Accuracy Of Breeding Values Research Articles

Potential of genomic selection for growth, meat content and colour traits in mixed-family breeding designs for the Pacific oyster Crassostrea gigas

Selective breeding programs have been initiated worldwide for the Pacific oyster Crassostrea gigas to improve economically important traits such as growth and disease resistance. The emergence of genomic tools has allowed novel perspectives for using genomic selection (GS) in mixed-family breeding designs, which are cheaper and easier to develop than classical breeding schemes. In this study, we evaluated the potential of GS for different growth-related and shell colour traits in two independent commercially selected populations (P1 and P2), based on mixed-family designs. From ≈14.5K informative SNPs genotyped with the bi-species Axiom Affymetrix 57K oyster array, ≈12.5K were mapped on the reference genome. A strong heterogeneity of marker density between and within chromosomes was reported, with a low linkage disequilibrium (below 0.1 at 0.1 Mb) between pairs of SNPs. The within-population structure was homogenous for each population, with effective sizes of 107 for P1 and 76 for P2. Heritability was estimated for each trait and population and ranged from 0.08 ± 0.04 (for mean darkness intensity in P1) to 0.56 ± 0.08 (for the mean upper valve b* value in P2) for a pedigree-based model and from 0.04 ± 0.02 (for mean darkness intensity in P1) to 0.69 ± 0.04 (for the mean darkness intensity in P2) for a genomic-based model. Growth-related traits were generally highly genetically and positively correlated with each other, but weakly correlated with colour traits. Accuracy of prediction was generally higher with the genomic model (GBLUP) than with the classical BLUP model, with a maximum gain of accuracy (from 0.38 to 0.66) for flesh weight adjusted by total weight in P2. Accuracy of breeding values was slightly higher for colour traits for P2, with higher heritability estimates. Overall, our results indicate that GS has a good potential to be implemented in mixed-family breeding programs in a shellfish such as C. gigas.

Comparison of accuracy of breeding value for cow from three methods in Hanwoo (Korean cattle) population.

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (Me) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between Me and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between Me and accuracy were -0.74 in CWT, -0.75 in EMA, -0.73 in MS, and -0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the Me, the higher the accuracy of EBV.

Comparison of various models for the estimation of heritability and breeding values.

The exploration of additive genetic variance for the selection of animals is the central paradigm in quantitative genetics and it is important to use appropriate animal models considering important factors. This study compares various factor effects for heritability and breeding values estimations on data collected on the Corriedale. Overall, the heritability estimates were the highest for birthweight (BW). Both the additive genetic variance and the heritability values were reduced upon adding more random effects. Year-effect as random reduced the heritability except for BW. The highest average accuracy for breeding values for birth, weaning, 6, 9, and 12-month-weights for were 0.656, 0.567, 0.519, 0.486, and 0.515. These were observed for models having sex or sex and parity as fixed effects. It is concluded that fixed and random factors have a significant impact on genetic-factors and breeding-value accuracies and should be considered during selection programs and make the selection program more effective.

Impact of inclusion non-additive effects on genome-wide association and variance’s components in Scottish black sheep

Context It’s well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection. Aim This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep. Methods This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used. Key results The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model. Conclusions The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16–24 weeks in Scottish Blackface lambs. Implications It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.

Spatial heterogeneity analysis for estimating breeding values of tree height in a hybrid larch progeny test plantation

ABSTRACT Microenvironmental heterogeneity in forest stands results in spatial variations in growth traits. Especially in progeny tests in tree breeding, this spatial variation can prevent the accurate estimation of genetic parameters, including breeding values. To quantify spatial heterogeneity, genetic models incorporating spatial coordinate information have been effectively used to accurately estimate breeding values of individuals. In this study, we measured the height of all Japanese larch and hybrid larch individuals in a progeny trial at 1, 2, 5, 10, and 15 years after planting and calculated genetic parameters, including breeding values, using genetic models. According to the fittings of the candidate models, a genetic model incorporating spatial coordinate information is more suitable for estimating genetic parameters. Overall, a genetic model incorporating spatial coordinate information could be effective in explaining the genetic effects of tree height, which will be useful for estimating more accurate breeding values in hybrid larch breeding programs.

