Accuracy Of Genomic Evaluation Research Articles

Genomic evaluation of threshold traits in different scenarios of threshold number using parametric and non-parametric statistical methods

AbstractThe aim was to study the effect of the threshold number on the accuracy of genomic evaluation of the threshold traits using support vector machine (SVM), genomic best linear unbiased prediction (GBLUP) and Bayesian method B (BayesB). For this purpose, a genome consisting of three chromosomes was simulated for 1000 individuals on which 3000 bi-allelic single nucleotide polymorphism markers were evenly distributed. Genomic breeding values were predicted in different scenarios of threshold number (1–6 thresholds), QTL number (30 and 300 QTLs) and heritability level (0.1, 0.3 and 0.5). By increasing the number of thresholds from 1 to 6 thresholds, especially at higher levels of heritability, the accuracy of genomic evaluation increased; however, the increase in accuracy was not linear so that it was much more noticeable when the number of thresholds increased from 1 to 2 thresholds. In the most studied scenarios, SVM showed a very poor performance compared to other methods. BayesB ranked first regarding prediction accuracy, though in some cases the observed differences with GBLUP was not significant. While increase in heritability increased the accuracy of genomic evaluation, change in the QTL number had a slight effect on the prediction accuracy. According to the results, the SVM is not recommended for genomic evaluation of threshold traits, especially those which have only one threshold and instead, use of GBLUP and BayesB is recommended. For traits with more than one threshold, fortunately we can achieve accuracy similar to continuous traits by applying traditional genomic evaluation methods.

Genome-Wide Association Analysis Reveals Novel Loci Related with Visual Score Traits in Nellore Cattle Raised in Pasture-Based Systems.

Body conformation traits assessed based on visual scores are widely used in Zebu cattle breeding programs. The aim of this study was to identify genomic regions and biological pathways associated with body conformation (CONF), finishing precocity (PREC), and muscling (MUSC) in Nellore cattle. The measurements based on visual scores were collected in 20,807 animals raised in pasture-based systems in Brazil. In addition, 2775 animals were genotyped using a 35 K SNP chip, which contained 31,737 single nucleotide polymorphisms after quality control. Single-step GWAS was performed using the BLUPF90 software while candidate genes were identified based on the Ensembl Genes 69. PANTHER and REVIGO platforms were used to identify key biological pathways and STRING to create gene networks. Novel candidate genes were revealed associated with CONF, including ALDH9A1, RXRG, RAB2A, and CYP7A1, involved in lipid metabolism. The genes associated with PREC were ELOVL5, PID1, DNER, TRIP12, and PLCB4, which are related to the synthesis of long-chain fatty acids, lipid metabolism, and muscle differentiation. For MUSC, the most important genes associated with muscle development were SEMA6A, TIAM2, UNC5A, and UIMC1. The polymorphisms identified in this study can be incorporated in commercial genotyping panels to improve the accuracy of genomic evaluations for visual scores in beef cattle.

Detection of Genomic Imprinting for Carcass Traits in Cattle Using Imputed High-Density Genotype Data.

Genomic imprinting is an epigenetic phenomenon defined as the silencing of an allele, at least partially, at a given locus based on the sex of the transmitting parent. The objective of the present study was to detect the presence of SNP-phenotype imprinting associations for carcass weight (CW), carcass conformation (CC) and carcass fat (CF) in cattle. The data used comprised carcass data, along with imputed, high-density genotype data on 618,837 single nucleotide polymorphisms (SNPs) from 23,687 cattle; all animal genotypes were phased with respect to parent of origin. Based on the phased genotypes and a series of single-locus linear models, 24, 339, and 316 SNPs demonstrated imprinting associations with CW, CC, and CF, respectively. Regardless of the trait in question, no known imprinted gene was located within 0.5 Mb of the SNPs demonstrating imprinting associations in the present study. Since all imprinting associations detected herein were at novel loci, further investigation of these regions may be warranted. Nonetheless, knowledge of these associations might be useful for improving the accuracy of genomic evaluations for these traits, as well as mate allocations systems to exploit the effects of genomic imprinting.

