Implementation Of Genomic Selection Research Articles

Inbreeding in a Population of Polish Holstein-Friesian Young Bulls Before and After Genomic Selection

Abstract Inbreeding was analysed in a population of 14,144 Polish Black-and-White Holstein-Friesian (PBWHF) young bulls born between 1994 and 2017 and bred under both conventional and genomic breeding programmes. The inbreeding coefficients were computed using a model with genetic groups, according to the algorithm given by VanRaden. It was found that in the analysed population all bulls are inbred (100% of the population), with the mean coefficient of inbreeding ranging from 0.09% to 26.95%. Pedigree analysis also showed a relationship between the changing number of bulls over the years and the dynamics of population inbreeding. These trends are connected with changes in the breeding scheme, related to the implementation of genomic selection in the breeding programme for PBWHF cattle in 2014. The increasing number of weaned young bulls in Poland was paralleled by a fairly consistent increase in the mean inbreeding, but the inbreeding dynamics were relatively small. A reverse trend was observed in the group of young bulls born after 2013. As the number of bulls very rapidly decreased in successive birth years, the mean inbreeding for successive birth-year groups very rapidly increased. As a result, the estimated linear trend was equal to 0.02% inbreeding per year of birth in the group of bulls raised before genomic selection (~20 birth-year) whereas in the group of bulls raised after genomic selection (~4 birth-year) the trend was much higher and amounted to 0.56% inbreeding per year of birth. The high mean inbreeding found in the group of the genomically selected young bulls may translate into higher inbreeding in the whole population of PBWHF cattle, because these bulls are now intensively used as sires. The results of our study also show that the implementation of genomic selection in the breeding programme caused a very rapid increase in the inbreeding rate per birth-year in young bulls.

181 Enhancing the efficiency of poultry production by optimising selection objectives and breeding strategies

Abstract Poultry, in general, have been the most successful farmed species in efficiently producing protein, be it egg laying chickens or meat producing chickens or turkeys. This has been the result of 60–80 years of continuous selection and improvement of traits that effect overall efficiency. The innate physiology of birds, high reproductive rates and relatively short generation intervals have helped speed these improvements. The contribution of feed to production costs has increased efforts to accurately measure feed intake, and selecting for efficient feed utilisation have been significant. Selection for feed efficiency forms an integral part of the breeding objective of the respective breeding programs, but in recent years other components of efficiency have become increasingly important within breeding objectives. Market segmentation, with greater volumes of fully deboned meat for further value added processing, has resulted in the optimisation of yield to feed for both broilers and turkeys. Allometric growth patterns, particularly late stage deposition of breast meat, gives rise to opportunities for optimisation of both slaughter age and weight. Genetic evaluations incorporating genomic information from birds with yield measurements is now implemented across both broilers and turkeys in many breeding programs. The implementation of genomic selection has benefits for not only yield, but also growth and feed efficiency, and we have shown improvements in genetic prediction of 11%, 23% and 31% for growth, breast meat and residual feed intake in turkeys, respectively. Reproductive efficiency has also been improved in broilers, turkeys and particularly layers, by improving evaluations using genomic information. Altering egg production curves to maintain production persistency for increasingly longer time periods has also been important. Layer flocks are now routinely in production for up to 500 days and similar objectives are used for selection in both broiler and turkey reproduction.

