Increasing Selection Intensity Research Articles

Genomic selection in forest trees comes to life: unraveling its potential in an advanced four-generation Eucalyptus grandis population.

Genomic Selection (GS) in tree breeding optimizes genetic gains by leveraging genomic data to enable early selection of seedlings without phenotypic data reducing breeding cycle and increasing selection intensity. Traditional assessments of the potential of GS in forest trees have typically focused on model performance using cross-validation within the same generation but evaluating effectively realized predictive ability (RPA) across generations is crucial. This study estimated RPAs for volume growth (VOL), wood density (WD), and pulp yield (PY) across four generations breeding of Eucalyptus grandis. The training set spanned three generations, including 34,461 trees with three-year growth data, 6,014 trees with wood quality trait data, and 1,918 trees with 12,695 SNPs (single nucleotide polymorphisms) data. Employing single-step genomic BLUP, we compared the genomic predictions of breeding values (GEBVs) for 1,153 fourth-generation full-sib seedlings in the greenhouse with their later-collected phenotypic estimated breeding values (EBVs) at age three years. RPAs were estimated using three GS targets (individual trees, trees within families, and families), two selection criteria (single- and multiple-trait), and training populations of either all 1,918 genotyped trees or the 67 direct ancestors of the selection candidates. RPAs were higher for wood quality traits (0.33 to 0.59) compared to VOL (0.14 to 0.19) and improved for wood traits (0.42 to 0.75) but not for VOL when trained only with direct ancestors, highlighting the challenges in accurately predicting growth traits. GS was more effective at excluding bottom-ranked candidates than selecting top-ranked ones. The between-family GS approach outperformed individual-tree selection for VOL (0.11 to 0.16) and PY (0.72 to 0.75), but not for WD (0.43 vs. 0.42). Furthermore, higher levels of relatedness and lower genotype by environment (G × E) interaction between training and testing populations enhanced RPAs for VOL (0.39). In summary, despite limited effectiveness in ranking top VOL individuals, GS effectively identified low-performing individuals and families. These multi-generational findings underscore GS's potential in tree breeding, stressing the importance of considering relatedness and G × E interaction for optimal performance.

A meta-analysis on the effects of marker coverage, status number, and size of training set on predictive accuracy and heritability estimates from genomic selection in tree breeding

Genomic selection (GS) is increasingly used in tree breeding because of the possibility to hasten breeding cycles, increase selection intensity or facilitate multi-trait selection, and to obtain less biased estimates of quantitative genetic parameters such as heritability. However, tree breeders are aiming to obtain accurate estimates of such parameters and breeding values while optimizing sampling and genotyping costs. We conducted a metadata analysis of results from 28 GS studies totalling 115 study-traits. We found that heritability estimates obtained using DNA marker-based information for a variety of traits and species were not significantly related to variation in the total number of markers ranging from about 1500 to 116 000, nor by the marker density, ranging from about 1 to 60 markers/centimorgan, nor by the status number of the breeding populations ranging from about 10 to 620, nor by the size of the training set ranging from 236 to 2458. However, the predictive accuracy of breeding values was generally higher when the status number of the breeding population was smaller, which was expected given the higher level of relatedness in small breeding populations, and the increased ability of a given number of markers to trace the long-range linkage disequilibrium in such conditions. According to expectations, the predictive accuracy also increased with the size of the training set used to build marker-based models. Genotyping arrays with a few to many thousand markers exist for several tree species and with the actual costs, GS could thus be efficiently implemented in many more tree breeding programs, delivering less biased genetic parameters and more accurate estimates of breeding values.

Genomic selection in Eucalyptus globulus as an assisted selection tool for growth rate and disease resistance

The adoption of genomic selection (GS) in forest tree breeding allows shortening breeding cycles, increasing selection intensity, and improving the accuracy of breeding values, which translate into increased genetic gains. Therefore, the objective of this work was to design a breeding strategy incorporating GS in the Eucalyptus globulus improvement plan of the INIA. Using the single step genomic selection (ssGBLUP) methodology, a multi-trait model of GS was developed considering different ages of evaluation (14 and 21 months), and combination of growth traits (total height and DBH) and heteroblasty (proportion of adult foliage). The model was tested through the predictive capacity (estimated through intra-population cross-validation), the prediction bias (estimated as the regression coefficient between the reference breeding values and those predicted by the model), and the comparison between the rankings of breeding values from the conventional ABLUP and the ssGBLUP (as the proportion of common individuals in the top 10%). The heritabilities in the strict sense for all traits were high, ranging from 0.51 to 0.72. The predictive abilities of the different cross-validations by site and by trait were high, varying from 0.72 to 0.92. The model tends to slightly underestimate DBH and the proportion of adult foliage at some sites and overestimate them at others (bias between 0.88 and 1.19), and overestimate total height in all sites (bias between 0.51 and 0.68). The proportion of common individuals in the top 10% by conventional selection and GS is high for all traits (0.76 - 0.86). These results suggest that the incorporation of GS in the improvement plan of E. globulus would be efficient. It is proposed to include GS in two stages of the breeding cycle: the selection of the best parents for controlled mating, increasing the selection intensity, and the selection of individuals in the nursery, reducing the stage of field evaluation of the selected clones. This strategy will markedly reduce the selection cycle, allowing commercial clones to be obtained in half the time compared to conventional selection.

Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids.

Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus. Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts. Genomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training. GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections was the highest for GBLUP_G (45% to 60%) compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees. We demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding.

Genomic selection for salinity tolerance in japonica rice.

Improving plant performance in salinity-prone conditions is a significant challenge in breeding programs. Genomic selection is currently integrated into many plant breeding programs as a tool for increasing selection intensity and precision for complex traits and for reducing breeding cycle length. A rice reference panel (RP) of 241 Oryza sativa L. japonica accessions genotyped with 20,255 SNPs grown in control and mild salinity stress conditions was evaluated at the vegetative stage for eight morphological traits and ion mass fractions (Na and K). Weak to strong genotype-by-condition interactions were found for the traits considered. Cross-validation showed that the predictive ability of genomic prediction methods ranged from 0.25 to 0.64 for multi-environment models with morphological traits and from 0.05 to 0.40 for indices of stress response and ion mass fractions. The performances of a breeding population (BP) comprising 393 japonica accessions were predicted with models trained on the RP. For validation of the predictive performances of the models, a subset of 41 accessions was selected from the BP and phenotyped under the same experimental conditions as the RP. The predictive abilities estimated on this subset ranged from 0.00 to 0.66 for the multi-environment models, depending on the traits, and were strongly correlated with the predictive abilities on cross-validation in the RP in salt condition (r = 0.69). We show here that genomic selection is efficient for predicting the salt stress tolerance of breeding lines. Genomic selection could improve the efficiency of rice breeding strategies for salinity-prone environments.

Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population

Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.

Genetic architecture of terpene chemistry and growth traits and the impact of inbreeding on these traits in western redcedar (Thuja plicata).

Western redcedar (WRC; Thuja plicata) is a conifer of the Pacific Northwest of North America prized for its durable and rot-resistant wood. WRC has naturally low outcrossing rates and readily self-fertilizes in nature. Challenges faced in WRC breeding and propagation involve selecting trees for accelerated growth while also ensuring enhanced heartwood rot resistance and resistance to ungulate browsing, as well as mitigating potential effects of inbreeding depression. Terpenes, a large and diverse class of specialized metabolites, confer both rot and browse resistance in the wood and foliage of WRC, respectively. Using a Bayesian modelling approach, we isolated single nucleotide polymorphism (SNP) markers estimated to be associated with three different foliar terpene traits and four different heartwood terpene traits, as well as two growth traits. We found that all traits were complex, being associated with between 1700 and 3600 SNPs linked with putatively causal loci, with significant polygenic components. Growth traits tended to have a larger polygenic component while terpene traits had larger major gene components; SNPs with small or polygenic effect were spread across the genome, while larger-effect SNPs tended to be localized to specific linkage groups. To determine whether there was inbreeding depression for terpene chemistry or growth traits, we used mixed linear models for a genomic selection training population to estimate the effect of the inbreeding coefficient F on foliar terpenes, heartwood terpenes and several growth and dendrochronological traits. We did not find significant inbreeding depression for any assessed trait. We further assessed inbreeding depression across four generations of complete selfing and found that not only was inbreeding depression not significant but that selection for height growth was the only significant predictor for growth during selfing, suggesting that inbreeding depression due to selfing during operational breeding can be mitigated by increased selection intensity.

Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.

Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

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.