صحت ارزیابی ژنومی با در نظر گرفتن اثر متقابل بین روش آماری برآورد آثار نشانگری، ساختار جمعیت و معماری ژنتیکی صفت

This study aimed to investigate the interaction effects between marker effect estimation methods, population structure, and genetic architecture of the trait on the accuracy of genomic evaluations. A reference population with two different effective population sizes (100 and 500) was simulated using the QMSim software. 500 markers and two different numbers of quantitative trait loci or QTLs (50 and 200) were distributed randomly through the genome including a 100 cM chromosome. In this study, three traits with different heritabilities (0.1, 0.3, and 0.5) were simulated. The genomic breeding values were predicted using Bayesian ridge regression, Bayes A, Bayes B, Bayes C, Bayesian LASSO, Reproducing kernel Hilbert space, and neural networks methods. Through the three heritabilities, as the effective population size increased, the accuracy of genomic evaluation decreased with different trends. As the number of QTLs increased, the accuracy of low heritability trait increased, but the accuracy of medium and high heritability traits decreased. Similarly, as the number of QTLs increased, the accuracy of the trait with normal distributed QTLs increased, but the accuracy of traits with gamma and univariate distributed QTLs decreased. For all types of QTL distributions, the increment of effective population size decreased the accuracy of genomic evaluations. The results of this study clearly showed the interaction effects between markers effect estimation methods, population structure, and genetic architecture of the trait on the accuracy of genomic evaluations.

Assessing the performance of a novel method for genomic selection: rrBLUP-method6

The aim of this study was to compare the predictive performance of ridge regression best linear unbiased prediction-method 6 (rrBLUPm6) with well-known genomic selection methods (rrBLUP, GBLUP and BayesA) in terms of accuracy of prediction, computing time and memory requirement. The impact of the genetic architecture and heritability on the accuracy of genomic evaluation was also studied. To this end, a genome was simulated which consisted of five chromosomes, one Morgan each, on which 5000 biallelic single-nucleotide polymorphisms (SNP) were distributed. Prediction of genomic breeding values was done in different scenarios of number of QTL (50 and 500 QTL), distribution of QTL effects (uniform, normal and gamma) and different heritability levels (0.1, 0.3 and 0.5). Pearson’s correlation between true and predicted genomic breeding values (rp,t) was used as the measure of prediction accuracy. Computing time and memory requirement were also measured for studied methods. The accuracy of rrBLUPm6 was higher than GBLUP and rrBLUP, and was comparable with BayesA. In addition, regarding computing time and memory requirement, rrBLUPm6 outperformed other methods and ranked first. A significant increase in accuracy of prediction was observed following increase in heritability. However, the number and distribution of QTL effects did not affect the accuracy of prediction significantly. As rrBLUPm6 showed a great performance regarding accuracy of prediction, computing time and memory requirement, we recommend it for genomic selection.

Population Genomics of American Mink Using Whole Genome Sequencing Data.

Characterizing the genetic structure and population history can facilitate the development of genomic breeding strategies for the American mink. In this study, we used the whole genome sequences of 100 mink from the Canadian Centre for Fur Animal Research (CCFAR) at the Dalhousie Faculty of Agriculture (Truro, NS, Canada) and Millbank Fur Farm (Rockwood, ON, Canada) to investigate their population structure, genetic diversity and linkage disequilibrium (LD) patterns. Analysis of molecular variance (AMOVA) indicated that the variation among color-types was significant (p < 0.001) and accounted for 18% of the total variation. The admixture analysis revealed that assuming three ancestral populations (K = 3) provided the lowest cross-validation error (0.49). The effective population size (Ne) at five generations ago was estimated to be 99 and 50 for CCFAR and Millbank Fur Farm, respectively. The LD patterns revealed that the average r2 reduced to <0.2 at genomic distances of >20 kb and >100 kb in CCFAR and Millbank Fur Farm suggesting that the density of 120,000 and 24,000 single nucleotide polymorphisms (SNP) would provide the adequate accuracy of genomic evaluation in these populations, respectively. These results indicated that accounting for admixture is critical for designing the SNP panels for genotype-phenotype association studies of American mink.

Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle

BackgroundRecently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations.ResultsThe accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method.ConclusionsOur findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.

