Australian fullblood (FB; N = 2120) Wagyu and Wagyu-crossed (XB; N = 6196) cattle, principally Wagyu × Angus F1 animals. Aims We provide technical details behind the development of the Wagyu Feeder Check and validate the ability of its GEBV to predict differences in performance of Wagyu cattle in daily weight gain at feedlot, carcase weight, carcase eye muscle area, carcase marbling score and carcase rump fat at the P8 site. Methods Data supplied from eight commercial supply chains across Australia was used to generate GEBV using mixed-model equations that incorporated a genomic relationship matrix build with 82 504 autosomal markers. The bias, dispersion, and accuracy of the GEBV were evaluated using a four-way cross-validation scheme where, in each turn, the phenotypes from a random 1549 (or 25%) XB cattle were set as missing. Key results The genomic estimate of the Wagyu content in the FB and XB population averaged 99.12% and 59.55%, respectively, and with most of the non-Wagyu content associated with Angus. The estimates of heritability (± s.e.) were 0.497 ± 0.016, 0.474 ± 0.004, 0.347 ± 0.014, 0.429 ± 0.003 and 0.422 ± 0.003 for daily weight gain at feedlot, carcase weight, eye muscle area, marbling and rump fat, respectively. Averaged across the four XB validation populations, the accuracy of GEBV was 0.624, 0.634, 0.385, 0.620, and 0.526 for the same set of traits. Conclusions Genomic predictions generated by Wagyu Feeder Check can predict differences in feedlot and carcase performance of Australian Wagyu cattle. Given the large content of Angus in the XB population, further research is required to determine the predictive ability of GEBV in Wagyu × Bos indicus and Wagyu × dairy animals. Implications Commercial feedlot operators finishing animals with a strong Wagyu breed component will benefit from using Wagyu Feeder Check for decision making.

Multi-breed genomic evaluation for tropical beef cattle when no pedigree information is available

BackgroundIt has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models.ResultsTreating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30–0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented.ConclusionsWhen no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.

Restriction site-associated DNA sequencing technologies as an alternative to low-density SNP chips for genomic selection: a simulation study in layer chickens

BackgroundTo reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line.ResultsGenome reduction and sequencing fragments were identified on reference genome using four restriction enzymes (EcoRI, TaqI, AvaII and PstI) and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent.ConclusionsRADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered.

Genomic evaluation of feed efficiency in US Holstein heifers

There is growing interest in improving feed efficiency (FE) traits in dairy cattle. The objectives of this study were to estimate the genetic parameters of RFI and its component traits (Dry matter intake (DMI), metabolic body weight (MBW) and average daily gain (ADG)) of Holstein heifers, and to develop a system for genomic evaluation for RFI in Holstein dairy calves. The RFI data were collected from 6,563 growing Holstein heifers (initial BW = 261 ± 52 kg; initial age = 266 ± 42 d) for 70 d, across 182 trials conducted between 2014 and 2022 at the STgenetics Ohio Heifer Center (South Charleston, Ohio) as part of EcoFeed® program which aims to improve FE by genetic selection. RFI was estimated as the difference between a heifer's actual feed intake and its expected feed intake, which was determined by regression of DMI against mid-point MBW, age, and ADG across each trial. A total of 61,283 single nucleotide polymorphisms were used in genomic analyses. Animals with phenotypes and genotypes were used as training population and 4 groups of prediction population, each with 2,000 animals, were selected from a pool of Holstein animals with genotypes, based on their relationship with the training population. All traits were analyzed using univariate animal model in DMU version 6 software. Pedigree information and genomic information were used to specify genetic relationships to estimate the variance components and genomic estimated breeding values (GEBVs), respectively. Breeding values of prediction population were estimated by using the 2-step approach, i.e., deriving the prediction equation of GEBVs from training population for the estimation of GEBVs of prediction population with only genotypes. Reliability of breeding values were obtained by approximation based on partitioning a function of accuracy of training population GEBVs and magnitudes of genomic relationships between individuals in the training and prediction population. Heifers had DMI (mean ± SD) of 8.11 ± 1.59 kg over the period of trial with growth rate of 1.08 ± 0.25 kg/d. The heritability estimates (mean ± SE) of RFI, MBW, DMI, and growth rate were 0.24 ± 0.02, 0.23 ± 0.02, 0.27 ± 0.02, and 0.19 ± 0.02, respectively. The range of genomic predicted transmitted abilities (gPTAs) of training population (-0.94 to 0.75) was higher compared with the range of gPTAs (-0.82 to 0.73) of different groups of prediction population. Average reliability of breeding values from training population was 58% and that of prediction population was 39%. The genomic prediction of RFI provided new tools to select for feed efficiency of heifers. Future research should be directed to find the relationship between RFI of heifers and cows, to select individuals based on their lifetime production efficiencies.