329 Mendelian sampling, QTL similarity, and accuracy of genomic selection

Abstract Superiority of genomic selection (GS) is argued to be due to better modeling of the Mendelian sampling (MS) and tracking of QTL similarities between individuals. It is not clear that a better genome-wide modeling of MS contributes to the increased accuracy. In fact, it might be that modeling of MS outside areas of the genome under selection pressure is detrimental to the accuracy of GS. If true, this hypothesis will provide a better framework to understand the complex relationships between MS, QTL similarity and accuracy. Increases in marker density and the need for marker prioritization makes this hypothesis even more practically important. Answering this question could have a significant impact on accuracy and the computational costs of GS implementation. A 30-chromosome genome with 50K SNPs was simulated. 200 QTL were simulated on two chromosomes for a trait with heritability of 0.4. Genomic relationships were calculated based on all 50K SNPs (G30), 3,333 SNPs on the two chromosomes carrying QTL (G2), and 46,667 SNPs on chromosomes without QTL (G28). Table 1 shows accuracies after 3 and 10 generations of (G)EBV-based selection (M1) and random selection (M2). BLUP accuracies are consistently higher (11.5 to 43.8%) than G28, showing that expected relationships better model QTL similarities than a dense panel of markers that lie outside QTL regions. Inclusion of markers that lie outside QTL regions with markers inside QTL regions reduces accuracies, as shown by the inferior (20.2 to 22.8%) performance of G30 compared to G2. Coefficients of variation were higher for low than high additive relationships suggesting that errors made in estimating QTL similarities for lowly related animals may have the most detrimental impact. Furthermore, while G28 markers capture more variation than pedigree, the superiority of BLUP indicates that variation captured by G28 is not consistent with true variation in QTL inheritance.

Bias in estimates of variance components in populations undergoing genomic selection: a simulation study

BackgroundAfter the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno1: phenotypes from only the conventional phase (1–20 years); (2) Pheno1 + 2: phenotypes from both the conventional phase and GS phase (1–35 years); (3) Pheno2: phenotypes from only the GS phase (21–35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base).ResultsIn general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno2), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno2) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated.ConclusionsThe results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.

Comparison of genomic prediction methods for evaluation of adaptation and productive efficiency traits in Braford and Hereford cattle

Various strategies to the use of genetic markers in genetic evaluations have been proposed and substantial genetic progress has been achieved in livestock breeding programs by incorporating genomic information. However, the implementation of genomic selection in beef cattle breeds, especially in composites, adapted to tropical and sub-tropical conditions, is well behind other cattle breeds. Alternative methods to perform genomic predictions for novel adaptation and productive efficiency traits in Brazilian Hereford and Braford need to be further investigated in order to improve the competitiveness of these beef cattle populations. Pedigree, phenotypes and a total of 3,680 genotypes (2,997 Braford and 683 Hereford) were used in this study to assess the predictive ability of genomic predictions for tick resistance, prepuce (navel) score, hair length, ocular pigmentation score, birth weight, pre- and post-weaning weight gain, scrotal circumference, body conformation, precocity, muscularity, and body size. Accuracies of Direct Genomic Values (DGVs) across all scenarios ranged from 0.08 to 0.66, and accuracies of Genomic Estimated Breeding Values (GEBVs) ranged from 0.08 to 0.81. In general, there were no statistical differences (P>0.05) between the DGVs and GEBVs accuracies, and the regression coefficients were not statistically different from 1 (i.e. not biased) for the large majority of traits and scenarios. Our findings indicate that accurate breeding values can be obtained at an early age for various adaptation and productive efficiency traits in Hereford and Braford. In addition, both DGVs and GEBVs predicted by single-step and two-step GBLUP methods produced similar accuracy and amount of bias. Considering the implementation's feasibility, the use the single-step GBLUP method for genetic evaluation of adaptation and productive efficiency traits in Braford and Hereford cattle is recommended.

Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection.

The implementation of genomic selection in recurrent breeding programs raises the concern that a higher inbreeding rate could compromise the long-term genetic gain. An optimized mating strategy that maximizes the performance in progeny and maintains diversity for long-term genetic gain is therefore essential. The optimal cross-selection approach aims at identifying the optimal set of crosses that maximizes the expected genetic value in the progeny under a constraint on genetic diversity in the progeny. Optimal cross-selection usually does not account for within-family selection, i.e., the fact that only a selected fraction of each family is used as parents of the next generation. In this study, we consider within-family variance accounting for linkage disequilibrium between quantitative trait loci to predict the expected mean performance and the expected genetic diversity in the selected progeny of a set of crosses. These predictions rely on the usefulness criterion parental contribution (UCPC) method. We compared UCPC-based optimal cross-selection and the optimal cross-selection approach in a long-term simulated recurrent genomic selection breeding program considering overlapping generations. UCPC-based optimal cross-selection proved to be more efficient to convert the genetic diversity into short- and long-term genetic gains than optimal cross-selection. We also showed that, using the UCPC-based optimal cross-selection, the long-term genetic gain can be increased with only a limited reduction of the short-term commercial genetic gain.