Using sequence variants of a QTL region improves the accuracy of genomic evaluation in French Saanen goats

The enhanced availability of sequence data in livestock provides an opportunity for more accurate predictions in routine genomic evaluations. Such evaluations would therefore no longer rely only on the linkage disequilibrium between a chip marker and the causal mutation. The objective of this study was to assess the usefulness of sequence data in Saanen goats (n = 33) to better capture a quantitative trait locus (QTL) on chromosome 19 (CHI19) and improve the accuracy of predictions for 3 milk production traits, 5 type traits, and somatic cell scores. All 1,207 50K genotypes were imputed to the sequence level. Four scenarios, each using a subset of CHI19 imputed variants, were then tested. Sequence-derived information included all CHI19 variants (529,576), all variants in the QTL region (22,269), 178 variants selected in the QTL region and added to an updated chip, or 178 randomly selected variants on CHI19. Two genomic evaluation models were applied: single-step genomic BLUP and weighted single-step genomic BLUP. All scenarios were compared with single-step genomic BLUP using 50K genotypes. Best overall results were obtained using single-step genomic BLUP on 50K genotypes completed with all variants in the QTL region of chromosome 19 (6.2% average increase in accuracy for 9 traits) with the highest accuracy gain for fat yield (17.9%), significant increases for milk (13.7%) and protein yields (12.5%), and type traits associated with CHI19. Despite its association with the QTL region of chromosome 19, the somatic cell score showed decreased accuracy in every alternative scenario. Using all CHI19 variants led to an overall decrease of 4.8% in prediction accuracy. The updated chip was efficient and improved genomic evaluations by 3.1 to 6.4% on average, depending on the scenario. Indeed, information from only a few carefully selected variants increased accuracies for traits of interest when used in a single-step genomic BLUP model. In conclusion, using QTL region variants imputed from sequence data in single-step genomic evaluations represents a promising perspective for such evaluations in dairy goats. Furthermore, using only a limited number of selected variants in QTL regions, as available on SNP chip updates, significantly increases the accuracy for QTL-associated traits without deteriorating the evaluation accuracy for other traits. The latter approach is interesting, as it avoids time-consuming imputation and data formatting processes and provides reliable genotypes.

Marker genotyping error effects on genomic predictions under different genetic architectures.

This study aimed to determine the effect of different rates of marker genotyping error on the accuracy of genomic prediction that was examined under distinct marker and quantitative trait loci (QTL) densities and different heritability estimates using a stochastic simulation approach. For each scenario of simulation, a reference population with phenotypic and genotypic records and a validation population with only genotypic records were considered. Marker effects were estimated in the reference population, and then their genotypic records were used to predict genomic breeding values in the validation population. The prediction accuracy was calculated as the correlation between estimated and true breeding values. The prediction bias was examined by computing the regression of true genomic breeding value on estimated genomic breeding value. The accuracy of the genomic evaluation was the highest in a scenario with no marker genotyping error and varied from 0.731 to 0.934. The accuracy of the genomic evaluation was the lowest in a scenario with marker genotyping error equal to 20% and changed from 0.517 to 0.762. The unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value were obtained in the reference and validation populations when the rate of marker genotyping error was equal to zero. The results showed that marker genotyping error can reduce the accuracy of genomic evaluations. Moreover, marker genotyping error can provide biased estimates of genomic breeding values. Therefore, for obtaining accurate results it is recommended to minimize the marker genotyping errors to zero in genomic evaluation programs.

Accuracy of genomic evaluation using imputed high-density genotypes for carcass traits in commercial Hanwoo population

This study aimed to evaluate the accuracy of genomic prediction using single-trait and multi-trait single-step genomic best linear unbiased prediction (ssGBLUP) based on 50 K SNP chip dataset and imputed high-density (HD) genotypes for various carcass traits in commercial Hanwoo population such as backfat thickness (BFT), carcass weight (CWT), rib eye area (REA), and marbling score (MS). Analyses involved phenotypes from 18,269 animals born between April 2006 and June 2017 from different commercial herds in South Korea as well as genomic information from 16,892 animals genotyped using customized Hanwoo 50 K SNP chip and imputed to HD genotypes. Cross-validation was performed on 3,041 animals in the validation population born from November 2016 to June 2017. The results showed that HD genotypes led to a marginal increase in prediction accuracies (0.6 to 2%) than 50 K genotypes for BFT, REA, and MS, while no improvement was noted for CWT. Compared to the single-trait model, the use of multi-trait model based on 50 K or HD genotypes produced only a small improvement in prediction accuracies (1.2 to 2%) for BFT and REA but without improvement for CWT. For MS, the accuracy of genomic prediction using the multi-trait model based on 50 K or HD genotype datasets was slightly lower than the single-trait model. Generally, inflation of predictions increased using the HD genotypes and multi-trait model. Therefore, we recommend the use of 50 K genotypes and single-trait model in the estimation of genomic breeding values for carcass traits in commercial Hanwoo population.