Mixed reference population in genomic evaluation for clinical mastitis in Czech Holstein cattle

The accuracy of genomic breeding values (GEBV) for clinical mastitis (CM) of Czech Holstein cattle was analysed. The single-step genomic method and mixed genomic reference populations were employed. Dataset included 92,388 Holstein cows and 160,426 lactations. CM lactation incidence was 19.05%. Cows calved between 2017 and 2022 in 119 herds. A total of 4,969 Holstein sires and 35,814 Holstein females were genotyped. Three genomic matrices were used, two of them encompassing females. The linear animal model with repeatability included fixed effects of herd,year-season and parity_age at calving. The highest average accuracy of GEBV occurred for genotyped cows. SD of GEBV accuracy for bulls was significantly higher than these for cows. The accuracy of genotyped cows with health phenotypes ranged from 0.06 to 0.51. For the genotyped bulls born in 2021 and 2022, maximal accuracy was 0.37; for genotyped heifers, maximal accuracy was 0.42. The highest average GEBV accuracy occurred for the reference population with genotyped bulls and genotyped cows with phenotypes. The average accuracy for the young genotyped bulls increased by 1p. p. when phenotyped cows were considered in the reference genomic population. The cow's GEBV accuracy benefited from including their genotypes in the prediction. It has been confirmed that due to the expansion of the genomic reference population to include a group of genotyped cows with phenotypes, the individual reliability of GEBV for CM has increased.

Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values.

The impact of inclusion of genome-wide genotypes into breeding value predictions for UK Texel sheep is addressed in this article. The main aim was to investigate the level of change in the accuracy values for EBVs when information from animal genotypes is incorporated into the genetic evaluations. New genetic parameters for a range of lamb growth, carcass composition and health traits are described and applied in the estimation of conventional breeding values (EBVs) for almost 822,000 animals as well as genomic breeding values (gEBVs) after adding 10,143 genotypes. Principal component analyses showed that there are no major distinct groups; hence, the population is mainly homogenous and genetically well-linked. Results suggested that the highest change in accuracy was observed for the animals that are not phenotyped but have good links to the reference population. This was seen especially for the lowly heritable health traits thereby proving that the use of genotypes in breeding values estimation may accelerate the genetic gain by producing more accurate values especially for young, un-phenotyped animals.

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

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

Toward combining qualitative race-specific and quantitative race-nonspecific disease resistance by genomic selection

Key messageA novel genomic selection strategy offers the unique opportunity to develop qualitative race-specific resistant varieties that possess high levels of the more durable quantitative race-nonspecific resistance in their genetic background.Race-specific qualitative resistance genes (R-genes) are conferring complete resistance in many pathosystems, but are frequently overcome by new virulent pathogen races. Once the deployed R-genes are overcome, a wide variation of quantitative disease resistance (QDR) can be observed in a set of previously race-specific, i.e., completely resistant genotypes—a phenomenon known as “vertifolia effect.” This race-nonspecific QDR is considered to be more durable in the long term, but provides merely a partial protection against pathogens. This simulation study aimed to detangle race-specific R-gene-mediated resistance of pending selection candidates and the QDR in their genetic background by employing different genomic selection strategies. True breeding values that reflected performance data for rust resistance in wheat were simulated, and used in a recurrent genomic selection based on several prediction models and training population designs. Using training populations that were devoid of race-specific R-genes was thereby pivotal for an efficient improvement of QDR in the long term. Marker-assisted preselection for the presence of R-genes followed by a genomic prediction for accumulating the many small to medium effect loci underlying QDR in the genetic background of race-specific resistant genotypes appeared furthermore to be a promising approach to select simultaneously for both types of resistance. The practical application of such a knowledge-driven genomic breeding strategy offers the opportunity to develop varieties with multiple layers of resistance, which have the potential to prevent intolerable crop losses under epidemic situations by displaying a high level of QDR even when race-specific R-genes have been overcome by evolving pathogen populations.