Imputation accuracy for genomic selection using embryo biopsy samples in Gir

The aim of this study was to establish a platform for genomic selection of in vitro-fertilized (IVF) Gir embryos. Multiple displacement amplification (MDA)-based embryo biopsy samples were genotyped, and genomic estimated breeding values (GEBV) for milk yield (305MY) were calculated. The concordance of GEBV and accuracy between embryo biopsies and the respective liveborn were assessed. Imputation was performed using two panels (Z-Chip and Bovine HD, Illumina) based on a database of 73,110 lactating cow’s database and pedigree files from 147,131 animals. Biopsied embryos had similar pregnancy rates (39% vs 40%), pregnancy loss rates (18% vs 20%), and pregnancy length compared to Control embryos. After genotyping, low call rate means were detected for biopsy samples compared to the respective calf samples (0.80 vs 0.98). Imputation presented 0.83 (Z-Chip) and 0.96 (HD) accuracy (CORRanim). Embryo GEBV accuracy levels were higher in BovineHD imputation (0.82) than Z-Chip imputation (0.55) or no imputation (0.62), and the correlation between embryo/calf pairs’ accuracy was 0.85 for BovineHD imputation, 0.11 for Z-Chip imputation, and 0.02 for no imputation. GEVB estimates correlation between embryo/calf pairs was 0.87 for BovineHD imputation, 0.80 for Z-Chip imputation, and 0.41 before imputation. The call rate of embryo samples did not affect the correlation between embryo/calf pairs for accuracy and GEBV before and after BovineHD imputation. Embryos obtained on the same farm presented GEBV 305MY differences of up to 800 kg, emphasizing the expected impact of embryo genomic selection for the Gir breed.

Comparison on genomic prediction using pedigree BLUP and single step GBLUP through the Hanwoo full-sib family.

When evaluating individuals with the same parent and no phenotype by pedigree best linear unbiased prediction (BLUP), it is difficult to explain carcass grade difference and select individuals because they have the same value in pedigree BLUP (PBLUP). However, single step GBLUP (ssGBLUP), which can estimate the breeding value suitable for the individual by adding genotype, is more accurate than the existing method. The breeding value and accuracy were estimated with pedigree BLUP and ssGBLUP using pedigree and genotype of 408 Hanwoo cattle from 16 families with the same parent among siblings produced by fertilized egg transplantation. A total of 14,225 Hanwoo cattle with pedigree, genotype and phenotype were used as the reference population. PBLUP obtained estimated breeding value (EBV) using the pedigree of the test and reference populations, and ssGBLUP obtained genomic EBV (GEBV) after constructing and H-matrix by integrating the pedigree and genotype of the test and reference populations. For all traits, the accuracy of GEBV using ssGBLUP is 0.18 to 0.20 higher than the accuracy of EBV obtained with PBLUP. Comparison of EBV and GEBV of individuals without phenotype, since the value of EBV is estimated based on expected values of alleles passed down from common ancestors. It does not take Mendelian sampling into consideration, so the EBV of all individuals within the same family is estimated to be the same value. However, GEBV makes estimating true kinship coefficient based on different genotypes of individuals possible, so GEBV that corresponds to each individual is estimated rather than a uniform GEBV for each individual. Since Hanwoo cows bred through embryo transfer have a high possibility of having the same parent, if ssGBLUP after adding genotype is used, estimating true kinship coefficient corresponding to each individual becomes possible, allowing for more accurate estimation of breeding value.

Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole-genome SNP chip panels.

The objective of the present study was to evaluate the breeding value and accuracy of genomic estimated breeding values (GEBVs) of carcass traits in Jeju Black cattle (JBC) using Hanwoo steers and JBC as a reference population using the single-trait animal model. Our research included genotype and phenotype information on 19,154 Hanwoo steers with 1097 JBC acting as the reference population. Likewise, the test population consisted of 418 genotyped JBC individuals with no phenotypic records for those carcass traits. For estimating the accuracy of GEBV, we divided the entire population into three groups. Hanwoo and JBC make up the first group; Hanwoo and JBC, who has both the genotype and phenotypic records, are referred to as the reference (training) population, and JBC, who lacks phenotypic information is referred to as the test (validation) population. The second group consists of the JBC (without phenotype) as the test population and Hanwoo as a reference population with phenotype and genotypic data. The only JBCs in the third group are those who have genotypic and phenotypic data on them as a reference population but no phenotypic data on them as a test population. The single-trait animal model was used in all three groups for statistical purposes. The reference populations estimated heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) as 0.30, 0.26, 0.26, and 0.34 for the Hanwoo steer and 0.42, 0.27, 0.26, and 0.48 for JBC. The average accuracy for carcass traits in Group 1 was 0.80 for the Hanwoo and JBC reference population compared with 0.73 for the JBC test population. Although the average accuracy for carcass traits in Group 2 was 0.80, it was 0.80 for the Hanwoo reference population and only 0.56 for the JBC test population. The average accuracy for the JBC reference and test populations was 0.68 and 0.50, respectively, when they were included in the accuracy comparison without the Hanwoo reference population. Groups 1 and 2 used Hanwoo as reference population, which led to a better average accuracy; however, Group 3 only used the JBC reference and test population, which led to a lower average accuracy. This might be due to the fact that Group 3 used a smaller reference size than the group that came before it and that the genetic makeup of the Hanwoo and JBC breeds differed. The GEBV accuracy for MS was higher than that of other traits across all three analysis groups, followed by CWT, EMA, and BF, which may be partially explained by the MS traits' higher heritability. This study suggests that in order to achieve more accuracy, a large reference population particular to a breed should be established. Therefore, to increase the accuracy of GEBV prediction and the genetic benefit from genomic selection in JBC, individual reference breeds, and large populations are required.

Genetic evaluations for endangered dual-purpose German Black Pied cattle using 50K SNPs, a breed-specific 200K chip, and whole-genome sequencing

Genetic evaluations of local cattle breeds are hampered due to small reference groups or biased due to the utilization of SNP effects estimated in other large populations. Against this background, there is a lack of studies addressing the possible advantage of whole-genome sequences (WGS) or consideration of specific variants from WGS data in genomic predictions for local breeds with small population size. Consequently, the aim of this study was to compare genetic parameters and accuracies of genomic estimated breeding values (GEBV) for 305-d production traits, fat-to protein ratio (FPR), and somatic cell score (SCS) at the first test date after calving and confirmation traits of the endangered German Black Pied cattle (DSN) breed using 4 different marker panels: (1) the commercial 50K Illumina BovineSNP50 BeadChip, (2) a customized 200K chip designed for DSN (DSN200K) which considers the most important variants for DSN from WGS, (3) randomly generated 200K chips based on WGS data, and (4) a WGS panel. The same number of animals was considered for all marker panel analyses (i.e., 1,811 genotyped or sequenced cows for conformation traits, 2,383 cows for lactation production traits, and 2,420 cows for FPR and SCS). Mixed models for the estimation of genetic parameters directly included the respective genomic relationship matrix from the different marker panels plus the trait-specific fixed effects. For the calculation of GEBV accuracies, we applied repeated random subsampling validation. In the process of separate cross-validations per trait, we created a validation set including 20% of cows with masked phenotypes, and a training set comprising 80% of the cows. The cows were selected randomly in a procedure with 10 replicates considering replacements in the different scenarios. The accuracy was defined as the correlation between the direct GEBV and the phenotypes with subtracted corresponding fixed effects for the cows in the validation set. For FPR and SCS, as well as for lactation production traits, heritabilities were largest based on WGS data, but the increase compared with the 50K or DSN200K applications was quite small in the range from 0.01 to 0.03. Also, for most of the conformation traits, heritabilities were largest based on WGS and DSN200K data, but the increase was in the range of the corresponding standard error. Accordingly, GEBV accuracies for most of the studied traits were highest based on WGS data or when utilizing the DSN200K chip, but the accuracy differences across the marker panels were quite small and nonsignificant. In conclusion, WGS data and the DSN200K chip only contributed to minor improvements in genomic predictions, still justifying the use of the commercial 50K chip. Nevertheless, WGS and the 200KDSN chip harbor breed-specific variants, which are valuable for studying causal genetic mechanisms in the endangered DSN population.