Reliability of genomic evaluation for egg quality traits in layers

BackgroundGenomic evaluation, based on the use of thousands of genetic markers in addition to pedigree and phenotype information, has become the standard evaluation methodology in dairy cattle breeding programmes over the past several years. Despite the many differences between dairy cattle breeding and poultry breeding, genomic selection seems very promising for the avian sector, and studies are currently being conducted to optimize avian selection schemes. In this optimization perspective, one of the key parameters is to properly predict the accuracy of genomic evaluation in pure line layers.ResultsIt was observed that genomic evaluation, whether performed on males or females, always proved more accurate than genetic evaluation. The gain was higher when phenotypic information was narrowed, and an augmentation of the size of the reference population led to an increase in accuracy prediction with regard to genomic evaluation. By taking into account the increase of selection intensity and the decrease of the generation interval induced by genomic selection, the expected annual genetic gain would be higher with ancestry-based genomic evaluation of male candidates than with genetic evaluation based on collaterals. This advantage of genomic selection over genetic selection requires more detailed further study for female candidates.ConclusionsIn conclusion, in the population studied, the genomic evaluation of egg quality traits of breeding birds at birth seems to be a promising strategy, at least for the selection of males.

On-farm net benefit of genotyping candidate female replacement cattle and sheep

The net benefit from investing in any technology is a function of the cost of implementation and the expected return in revenue. The objective of the present study was to quantify, using deterministic equations, the net monetary benefit from investing in genotyping of commercial females. Three case studies were presented reflecting dairy cows, beef cows and ewes based on Irish population parameters; sensitivity analyses were also performed. Parameters considered in the sensitivity analyses included the accuracy of genomic evaluations, replacement rate, proportion of female selection candidates retained as replacements, the cost of genotyping, the sire parentage error rate and the age of the female when it first gave birth. Results were presented as an annualised monetary net benefit over the lifetime of an individual, after discounting for the timing of expressions. In the base scenarios, the net benefit was greatest for dairy, followed by beef and then sheep. The net benefit improved as the reliability of the genomic evaluations improved and, in fact, a negative net benefit of genotyping was less frequent when the reliability of the genomic evaluations was high. The impact of a 10% point increase in genomic reliability was, however, greatest in sheep, followed by beef and then dairy. The net benefit of genotyping female selection candidates reduced as replacement rate increased. As genotyping costs increased, the net benefit reduced irrespective of the percentage of selection candidates kept, the replacement rate or even the population considered. Nonetheless, the association between the genotyping cost and the net benefit of genotyping differed by the percentage of selection candidates kept. Across all replacement rates evaluated, retaining 25% of the selection candidates resulted in the greatest net benefit when genotyping cost was low but the lowest net benefit when genotyping cost was high. Genotyping breakeven cost was non-linearly associated with the percentage of selection candidates retained, reaching a maximum when 50% of selection candidates were retained, irrespective of replacement rate, genomic reliability or the population. The genotyping breakeven cost was also non-linearly associated with replacement rate. The approaches outlined within provide the back-end framework for a decision support tool to quantify the net benefit of genotyping, once parameterised by the relevant population metrics.

Genome-wide association study and genomic evaluation of feed efficiency traits in Japanese Black cattle using single-step genomic best linear unbiased prediction method.