Genomic Selection for Antioxidant Production in a Panel of Sorghum bicolor and S. bicolor × S. halepense Lines.

The purpose of this work was to assess the performance of four genomic selection (GS) models (GBLUP, BRR, Bayesian LASSO and BayesB) in 4 sorghum grain antioxidant traits (phenols, flavonoids, total antioxidant capacity and condensed tannins) using whole-genome SNP markers in a novel diversity panel of Sorghum bicolor lines and landraces and S. bicolor × S. halepense recombinant inbred lines. One key breeding problem modelled was predicting the performance in the antioxidant production of new and unphenotyped sorghum genotypes (validation set). The population was weakly structured (analysis of molecular variance, AMOVA R2 = 9%), showed a significant genetic diversity and expressed antioxidant traits with a good level of variability and high correlation. The S. bicolor × S. halepense lines outperformed Sorghum bicolor populations for all the antioxidants. The four GS models implemented in this work performed comparably across traits, with accuracy ranging from 0.49 to 0.58, and are considered high enough to sustain sorghum breeding for antioxidants production and allow important genetic gains per unit of time and cost. The results presented in this work are expected to contribute to GS implementation and the genetic improvement of sorghum grain antioxidants for different purposes, including the manufacture of health-promoting and specialty foods.

The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds

BackgroundIn France, implementation of genomic evaluations in dairy cattle breeds started in 2009 and this has modified the breeding schemes drastically. In this context, the goal of our study was to understand the impact of genomic selection on the genetic diversity of bulls from three French dairy cattle breeds born between 2005 and 2015 (Montbéliarde, Normande and Holstein) and the factors that are involved.MethodsWe compared annual genetic gains, inbreeding rates based on runs of homozygosity (ROH) and pedigree data, and mean ROH length within breeds, before and after the implementation of genomic selection.ResultsGenomic selection induced an increase in mean annual genetic gains of 50, 71 and 33% for Montbéliarde, Normande and Holstein bulls, respectively, and in parallel, the generation intervals were reduced by a factor of 1.7, 1.9 and 2, respectively. We found no significant change in inbreeding rate for the two national breeds, Montbéliarde and Normande, and a significant increase in inbreeding rate for the Holstein international breed, which is now as high as 0.55% per year based on ROH and 0.49% per year based on pedigree data (equivalent to a rate of 1.36 and 1.39% per generation, respectively). The mean ROH length was longer for bulls from the Holstein breed than for those from the other two breeds.ConclusionsWith the implementation of genomic selection, the annual genetic gain increased for bulls from the three major French dairy cattle breeds. At the same time, the annual loss of genetic diversity increased for Holstein bulls, possibly because of the massive use of a few elite bulls in this breed, but not for Montbéliarde and Normande bulls. The increase in mean ROH length in Holstein may reflect the occurrence of recent inbreeding. New strategies in breeding schemes, such as female donor stations and embryo transfer, and recent implementation of genomic evaluations in small regional breeds should be studied carefully in order to ensure the sustainability of breeding schemes in the future.

SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus.

Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.

Genome-Wide Patterns of Population Structure and Linkage Disequilibrium in Farmed Nile Tilapia (Oreochromis niloticus).