Exploring the statistical nature of independent chromosome segments

Previous research in genomics developed the concept of independent chromosome segments and suggested that genomic prediction works on capturing the segment effects rather than on LD to individual QTL. The number of independent chromosome segments (Me) was posited as 4NeL, a function of effective population size (Ne) and genome length (L) in Morgans (M). The objective of this study was to determine whether the physical segments are approximately consecutive haplotype blocks of length ¼ M, with the number of haplotype blocks for each physical location equal to Ne. In a simulated population, the number of animals randomly selected as reference animals is represented as Na. For all animals, the genome was split into equal-sized segments represented as Ns. For each specific location, segments of non-reference animals were assigned the most similar segment of one reference animal. Genomic analyses estimated the value of the segment effects, and breeding values were a sum of all segment effects for a specific animal. Accuracies of segment effects were calculated by correlating the true breeding values (TBV) and the breeding values based on segments. Segment effect accuracies were compared with the true accuracy calculated by the correlation of TBV and genomic estimated breeding values (GEBV) computed using GBLUP. Accuracies were maximized at Na=Ne, Ns=4 L, but they were not as high as in GBLUP. Accuracies may be smaller using the statistical concept of segments due to approximations based on computing limitations, as the origin of each segment and the recombination sites were unknown in the simulation. Therefore, random animals served as reference animals, and each non-reference animal received the most similar segment of a reference animal instead of a linear combination of such segments. Genomic selection acts partially on ¼ M long chromosome segments, and using the statistical definition of segments moderately explains the accuracy. More complex simulations are needed to investigate the issue thoroughly.

First large-scale genomic prediction in the honey bee

Genomic selection has increased genetic gain in several livestock species, but due to the complicated genetics and reproduction biology not yet in honey bees. Recently, 2970 queens were genotyped to gather a reference population. For the application of genomic selection in honey bees, this study analyzes the accuracy and bias of pedigree-based and genomic breeding values for honey yield, three workability traits, and two traits for resistance against the parasite Varroa destructor. For breeding value estimation, we use a honey bee-specific model with maternal and direct effects, to account for the contributions of the workers and the queen of a colony to the phenotypes. We conducted a validation for the last generation and a five-fold cross-validation. In the validation for the last generation, the accuracy of pedigree-based estimated breeding values was 0.12 for honey yield, and ranged from 0.42 to 0.61 for the workability traits. The inclusion of genomic marker data improved these accuracies to 0.23 for honey yield, and a range from 0.44 to 0.65 for the workability traits. The inclusion of genomic data did not improve the accuracy of the disease-related traits. Traits with high heritability for maternal effects compared to the heritability for direct effects showed the most promising results. For all traits except the Varroa resistance traits, the bias with genomic methods was on a similar level compared to the bias with pedigree-based BLUP. The results show that genomic selection can successfully be applied to honey bees.

Assessing accuracy of genomic breeding values of selection candidates under biosecurity restrictions by progeny testing in Chinese shrimp Fenneropenaeus chinensis

Assessing accuracy of genomic breeding values of selection candidates under biosecurity restrictions by progeny testing in Chinese shrimp Fenneropenaeus chinensis

Genomic evaluation of commercial herds with different pedigree structures using the single-step genomic BLUP in Nelore cattle

The aim of this work was to evaluate the impact of applying genomic information in pedigree uncertainty situations on genetic evaluations for growth- and cow productivity-related traits in Nelore commercial herds. Records for accumulated cow productivity (ACP) and adjusted weight at 450 days of age (W450) were used, as well as genotypes of registered and commercial herd animals, genotyped with the Clarifide Nelore 3.1 panel (~29,000 SNPs). The genetic values for commercial and registered populations were estimated using different approaches that included (ssGBLUP) or did not include genomic information (BLUP), with different pedigree structures. Different scenarios were tested, varying the proportion of young animals with unknown sires (0, 25, 50, 75, and 100%), and unknown maternal grandsires (0, 25, 50, 75, and 100%). The prediction accuracies and abilities were calculated. The estimated breeding value accuracies decreased as the proportion of unknown sires and maternal grandsires increased. The genomic estimated breeding value accuracy using the ssGBLUP was higher in scenarios with a lower proportion of known pedigree when compared to the BLUP methodology. The results obtained with the ssGBLUP showed that it is possible to obtain reliable direct and indirect predictions for young animals from commercial herds without pedigree structure.

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

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

Genomic prediction in Brazilian Guzerá cattle: application of a single-step approach to productive and reproductive traits.