The objectives of this study were to better understand the genetic architecture and the possibility of genomic evaluation for feed efficiency traits by (i) performing genome-wide association studies (GWAS), and (ii) assessing the accuracy of genomic evaluation for feed efficiency traits, using single-step genomic best linear unbiased prediction (ssGBLUP)-based methods. The analyses were performed in residual feed intake (RFI), residual body weight gain (RG), and residual intake and body weight gain (RIG) during three different fattening periods. The phenotypes from 4,578 Japanese Black steers, which were progenies of 362 progeny-tested bulls and the genotypes from the bulls were used in this study. The results of GWAS showed that a total of 16, 8, and 12 gene ontology terms were related to RFI, RG, and RIG, respectively, and the candidate genes identified in RFI and RG were involved in olfactory transduction and the phosphatidylinositol signaling system, respectively. The realized reliabilities of genomic estimated breeding values were low to moderate in the feed efficiency traits. In conclusion, ssGBLUP-based method can lead to understand some biological functions related to feed efficiency traits, even with small population with genotypes, however, an alternative strategy will be needed to enhance the reliability of genomic evaluation.

The effect of genotype imputation and some important factors on the accuracy of genomic prediction and its persistency over time

This study was conducted to investigate the effect of genotype imputation on accuracy of genomic prediction. In addition, the effect of heritability, marker density, and reference population size on the accuracy of genomic prediction and its persistency over time was also studied. To this end, a genome comprised of 3 chromosomes on which 6000 bi-allelic SNP with equal initial frequency of 0.5 were arrayed was simulated. For assessing the genotype imputation, respectively, 5%, 30%, 50%, 70%, 90% and 95% of genotypes were masked and then imputed with Random Forest algorithm. Two reference population size of 1000 and 5000 animals and two marker densities of 20 SNPs and 100 SNPs per centimorgan were considered. Correlation coefficient between the true and predicted genomic breeding values (rp,t) was used as the prediction accuracy. The imputation accuracy (IA) decreased in parallel to increase in the percentage of imputed genotypes. It was ≈1 when only 5% of genotypes were imputed; however, as the proportion of masked genotypes increased beyond 70%, a rapid decline in IA was observed. In addition, by increasing the percentage of masked genotypes, rp,t decreased in such a way that it decreased from 0.78 to, respectively, 0.77, 0.75, 0.73, 0.70, 0.61, and 0.51 when, respectively, 5%, 30%, 50%, 70%, 90% and 95% of genotypes were imputed. Therefore, genotype imputation could be used effectively to decrease the cost of genomic selection. Significant increases were observed in accuracy of genomic prediction following increase in heritability, marker density and reference population size. With a heritability level of 0.2, marker density of 20 SNPs/cM and a reference population size of 1000 individuals, after 9 generations derived from the reference population, the accuracy decreased by 26% (0.78 to 0.57). By increasing marker density to 100 SNP/cM, the rp,t decreased by 16% (from 0.88 to 0.72). Moreover, by increasing reference population size from 1000 to 5000 individuals, the accuracy decreased by 14% (from 0.92 to 0.79) after 9 generations. In addition, by increasing heritability from 0.2 to 0.5, after 9 generations, there was 14% decrease in rp,t. It was concluded that increasing the reference population size and marker density could be effective strategies to prevent decrease in accuracy of genomic evaluation over time.

Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits, udder type traits, and somatic cell scores in French dairy goats

Genomic evaluation of French dairy goats is routinely conducted using the single-step genomic BLUP (ssGBLUP) method. This method has the advantage of simultaneously using all phenotypes, pedigrees, and genotypes. However, ssGBLUP assumes that all SNP explain the same amount of genetic variance, which is unlikely in the case of traits whose major genes or QTL are segregating. In this study, we investigated the effect of weighted ssGBLUP and its alternatives, which give more weight to SNP associated with the trait, on the accuracy of genomic evaluation of milk production, udder type traits, and somatic cell scores. The data set included 2,955 genotyped animals and 2,543,680 pedigree animals. The number of phenotypes varied with the trait. The accuracy of genomic evaluation was assessed on 205 genotyped Alpine and 146 genotyped Saanen goats born between 2009 and 2012. For traits with unknown QTL, weighted ssGBLUP was less accurate than, or as accurate as, ssGBLUP. For traits with identified QTL (i.e., QTL only present in the Saanen breed), weighted ssGBLUP outperformed ssGBLUP by between 2 and 14%.

Potential of preimplantation genomic selection for carcass traits in Japanese Black cattle.