Effects of single or serial embryo splitting on the development and morphokinetics of in vitro produced bovine embryos

Embryo splitting can be used in cattle in vitro production (IVP) to improve embryo availability and to increase selection intensity. Despite this widespread utility, a comparative investigation of the viability of IVP embryos split at Day 2 (2-cell stage), Day 3 (8-cell stage), and blastocyst stage has not been undertaken. Similarly, the suitability of splitting Day 3 embryos with atypical numbers of blastomeres, and the feasibility of serial-splitting cleavage stage embryos, have not been investigated in cattle. Here, we demonstrate that the strategy most likely to produce the greatest output of viable embryos is the splitting of Day 3 embryos into four parts, regardless of whether embryos with exactly eight cells or an atypical number of blastomeres are used. This approach was found to produce 1.8 blastocysts per zygote on average compared to just 0.4 blastocysts per zygote for non-split controls. Single-splitting was also found to be superior to serial-splitting which, whilst feasible, impaired embryo viability as judged by cell number at day 7 post-insemination. Interestingly, zygotes (≥2 cells) split once on either Day 2 or Day 3 post-insemination, whilst resulting in smaller blastocysts than control embryos, displayed higher cell counts than expected at the blastocyst stage, suggesting a compensatory mechanism might be at play. Indeed, time-lapse imagery revealed that zygotes split at 2-cells reached the compact morula and expanded blastocyst stages earlier than either those split at Day 3 or non-split controls. Developmental events between splits originating from the same progenitor appeared well synchronized only up to the third cleavage division.

Screening clonally replicated Acacia mangium breeding populations for tolerance to Ceratocystis canker and wilt disease

Three screening trials of clonally replicated Acacia mangium seedlings were evaluated for survival and lesion length following inoculation with locally collected strains of Ceratocystis in Indonesia. Tolerance in the population was low with 6.7% of the 1033 clones represented by more than 4 ramets surviving repeated inoculations. Differences in tolerance among populations were slight; however, populations with consistently higher survival and shorter lesion lengths were from Papua New Guinea rather than Queensland. Estimates of the proportion of the experimental variation attributable to differences among parents (heritability) were low to moderate for both survival and lesion length. Estimates of the proportion of the experimental variation that was attributable to differences among clones (repeatability) were greater but typically similar to the heritability estimates, indicating that initial improvements from selection will primarily be derived from identifying tolerant parents. While genetic correlations among experiments were positive, estimates could not exclude the existence of host–pathogen interactions. Two validation trials of the tolerant clones were assessed 9 months after establishment; these trials verified that one-third of the clones identified in the nursery screening were also tolerant to Ceratocystis in field trials. The experiments confirmed that nursery screening may be used to quickly focus efforts on parents that produce more tolerant progeny, screening additional seedlings to increase selection intensity rather than using clonal replication to increase accuracy would lead to greater improvements in tolerance and field trials are required to verify disease tolerance at later ages.

Development of NIRS re-calibration model for ethiopian barley (Hordeum vulgare) lines traits to determine their brewing potential

Barley is one of the top five grain crops in Ethiopia, covering approximately 1 million hectares, and is grown for food, animal's feed and industrial purposes. Ethiopian Agricultural Research Institute (EIAR) is also conducting a barley research and breeding program with the aim of improving grain yield and resilience through screening lines. Sample materials used for the experiment were taken from 2018, 2019 and 2020 seasons to capture the variation in the growing year. As a result, the spectral data of the samples and the wet chemical data were generated using official international methods. Subsequently, a recalibration based on 280 samples of breeding lines of different genotypes, growing years and locations was developed in order to predict the protein, extract and friability content characteristics of important barley malt parameters. Wet chemical data were collected from processed/malted barley grains. A calibration model was created on the basis of the calibration software OPUS LAB 7.5 from Bruker Optics GmbH. The protein calibration (R 2 c = 0.90; RPD = 3.8) can be viewed as broadly applicable; the extract content (R 2 c = 0.88 and RPD = 2.8) and friability (R 2 c = 0.85 and RPD = 2.7) could be useful as well as a good predictive model for line screening. The calibration model was missing to predict long-range sample sources before the model range was expanded by recalibration process including different sample sources. Therefore, these NIRS models enable the selection of suitable food and malt barley genotypes at wide range. Since NIRS is fast and inexpensive, the barley improvement program can increase selection intensity, especially in the early testing phase. So the best precision of the NIRS recalibration discovered in this study enables to identify top candidate lines with a minimum of errors. • The aim was predicting grain barley quality trait using near infrared spectroscopy. • The 2018, 2019 and 2020 seasons of 280 barley genotypes were used for the study. • Models developed for protein, extract & friability from reference ^ spectral data. • Protein, extract & friability model predictive result is 3.8, 2.8 and 2.7 respectively. • A predictive model (>2.5) & (2–2.5) are excellent and good respectively.