This study aimed to investigate the feasibility of genomic prediction for productive and reproductive traits in Guzerá cattle using single-step genomic best linear unbiased prediction (ssGBLUP). Evaluations included the 305-day cumulative yields (first lactation, in kg) of milk, lactose, protein, fat, and total solids; adjusted body weight (kg) at the ages of 450, 365, and 210days; and age at first calving (in days), from a database containing 197,283 measurements from Guzerá males and females born between 1954 and 2018. The pedigree included 433,823 animals spanning up to 14 overlapping generations. A total of 1618 animals were genotyped. The analyses were performed using ssGBLUP and traditional BLUP methods. Predictive ability and bias were accessed using cross-validation: predictive ability was similar between the methods and ranged from 0.27 to 0.47 for the genomic-based model and from 0.30 to 0.45 for the pedigree-based model; the bias was also similar between the methods, ranging from 0.88 to 1.35 in the genomic-based model and from 0.96 to 1.41 in the pedigree-based model. The individual accuracies of breeding values were evidently increased in the genomic evaluation, with values ranging from 0.41 to 0.56 in the genomic-based model and from 0.26 to 0.54 in the pedigree-based model. Even based on a small number of genotyped animals and a small database for some traits, the results suggest that ssGBLUP is feasible and may be applied to national genetic evaluation of the breed to increase the accuracy of breeding values without greatly impacting predictive ability and bias.

Effect of minor allele frequency and density of single nucleotide polymorphism marker arrays on imputation performance and prediction ability using the single-step genomic Best Linear Unbiased Prediction in a simulated beef cattle population

Context In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. Methods The simulated genomic and phenotypic data were obtained through QMsim software. 735 293 SNPs markers and 7000 quantitative trait loci (QTL) were randomly simulated. The mutation rate (10−5), QTL effects distribution (gamma distribution with shape parameter = 0.4) and minor allele frequency (MAF ≥ 0.02) of markers were used for quality control. A total of 335k SNPs (high density, HD) and 1000 QTLs were finally considered. Densities of 33 500 (35k), 16 750 (16k), 4186 (4k) and 2093 (2k) SNPs were customised through windows of 10, 20, 80 and 160 SNPs by chromosome, respectively. Three marker selection criteria were used within windows: (1) informative markers with MAF values close to 0.5 (HI); (2) less informative markers with the lowest MAF values (LI); (3) markers evenly distributed (ED). We evaluated the prediction of the high-density array and of 12 scenarios of customised SNP arrays, further the imputation performance of them. The genomic predictions and imputed genotypes were obtained with Blupf90 and FImpute software, respectively, and statistics parameters were applied to evaluate the accuracy of genotypes imputed. The Pearson’s correlation, the coefficient of regression, and the difference between genomic predictions and true breeding values were used to evaluate the prediction ability (PA), inflation (b), and bias (d), respectively. Key results Densities above 16k SNPs using HI and ED criteria displayed lower b, higher PA and higher imputation accuracy. Consequently, similar values of PA, b and d were observed with the use of imputed genotypes. The LI criterion with densities higher than 35k SNPs, showed higher PA and similar predictions using imputed genotypes, however lower b and quality of imputed genotypes were observed. Conclusion The results obtained showed that at least 5% of HI or ED SNPs available in the HD array are necessary to obtain reliable genomic predictions and imputed genotypes. Implications The development of low-density customised arrays based on criteria of MAF and even distribution of SNPs, might be a cost-effective and feasible approach to implement genomic selection in beef cattle.

Determination of Genomic Breed Composition using ADMIXTURE Software

Information from a reference population of animals with accurate breeding values is used to predict the genetic merit of a test population of animals for which such accurate phenotypic information is not available. For crosses, these procedures may be used to predict the proportion of genes of one breed in crossbred cattle when sets of genotypes from purebred animals are available as a reference. The genomic breed composition in 6326 HF crossbred animals (as a test population) performing in Gujarat was determined from SNPs (single nucleotide polymorphism) genotyped by INDUSCHIP array using ADMIXTURE software. The genotypes of 651 purebred indigenous and 120 exotic animals were used as reference genotype for determination of genomic breed composition. Unsupervised clustering analysis using ADMIXTURE software was carried out to infer ancestry ratios for 250 purebred animals using 14718 pruned INDUSCHIP SNPs. It showed that all the B. indicus animals were assigned to their respective breeds. A supervised clustering using 14718 INDUSCHIP variants was performed to infer breed proportions in 6326 crossbred animals with keeping K value as 9. As several indigenous breeds show evidence of admixture, the screening and selection for admixture in candidate bulls for breed purity can improve breeding programs. The average proportion of exotic inheritance was 55% in crossbred population. Hence, SNP genotyping gives opportunity to determine the exact breed proportions in crossbred animals and will be useful in regulation of exotic inheritance in the crossbred population for the optimum productivity of the animals reared by farmers.