Preimplantation genomic selection using genomic estimated breeding values (GEBVs) based on single nucleotide polymorphism (SNP) genotypes is expected to accelerate genetic improvement in cattle. To develop a preimplantation genomic selection system for carcass traits in Japanese Black cattle, we investigated the accuracy of genomic evaluation of carcass traits using biopsied embryonic cells (Experiment 1); we also performed an empirical evaluation for embryo transfer (ET) of vitrified GEBV-evaluated blastocysts to assess the efficiency of the preimplantation genomic selection system (Experiment 2). In Experiment 1, the mean call rate for SNP genotyping using approximately 15 biopsied cells was 98.1 ± 0.3%, whereas that for approximately 5 biopsied cells was 91.5 ± 2.4%. The mean concordance rate for called genotypes between ~15-cell biopsies and the corresponding biopsied embryos was 99.9 ± 0.02%. The GEBVs for carcass weight, ribeye area, and marbling score calculated from ~15-cell biopsies closely matched those from the corresponding calves produced by ET. In Experiment 2, a total of 208 in vivo blastocysts were biopsied (~15-cell) and the biopsied cells were processed for SNP genotyping, where 88.5% of the samples were found to be suitable for GEBV calculation. Large variations in GEBVs for carcass traits were observed among full-sib embryos and, among the embryos, some presented higher GEBVs for ribeye area and marbling score than their parents. The conception rate following ET of vitrified GEBV-evaluated blastocysts was 41.9% (13/31). These findings suggest the possible application of preimplantation genomic selection for carcass traits in Japanese Black cattle.

Multivariate genomic predictions for age at puberty in tropically adapted beef heifers.

Heifers that have an earlier age at puberty often have greater lifetime productivity. Age at puberty is moderately heritable so selection should effectively reduce the number of days to puberty, and improve heifer productivity and profitability as a result. However, recording age at puberty is intensive, requiring repeat ovarian scanning to determine age at first corpus luteum (AGECL). Genomic selection has been proposed as a strategy to select for earlier age at puberty; however, large reference populations of cows with AGECL records and genotypes would be required to generate accurate GEBV for this trait. Reproductive maturity score (RMS) is a proxy trait for age at puberty for implementation in northern Australia beef herds, where large scale recording of AGECL is not feasible. RMS assigns a score of 0 to 5 from a single ovarian scan to describe ovarian maturity at ~600 d. Here we use multivariate genomic prediction to evaluate the value of a large RMS data set to improve accuracy of GEBV for age at puberty (AGECL). There were 882 Brahman and 990 Tropical Composite heifers with AGECL phenotypes, and an independent set of 974 Brahman, 1,798 Santa Gertrudis, and 910 Droughtmaster heifers with RMS phenotypes. All animals had 728,785 real or imputed SNP genotypes. The correlation of AGECL and RMS (h2 = 0.23) was estimated as -0.83 using the genomic information. This result also demonstrates that using genomic information it is possible to estimate genetic correlations between traits collected on different animals in different herds, with minimal or unknown pedigree linkage between them. Inclusion of heifers with RMS in the multi-trait model improved the accuracy of genomic evaluations for AGECL. Accuracy of RMS GEBV generally did not improve by adding heifers with AGECL phenotypes into the reference population. These results suggest that RMS and AGECL may be used together in a multi-trait prediction model to increase the accuracy of prediction for age at puberty in tropically adapted beef cattle.

Towards multi-breed genomic evaluations for female fertility of tropical beef cattle.