Nile tilapia (Oreochromis niloticus) is one of the most produced farmed fish in the world and represents an important source of protein for human consumption. Farmed Nile tilapia populations are increasingly based on genetically improved stocks, which have been established from admixed populations. To date, there is scarce information about the population genomics of farmed Nile tilapia, assessed by dense single nucleotide polymorphism (SNP) panels. The patterns of linkage disequilibrium (LD) may affect the success of genome-wide association studies (GWAS) and genomic selection (GS), and also provide key information about demographic history of farmed Nile tilapia populations. The objectives of this study were to provide further knowledge about the population structure and LD patterns, as well as, estimate the effective population size (N e) for three farmed Nile tilapia populations, one from Brazil (POP A) and two from Costa Rica (POP B and POP C). A total of 55 individuals from each population, were genotyped using a 50K SNP panel selected from a whole-genome sequencing (WGS) experiment. The first two principal components explained about 20% of the total variation and clearly differentiated between the three populations. Population genetic structure analysis showed evidence of admixture, especially for POP C. The contemporary N e estimated, based on LD values, ranged from 78 to 159. No differences were observed in the LD decay among populations, with a rapid decrease of r 2 with increasing inter-marker distance. Average r 2 between adjacent SNP pairs ranged from 0.19 to 0.03 for both POP A and C, and 0.20 to 0.03 f or POP B. Based on the number of independent chromosome segments in the Nile tilapia genome, at least 9.4, 7.6, and 4.6K SNPs for POP A, POP B, and POP C respectively, are required for the implementation of GS in the present farmed Nile tilapia populations.

Fourth cycle breeding and testing strategy for Pinus taeda in the NC State University Cooperative Tree Improvement Program

The Cooperative Tree Improvement Program at North Carolina State University used the differential evolution algorithm developed for animal breeding (Kinghorn, Genet Sel Evol 43(1):4, Kinghorn 2011) to design the mating in the 4th cycle of loblolly pine (Pinus taeda L.) breeding. The mate selection algorithm optimizes genetic gain while putting constraints on progeny inbreeding and coancestry level. The final mating list (4th cycle population) is expected to produce an index breeding value 63% higher than the candidate population mated at random. The average inbreeding coefficient in the candidate population was 0.000146. It increased to 0.000285 in the 4th cycle progeny. Only eight crosses out of 748 with progeny inbreeding value greater than zero were included in the mating list. One parent with a high index breeding value was used in 13 matings. A high majority of 421 individuals (75%) were mated two or more times; the remaining were mated once. The Cooperative also made significant changes in experimental design protocols to increase the efficiency of progeny testing by implementing an alpha-cyclic incomplete row-column design. Progeny test series across years were connected with a rolling front testing strategy by overlapping genetic entries across years. The Cooperative is expected to complete the 4th cycle in about 14 years, a significant reduction compared to previous cycles. With availability of high-throughput genotyping platforms, the Cooperative is moving toward implementation of genomic selection in the 5th cycle and beyond.

A bivariate genomic model with additive, dominance and inbreeding depression effects for sire line and three-way crossbred pigs

BackgroundCrossbreeding is widely used in pig production because of the benefits of heterosis effects and breed complementarity. Commonly, sire lines are bred for traits such as feed efficiency, growth and meat content, whereas maternal lines are also bred for reproduction and longevity traits, and the resulting three-way crossbred pigs are used for production of meat. The most important genetic basis for heterosis is dominance effects, e.g. removal of inbreeding depression. The aims of this study were to (1) present a modification of a previously developed model with additive, dominance and inbreeding depression genetic effects for analysis of data from a purebred sire line and three-way crossbred pigs; (2) based on this model, present equations for additive genetic variances, additive genetic covariance, and estimated breeding values (EBV) with associated accuracies for purebred and crossbred performances; (3) use the model to analyse four production traits, i.e. ultra-sound recorded backfat thickness (BF), conformation score (CONF), average daily gain (ADG), and feed conversion ratio (FCR), recorded on Danbred Duroc and Danbred Duroc-Landrace–Yorkshire crossbred pigs reared in the same environment; and (4) obtain estimates of genetic parameters, additive genetic correlations between purebred and crossbred performances, and EBV with associated accuracies for purebred and crossbred performances for this data set.ResultsAdditive genetic correlations (with associated standard errors) between purebred and crossbred performances were equal to 0.96 (0.07), 0.83 (0.16), 0.75 (0.17), and 0.87 (0.18) for BF, CONF, ADG, and FCR, respectively. For BF, ADG, and FCR, the additive genetic variance was smaller for purebred performance than for crossbred performance, but for CONF the reverse was observed. EBV on Duroc boars were more accurate for purebred performance than for crossbred performance for BF, CONF and FCR, but not for ADG.ConclusionsMethodological developments led to equations for genetic (co)variances and EBV with associated accuracies for purebred and crossbred performances in a three-way crossbreeding system. As illustrated by the data analysis, these equations may be useful for implementation of genomic selection in this system.