New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

BackgroundRecurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. Usually, generations are discrete in recurrent selection, which means that breeding candidates are evaluated and considered for selection for only one cycle. Alternately, generations can overlap, with breeding candidates considered for selection as parents for multiple cycles. With recurrent genomic selection but not phenotypic selection, candidates can be re-evaluated by using genomic estimated breeding values without additional phenotyping of the candidates themselves. Therefore, it may be that candidates with true high breeding values discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by stochastic simulation.ResultsWith phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these individuals erroneously believed to have high value were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as individuals from previous breeding cycles typically had truly lower breeding values than candidates from the current generation. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low.ConclusionMaintaining discrete generations in recurrent phenotypic selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

Twelve Years into Genomic Selection in Forest Trees: Climbing the Slope of Enlightenment of Marker Assisted Tree Breeding

Twelve years have passed since the early outlooks of applying genomic selection (GS) to forest tree breeding, initially based on deterministic simulations, soon followed by empirical reports. Given its solid projections for causing a paradigm shift in tree breeding practice in the years to come, GS went from a hot, somewhat hyped, topic to a fast-moving area of applied research and operational implementation worldwide. The hype cycle curve of emerging technologies introduced by Gartner Inc. in 1995, models the path a technology takes in terms of expectations of its value through time. Starting with a sudden and excessively positive “peak of inflated expectations” at its introduction, a technology that survives the “valley of disappointment” moves into maturity to climb the “slope of enlightenment”, to eventually reach the “plateau of productivity”. Following the pioneering steps of GS in animal breeding, we have surpassed the initial phases of the Gartner hype cycle and we are now climbing the slope of enlightenment towards a wide application of GS in forest tree breeding. By merging modern high-throughput DNA typing, time-proven quantitative genetics and mixed-model analysis, GS moved the focus away from the questionable concept of dissecting a complex, polygenic trait in its individual components for breeding advancement. Instead of trying to find the needle in a haystack, i.e., the “magic” gene in the complex and fluid genome, GS more efficiently and humbly “buys the whole haystack” of genomic effects to predict complex phenotypes, similarly to an exchange-traded fund that more efficiently “buys the whole market”. Tens of studies have now been published in forest trees showing that GS matches or surpasses the performance of phenotypic selection for growth and wood properties traits, enhancing the rate of genetic gain per unit time by increasing selection intensity, radically reducing generation interval and improving the accuracy of breeding values. Breeder-friendly and cost-effective SNP (single nucleotide polymorphism) genotyping platforms are now available for all mainstream plantation forest trees, but methods based on low-pass whole genome sequencing with imputation might further reduce genotyping costs. In this perspective, I provide answers to why GS will soon become the most efficient and effective way to carry out advanced tree breeding, and outline a simple pilot demonstration project that tree breeders can propose in their organization. While the fundamental properties of GS in tree breeding are now solidly established, strategic, logistics and financial aspects for the optimized adoption of GS are now the focus of attentions towards the plateau of productivity in the cycle, when this new breeding method will become fully established into routine tree improvement.

Evolution of Genetics Organisations’ Strategies through the Implementation of Genomic Selection: Learnings and Prospects

Since its initial description in 2001, and with falling costs of genotyping, genomic selection has been implemented in a wide range of species. Theory predicts that the genomic selection approach to genetic improvement offers scope both for faster progress and the opportunity to make change in traits formerly less tractable to selection (hard-to-measure traits). This paper reports a survey of organisations involved in genetic improvement, across species, countries, and roles both public and private. While there are differences across organisations in what have been the most significant outcomes to date, both the increased accuracy of breeding values that underpins potentially faster progress, and the re-balancing of genetic change to include real progress in the hard-to-measure traits, have been widely observed. Across organisations, learnings have included the increasing importance of investment in phenotyping, and opportunities to evolve business models to engage more directly with a wider range of stakeholders. Genomic selection can be considered a more modular approach to genetic improvement, and its simplicity and effectiveness can transform both genetic improvement and the effectiveness of multi-disciplinary approaches to improving livestock and plant production, enabling potentially very significant increases in agricultural productivity, profitability and sustainability.