Validation with single-step SNPBLUP shows that evaluations can continue using a single mean of genotyped individuals, even with multiple breeds

BackgroundIn genomic prediction, it is common to centre the genotypes of single nucleotide polymorphisms based on the allele frequencies in the current population, rather than those in the base generation. The mean breeding value of non-genotyped animals is conditional on the mean performance of genotyped relatives, but can be corrected by fitting the mean performance of genotyped individuals as a fixed regression. The associated covariate vector has been referred to as a ‘J-factor’, which if fitted as a fixed effect can improve the accuracy and dispersion bias of sire genomic estimated breeding values (GEBV). To date, this has only been performed on populations with a single breed. Here, we investigated whether there was any benefit in fitting a separate J-factor for each breed in a three-way crossbred population, and in using pedigree-based expected or genome-based estimated breed fractions to define the J-factors.ResultsFor body weight at 7 days, dispersion bias decreased when fitting multiple J-factors, but only with a low proportion of genotyped individuals with selective genotyping. On average, the mean regression coefficients of validation records on those of GEBV increased with one J-factor compared to none, and further increased with multiple J-factors. However, for body weight at 35 days this was not observed. The accuracy of GEBV remained unchanged regardless of the J-factor method used. Differences between the J-factor methods were limited with correlations approaching 1 for the estimated covariate vector, the estimated coefficients of the regression on the J-factors, and the GEBV.ConclusionsBased on our results and in the particular design analysed here, i.e. all the animals with phenotype are of the same type of crossbreds, fitting a single J-factor should be sufficient, to reduce dispersion bias. Fitting multiple J-factors may reduce dispersion bias further but this depends on the trait and genotyping rate. For the crossbred population analysed, fitting multiple J-factors has no adverse consequences and if this is done, it does not matter if the breed fractions used are based on the pedigree-expectation or the genomic estimates. Finally, when GEBV are estimated from crossbred data, any observed bias can potentially be reduced by including a straightforward regression on actual breed proportions.

Selective genotyping strategies for a sib test scheme of a broiler breeder program

BackgroundIn broiler breeding, genotype-by-environment interaction is known to result in a genetic correlation between body weight measured in bio-secure and commercial environments that is substantially less than 1. Thus, measuring body weights on sibs of selection candidates in a commercial environment and genotyping them could increase genetic progress. Using real data, the aim of this study was to evaluate which genotyping strategy and which proportion of sibs placed in the commercial environment should be genotyped to optimize a sib-testing breeding program in broilers. Phenotypic body weight and genomic information were collected on all sibs raised in a commercial environment, which allowed to retrospectively analyze different sampling strategies and genotyping proportions.ResultsAccuracies of genomic estimated breeding values (GEBV) obtained with the different genotyping strategies were assessed by computing their correlation with GEBV obtained when all sibs in the commercial environment were genotyped. Results showed that, compared to random sampling (RND), genotyping sibs with extreme phenotypes (EXT) resulted in higher GEBV accuracy across all genotyping proportions, especially for genotyping proportions of 12.5% or 25%, which resulted in correlations of 0.91 vs 0.88 for 12.5% and 0.94 vs 0.91 for 25% genotyped. Including pedigree on birds with phenotype in the commercial environment that were not genotyped increased accuracy at lower genotyping proportions, especially for the RND strategy (correlations of 0.88 vs 0.65 at 12.5% and 0.91 vs 0.80 at 25%), and a smaller but still substantial increase in accuracy for the EXT strategy (0.91 vs 0.79 for 12.5% and 0.94 vs 0.88 for 25% genotyped). Dispersion bias was virtually absent for RND if 25% or more birds were genotyped. However, GEBV were considerably inflated for EXT, especially when the proportion genotyped was low, which was further exacerbated if the pedigree of non-genotyped sibs was excluded.ConclusionsWhen less than 75% of all animals placed in a commercial environment are genotyped, it is recommended to use the EXT strategy, because it yields the highest accuracy. However, caution should be taken when interpreting the resulting GEBV because they will be over-dispersed. When 75% or more of the animals are genotyped, random sampling is recommended because it yields virtually no bias of GEBV and results in similar accuracies as the EXT strategy.