Genomic selection improves the possibility of applying multiple breeding programs in different environments

One joint breeding program (BP) for different dairy cattle environments can be advantageous for genetic gain depending on the genetic correlation between environments (rg). The break-even correlation (rb) refers to the specific rg where genetic gain with 1 joint BP is equal to the genetic gain of 2 environment-specific BP. One joint BP has the highest genetic gain if rg is higher than rb, whereas 2 environment-specific BP have higher genetic gain if rg is lower than rb. Genetic gain in this context is evaluated from a breeding company's perspective that aims to improve genetic gain in both environments. With the implementation of genomic selection, 2 types of collaboration can be identified: exchanging breeding animals and exchanging genomic information. The aim of this study was to study genetic gain in multiple environments with different breeding strategies with genomic selection. The specific aims were (1) to find rb when applying genomic selection; (2) to assess how much genetic gain is lost when applying a suboptimal breeding strategy; (3) to study the effect of the reliability of direct genomic values, number of genotyped animals, and environments of different size on rb and genetic gain; and (4) to find rb from each environment's point of view. Three breeding strategies were simulated: 1 joint BP for both environments, 2 environment-specific BP with selection of bulls across environments, and 2 environment-specific BP with selection of bulls within environments. The rb was 0.65 and not different from rb with progeny-testing breeding programs when compared at the same selection intensity. The maximum loss in genetic gain in a suboptimal breeding strategy was 24%. A higher direct genomic value reliability and an increased number of genotyped selection candidates increased genetic gain, and the effect on rb was not large. A different size in 2 environments decreased rb by, at most, 0.10 points. From a large environment's point of view, 1 joint BP was the optimal breeding strategy in most scenarios. From a small environment's point of view, 1 joint BP was only the optimal breeding strategy at high rg. When the exchange of breeding animals between environments was restricted, genetic gain could still increase in each environment. This was due to the exchange of genomic information between environments, even when rg between environments were as low as 0.4. Thus, genomic selection improves the possibility of applying environment-specific BP.

Genomic Prediction for Winter Survival of Lowland Switchgrass in the Northern USA.

The lowland ecotype of switchgrass has generated considerable interest because of its higher biomass yield and late flowering characteristics compared to the upland ecotype. However, lowland ecotypes planted in northern latitudes exhibit very low winter survival. Implementation of genomic selection could potentially enhance switchgrass breeding for winter survival by reducing generation time while eliminating the dependence on weather. The objectives of this study were to assess the potential of genomic selection for winter survival in lowland switchgrass by combining multiple populations in the training set and applying the selected model in two independent testing datasets for validation. Marker data were generated using exome capture sequencing. Validation was conducted using (1) indirect indicators of winter adaptation based on geographic and climatic variables of accessions from different source locations and (2) winter survival estimates of the phenotype. The prediction accuracies were significantly higher when the training dataset comprising all populations was used in fivefold cross validation but its application was not useful in the independent validation dataset. Nevertheless, modeling for population heterogeneity improved the prediction accuracy to some extent but the genetic relationship between the training and validation populations was found to be more influential. The predicted winter survival of lowland switchgrass indicated latitudinal and longitudinal variability, with the northeast USA the region for most cold tolerant lowland populations. Our results suggested that GS could provide valuable opportunities for improving winter survival and accelerate the lowland switchgrass breeding programs toward the development of cold tolerant cultivars suitable for northern latitudes.