Developing accurate genomic evaluations of fertility for tropical beef cattle must deal with at least two major challenges (i) recording cow fertility traits in extensive production systems on large numbers of cows and (ii) the genomic evaluations should work across the breeds, crossbreds, and composites used in tropical beef production. Here, we assess accuracy of genomic evaluations for a trait which can be collected on a large scale in extensive conditions, corpus luteum score (CLscore), which is 1 if ovarian scanning indicates a heifer has cycled by 600 d and 0 if not, in a multi-breed population. A total of 3,696 heifers, including 979 Brahmans, 914 Droughtmasters, and 1,803 Santa Gertrudis in seven herds across 3-yr cohorts with CLscores, were genotyped for 24,211 SNPs. Genotypes were imputed to 728,785 SNPs. GBLUP and BayesR were used to predict GEBV. Accuracy of GEBV was evaluated with two validation strategies. In the first strategy, the last year cohort of heifers from each herd was used for validation, such that every herd had heifers in both reference and validation populations. In the second validation strategy, each herd in turn was removed in its entirety from the reference population, and was used for validation. For both validation strategies, accuracy of GEBV for single breed and multi-breed reference populations was assessed. For the first validation strategy, accuracy of GEBV ranged from 0.2 for Brahmans to 0.4 for Droughtmasters. Increasing marker density from 24K SNPs to 728K SNPs resulted in a small increase in accuracy, and including multiple-breeds in the reference did not help improve accuracy. These results suggest that provided a herd has animals in the reference population, the accuracy of the GEBV is largely determined by within herd (linkage) information. The situation was very different when entire herds were predicted in the second validation. In this case accuracy of GEBV using only 24K SNPs and only a within breed reference was close to zero for all breeds. Accuracy increased substantially when 728K SNPs, BayesR, and a multi-breed reference were used, from 0.15 for Brahmans to 0.35 for Santa Gertrudis. Given the second validation strategy is more likely to reflect the situation for many herds in tropical beef production (no animals in the reference), genomic evaluations for fertility in tropical beef cattle should be based on high-density markers (728K SNPs) and should be multi-breed.

Vers une sélection génomique chez les caprins laitiers

La sélection génomique, qui a révolutionné la sélection génétique des bovins laitiers notamment, est désormais envisagée dans d’autres filières animales. Chez les caprins laitiers français, le gain de précision attendu des valeurs génomiques était un des questionnements de la filière en raison de la petite taille de la population de référence disponible (825 mâles et 1945 femelles génotypés sur une puce SNP 50K). Le but de cette étude est de tester différentes techniques d’évaluation génomique afin d’obtenir les évaluations génomiques les plus précises possibles. Une étude de la structure génétique de la population de référence caprine constituée d’animaux de races Saanen et Alpine, a révélé de faibles niveaux de déséquilibre de liaison (0,17 entre deux SNP consécutifs), de consanguinité et de parenté au sein de la population, ce qui n’est pas favorable à une bonne précision des évaluations génomiques. Les méthodes d’évaluations génomiques (GBLUP ou Bayésiennes), basées sur des performances pré-corrigées n’ont pas permis une amélioration significative des précisions des évaluations génomiques pour les caractères évalués en routine (caractères de production, de morphologie et comptages de cellules somatiques). Cependant les évaluations génomiques basées sur les performances propres des femelles ont permis d’obtenir des précisions supérieures à celles obtenues sur ascendance. La sélection génomique est donc envisageable chez les caprins laitiers français. Ces précisions peuvent également être légèrement augmentées par l’inclusion de gènes majeurs tels que celui de la caséine αs1.

Accuracy of Genomic Evaluations of Juvenile Growth Rate in Common Carp (Cyprinus carpio) Using Genotyping by Sequencing.

Cyprinids are the most important group of farmed fish globally in terms of production volume, with common carp (Cyprinus carpio) being one of the most valuable species of the group. The use of modern selective breeding methods in carp is at a formative stage, implying a large scope for genetic improvement of key production traits. In the current study, a population of 1,425 carp juveniles, originating from a partial factorial cross between 40 sires and 20 dams, was used for investigating the potential of genomic selection (GS) for juvenile growth, an exemplar polygenic production trait. RAD sequencing was used to identify and genotype SNP markers for subsequent parentage assignment, construction of a medium density genetic map (12,311 SNPs), genome-wide association study (GWAS), and testing of GS. A moderate heritability was estimated for body length of carp at 120 days (as a proxy of juvenile growth) of 0.33 (s.e. 0.05). No genome-wide significant QTL was identified using a single marker GWAS approach. Genomic prediction of breeding values outperformed pedigree-based prediction, resulting in 18% improvement in prediction accuracy. The impact of reduced SNP densities on prediction accuracy was tested by varying minor allele frequency (MAF) thresholds, with no drop in prediction accuracy until the MAF threshold is set <0.3 (2,744 SNPs). These results point to the potential for GS to improve economically important traits in common carp breeding programs.