Multi-breed genomic predictions and functional variants for fertility of tropical bulls.

Worldwide, most beef breeding herds are naturally mated. As such, the ability to identify and select fertile bulls is critically important for both productivity and genetic improvement. Here, we collected ten fertility-related phenotypes for 6,063 bulls from six tropically adapted breeds. Phenotypes were comprised of four bull conformation traits and six traits directly related to the quality of the bull's semen. We also generated high-density DNA genotypes for all the animals. In total, 680,758 single nucleotide polymorphism (SNP) genotypes were analyzed. The genomic correlation of the same trait observed in different breeds was positive for scrotal circumference and sheath score on most breed comparisons, but close to zero for the percentage of normal sperm, suggesting a divergent genetic background for this trait. We confirmed the importance of a breed being present in the reference population to the generation of accurate genomic estimated breeding values (GEBV) in an across-breed validation scenario. Average GEBV accuracies varied from 0.19 to 0.44 when the breed was not included in the reference population. The range improved to 0.28 to 0.59 when the breed was in the reference population. Variants associated with the gene HDAC4, six genes from the spermatogenesis-associated (SPATA) family of proteins, and 29 transcription factors were identified as candidate genes. Collectively these results enable very early in-life selection for bull fertility traits, supporting genetic improvement strategies currently taking place within tropical beef production systems. This study also improves our understanding of the molecular basis of male fertility in mammals.

On the value of adding commercial data into the reference population of the Angus SteerSELECT genomic tool

Context Angus SteerSELECT is a genomic tool designed to provide genomic estimated breeding values (GEBV) for nine traits related to growth, feedlot performance, carcase characteristics and immune competence. At present, GEBV for carcase characteristics are based on a reference population of 3766 Australian Angus steers. Aims We aimed to investigate the potential benefit of incorporating commercial data into the existing reference population of the Angus SteerSELECT. To this aim, we employ a population of 2124 genotyped commercial Angus steers with carcase performance data from four commercial feedlot operators. Methods The benefit of incorporating the commercial data (COMM) into the reference (REFE) population was assessed in terms of quality and integrity of the COMM data and meta-data to model the phenotypes adequately. We computed bias, dispersion, and accuracy of GEBV for carcase weight (CWT) and marbling (MARB) before and after including the COMM data, in whole or in partial, into the REFE population. Key results The genomic estimate of the Angus content in the COMM population averaged 96.9% and ranged from 32.87% to 100%. For CWT, the estimates of heritability were 0.419 ± 0.026 and 0.368 ± 0.038 for the REFE and COMM populations respectively, and with a genetic correlation of 0.756 ± 0.068. For MARB, the same three parameter estimates were 0.357 ± 0.027, 0.340 ± 0.038 and 0.879 ± 0.073 respectively. The ACC of CWT GEBV increased significantly (P < 0.0001) from 0.475 when the COMM population was not part of the REFE to 0.546 (or 15%) when a random 50% of the COMM population was included in the REFE. Similarly significant increases in ACC were observed for MARB GEBV (0.470–0.521 or 11%). Conclusions The strong genomic relationship between the REFE and the COMM populations, coupled with the significant increases in GEBV accuracies, demonstrated the potential benefits of including the COMM population into the reference population of a future improved version of the Angus SteerSELECT genomic tool. Implications Commercial feedlot operators finishing animals with a strong Angus breed component will benefit from having their data represented in the reference population of the Angus SteerSELECT genomic tool.

Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle

Context Feed is the largest expense on a dairy farm, therefore improving feed efficiency is important. Recording dry-matter intake (DMI) is a prerequisite for calculating feed efficiency. Genetic variation of feed intake and feed efficiency varies across lactation stages and parities. DMI is an expensive and difficult-to-measure trait. This raises the question of which time periods during lactation would be most appropriate to measure DMI. Aims The aim was to evaluate whether sequence variants selected from genome-wide association studies (GWAS) for DMI recorded at multiple lactation time periods and parities would increase the accuracy of genomic estimated breeding values (GEBVs) for DMI and residual feed intake (RFI). Methods Data of 2274 overseas lactating cows were used for the GWAS to select sequence variants. GWAS was performed using the average of the DMI phenotypes in a 30-day window of six different time periods across the lactation. The most significant sequence variants were selected from the GWAS at each time period for either first or later parities. GEBVs for DMI and RFI in Australian lactating cows were estimated using BayesRC with 50 k single nucleotide polymorphisms (SNPs) and selected GWAS sequence variants. Key results There were differences in DMI genomic correlations and heritabilities between first and later parities and within parity across lactation time periods. Compared with using 50 k single-nucleotide polymorphisms (SNPs) only, the accuracy of DMI GEBVs increased by up to 11% by using the 50 k SNPs plus the selected sequence variants. Compared with DMI, the increase in accuracy for RFI was lower (by 6%) likely because the sequence variants were selected from GWAS for DMI not RFI. The accuracies for DMI and RFI GEBVs were highest by using selected sequence variants from the DMI GWAS in the mid- to late-lactation periods in later parity. Conclusions Our results showed that DMI phenotypes in late lactation time periods could capture more genetic variation and increase genomic prediction accuracy through the use of custom genotype panels in genomic selection. Implications Collecting DMI at the optimal time period(s) of lactation may help develop more accurate and cost-effective breeding values for feed efficiency in dairy cattle.

Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP

BackgroundAlthough single-step GBLUP (ssGBLUP) is an animal model, SNP effects can be backsolved from genomic estimated breeding values (GEBV). Predicted SNP effects allow to compute indirect prediction (IP) per individual as the sum of the SNP effects multiplied by its gene content, which is helpful when the number of genotyped animals is large, for genotyped animals not in the official evaluations, and when interim evaluations are needed. Typically, IP are obtained for new batches of genotyped individuals, all of them young and without phenotypes. Individual (theoretical) accuracies for IP are rarely reported, but they are nevertheless of interest. Our first objective was to present equations to compute individual accuracy of IP, based on prediction error covariance (PEC) of SNP effects, and in turn, are obtained from PEC of GEBV in ssGBLUP. The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies.ResultsCorrelations between GEBV and IP for the validation animals using SNP effects from ssGBLUP evaluations were ≥ 0.99. When all available genotyped animals were used for PEC computations, correlations between GEBV and IP accuracy were ≥ 0.99. In addition, IP accuracies were compatible with GEBV accuracies either with direct inversion of the genomic relationship matrix (G) or using the algorithm for proven and young (APY) to obtain the inverse of G. As the number of genotyped animals included in the PEC computations decreased from around 55,000 to 15,000, correlations were still ≥ 0.96, but IP accuracies were biased downwards.ConclusionsTheoretical accuracy of indirect prediction can be successfully obtained by computing SNP PEC out of GEBV PEC from ssGBLUP equations using direct or APY G inverse. It is possible to reduce the number of genotyped animals in PEC computations, but accuracies may be underestimated. Further research is needed to approximate SNP PEC from ssGBLUP to limit the computational requirements with many genotyped animals.