Study of whole genome linkage disequilibrium patterns of Iranian water buffalo breeds using the Axiom Buffalo Genotyping 90K Array.

Accuracy of genome-wide association studies, and the successful implementation of genomic selection depends on the level of linkage disequilibrium (LD) across the genome and also the persistence of LD phase between populations. In the present study LD between adjacent SNPs and LD decay between SNPs was calculated in three Iranian water buffalo populations. Persistence of LD phase was evaluated across these populations and effective population size (Ne) was estimated from corrected r2 information. A set of 404 individuals from three Iranian buffalo populations were genotyped with the Axiom Buffalo Genotyping 90K Array. Average r2 and |D'| between adjacent SNP pairs across all chromosomes was 0.27 and 0.66 for AZI, 0.29 and 0.68 for KHU, and 0.32 and 0.72 for MAZ. The LD between the SNPs decreased with increasing physical distance from 100Kb to 1Mb between markers, from 0.234 to 0.018 for AZI, 0.254 to 0.034 for KHU, and 0.297 to 0.119 for MAZ, respectively. These results indicate that a density of 90K SNP is sufficient for genomic analyses relying on long range LD (e.g. GWAS and genomic selection). The persistence of LD phase decreased with increasing marker distances across all the populations, but remained above 0.8 for AZI and KHU for marker distances up to 100Kb. For multi-breed genomic evaluation, the 90K SNP panel is suitable for AZI and KHU buffalo breeds. Estimated effective population sizes for AZI, KHU and MAZ were 477, 212 and 32, respectively, for recent generations. The estimated effective population sizes indicate that the MAZ is at risk and requires careful management.

An experimental approach for estimating the genomic selection advantage for Fusarium head blight and Septoria tritici blotch in winter wheat.

The genomic selection advantage for Fusarium head blight is promising but failed for Septoria tritici blotch. Selection of new breeding parents based on predictions must be treated with caution. Genomic selection (GS) is an approach that uses whole-genome marker data to estimate breeding values of untested genotypes and holds the potential to improve the genetic gain in breeding programs by shortening the breeding cycle and increasing the selection intensity. However, reported realized gain from genomic selection is limited to few experiments. In this study, a training population of 1120 winter wheat lines derived from 14 bi-parental families was genotyped with genome-wide single nucleotide polymorphism markers and phenotyped for Fusarium head blight (FHB) and Septoria tritici blotch (STB) severity, plant height and heading date. We used weighted ridge regression best linear unbiased prediction to calculate genomic estimated breeding values (GEBVs) of 2500 genotypes. Based on GEBVs, we selected the most resistant individuals as well as a random sample and tested them in a multi-location field trial. We computed moderate coefficients of correlation between observed and predicted trait values for FHB severity, plant height and heading date and achieved a genomic selection advantage of 10.62 percentage points for FHB resistance compared to the randomly chosen subpopulation. Genomic selection failed for the improvement of STB resistance with a genomic selection advantage of only 2.14 percentage points. Our results also indicate that the selection of new breeding parents based on GEBVs must be treated with caution. Taken together, the implementation of GS for FHB resistance, plant height and heading date seems to be promising. For traits with very strong genotype × environment variance, like STB resistance, GS appears to be still challenging.

Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice.

Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments.

Performance of whole genome prediction for growth traits in a crossbred chicken population

In the past decades, crossbreeding has been widely used to improve productivity in plant and animal husbandry. With the rapid implementation of genomic selection (GS) in these industries and a decrease in the cost of genotyping, genomic prediction (GP) with data from crossbred populations is an emerging research interest. Using a crossbred population derived from a cross between White Recessive Rock (WRR) and Xinghua (XH) chickens (n = 473), the predictive ability and selection differential of conventional best linear unbiased prediction (BLUP) and 3 GP methods (GBLUP, RKHS, and BayesB) were compared. All chickens were genotyped by a 60K SNP chip. Twenty traits containing body weight (BW) at 1 to 90 d of age, breast muscle weight (BMW), leg muscle weight (LMW), wing weight (WW), and average daily gain (ADG) of different periods were analyzed. The accuracy of GP was higher than that of conventional BLUP for 18 out of 20 investigated traits. The average selection differential on BW selected with GP methods was greater than that from conventional BLUP, with a proportion selected varied between 5 and 30%. Overall, the GP methods outperformed conventional BLUP for both predictive ability and selection effect in the tested crossbred chicken population. Using genomic data from crossbred populations could potentially benefit the decision making for the purpose of marketing or breeding within crossbred population.

Reliability of genomic prediction for milk fatty acid composition by using a multi-population reference and incorporating GWAS results

BackgroundLarge-scale phenotyping for detailed milk fatty acid (FA) composition is difficult due to expensive and time-consuming analytical techniques. Reliability of genomic prediction is often low for traits that are expensive/difficult to measure and for breeds with a small reference population size. An effective method to increase reference population size could be to combine datasets from different populations. Prediction models might also benefit from incorporation of information on the biological underpinnings of quantitative traits. Genome-wide association studies (GWAS) show that genomic regions on Bos taurus chromosomes (BTA) 14, 19 and 26 underlie substantial proportions of the genetic variation in milk FA traits. Genomic prediction models that incorporate such results could enable improved prediction accuracy in spite of limited reference population sizes. In this study, we combine gas chromatography quantified FA samples from the Chinese, Danish and Dutch Holstein populations and implement a genomic feature best linear unbiased prediction (GFBLUP) model that incorporates variants on BTA14, 19 and 26 as genomic features for which random genetic effects are estimated separately. Prediction reliabilities were compared to those estimated with traditional GBLUP models.ResultsPredictions using a multi-population reference and a traditional GBLUP model resulted in average gains in prediction reliability of 10% points in the Dutch, 8% points in the Danish and 1% point in the Chinese populations compared to predictions based on population-specific references. Compared to the traditional GBLUP, implementation of the GFBLUP model with a multi-population reference led to further increases in prediction reliability of up to 38% points in the Dutch, 23% points in the Danish and 13% points in the Chinese populations. Prediction reliabilities from the GFBLUP model were moderate to high across the FA traits analyzed.ConclusionsOur study shows that it is possible to predict genetic merits for milk FA traits with reasonable accuracy by combining related populations of a breed and using models that incorporate GWAS results. Our findings indicate that international collaborations that facilitate access to multi-population datasets could be highly beneficial to the implementation of genomic selection for detailed milk composition traits.

Leveraging Available Resources and Stakeholder Involvement for Improved Productivity of African Livestock in the Era of Genomic Breeding.

The African continent is home to diverse populations of livestock breeds adapted to harsh environmental conditions with more than 70% under traditional systems of management. Animal productivity is less than optimal in most cases and is faced with numerous challenges including limited access to adequate nutrition and disease management, poor institutional capacities and lack of adequate government policies and funding to develop the livestock sector. Africa is home to about 1.3 billion people and with increasing demand for animal proteins by an ever growing human population, the current state of livestock productivity creates a significant yield gap for animal products. Although a greater section of the population, especially those living in rural areas depend largely on livestock for their livelihoods; the potential of the sector remains underutilized and therefore unable to contribute significantly to economic development and social wellbeing of the people. With current advances in livestock management practices, breeding technologies and health management, and with inclusion of all stakeholders, African livestock populations can be sustainably developed to close the animal protein gap that exists in the continent. In particular, advances in gene technologies, and application of genomic breeding in many Western countries has resulted in tremendous gains in traits like milk production with the potential that, implementation of genomic selection and other improved practices (nutrition, healthcare, etc.) can lead to rapid improvement in traits of economic importance in African livestock populations. The African livestock populations in the context of this review are limited to cattle, goat, pig, poultry, and sheep, which are mainly exploited for meat, milk, and eggs. This review examines the current state of livestock productivity in Africa, the main challenges faced by the sector, the role of various stakeholders and discusses in-depth strategies that can enable the application of genomic technologies for rapid improvement of livestock traits of economic importance.