Ignoring genotype by environment interaction in the genetic evaluation of dairy cattle reduces accuracy but may increase selection intensity

Genotype by environment interaction (G×E) may exist for traits that are expressed in different environments. The G×E is often ignored in the genetic evaluation of selection candidates. We hypothesized that genetic gain in 2 environments is always higher when the true value of the genetic correlation (rg) between traits expressed in different environments is considered in the genetic evaluation. We tested this hypothesis by stochastic simulation of dairy cattle breeding programs in a mainstream and a niche environment. The rg was varied from 0 to 1 in steps of 0.1. We simulated the following 3 scenarios: 1Trait_1Index, 2Traits_1Index, and 2Traits_2Indices. The G×E was ignored in the genetic evaluation in the scenario with 1Trait and included in scenarios with 2Traits. Selection was based on the mainstream selection index in both environments in scenarios with 1Index. Selection in the mainstream environment was based on the mainstream selection index and selection in the niche environment was based on the niche selection index in the scenario with 2Indices. With moderate G×E (rg between 0.6 and 0.9), the highest genetic gain was achieved in the niche environment by selecting for the mainstream selection index and ignoring G×E. At lower rg, the highest genetic gain was achieved when considering G×E and selecting for the niche selection index. For the mainstream environment, it was never an advantage to ignore G×E. Therefore, although our hypothesis was confirmed in most cases, there were cases where ignoring G×E was the better option, and using the correct evaluation led to inferior genetic gain. The results of the current study can be used in animal breeding programs that encompass multiple environments.

Genomic relationship–based genetic parameters and prospects of genomic selection for growth and wood quality traits in Eucalyptus benthamii

The unique adaptation of Eucalyptus benthamii to low temperatures coupled to fast growth and versatile wood quality has made it a valued plantation species in frost-prone areas worldwide, but little is known on its quantitative genetic parameters for key industrial traits. We used GBLUP additive (GA), additive-dominant (GAD), single-step (HBLUP), and pedigree-based predictive models to estimate lignin, extractives, carbohydrates, and wood density at age 4 and tree volume at age 6. By capturing hidden relatedness and correcting pedigree errors, SNP data disentangled non-additive from additive variance providing more realistic estimates of narrow-sense heritability than pedigrees, and more accurate predictions of trait values. Predictive abilities (PAs) ranged from 0.12 for volume (pedigree-based model) to 0.44 for wood density (models H, GA, and GAD). Considerable dominance variance was seen for all traits, growth was the trait most influenced by it, resulting in PAs 48.9% higher when this effect is considered, a result with important consequences both for clonal propagation and overall selection efficiency (Seff). Using a HBLUP model, phenotypes of non-genotyped trees increased PAs by increasing sample size and provided realized relationships with reduced genotyping cost. In a recurrent selection program, the preclusion of progeny testing provides an increase in Seff between 232% and 299%. In a clonal selection program, the elimination of both progeny and initial clonal trial may increase Seff between 134% and 277%. Increasing selection intensity by genomic prediction resulted in an additional impact on Seff. This study provides groundwork to implement genomic selection in E. benthamii breeding.

Genetic parameter and genetic trend estimates for reproductive traits in Wistar rats

ABSTRACT The aim of this study was to estimate genetic parameters and genetic trends for reproductive traits in Wistar rats. A total of 1,167 data records from 283 females over six generations of monogamous mating pairs was used. Heritability and genetic correlation were estimated through Bayesian inference and genetic trends were calculated by linear regression of breeding values over generations. Heritability estimates for litter size at birth (LS), calving interval (CI), pup mortality (PM) and maternal cannibalism (CAN) presented low magnitude, ranging from 0.01 to 0.13. CAN presented high and positive genetic correlation with LS and PM (0.77 and 0.78, respectively). On the other hand, all the other estimated genetic correlations were not significant. Genetic trend was positive for LS (+0.0900 pups per generation), and negative for PM and CAN (-1.0085 and -0.5217 pups per generation, respectively). For CI the genetic trend was not significant. It is recommended to increase selection intensity on dams in this Wistar rat population in order to accelerate the genetic progress.

Genetic-quantitative analysis for reproductive traits in Nellor cattle selected for sexual precocity

Context The selection of animals for sexual precocity and reproductive efficiency is a trend to reduce the production cycle, promote higher economic viability to the system, increase selection intensity and higher genetic gain, as well to promote the profitability of production systems. To include these traits as selection criterion in cattle breeding programs, estimating genetic parameters and studying the possibility of obtaining genetic gains is required. The hypothesis tested was that the indicators of sexual precocity traits present genetic variance and moderate heritability that allows these to be used as a criterion of selection to obtain improvement for sexual precocity, without negative implications for the reproductive efficiency in Nellore cattle. Aims This study was carried out to estimate the genetic parameters for reproductive traits (scrotal circumference at 365 and 450 days of age, gestation length, days open, calving interval, real fertility, cumulative productivity, calf weight:cow weight ratio) and age at first conception and first calving in a Nellore cattle herd under selection for sexual precocity. Methods Data of reproductive traits and indicators of sexual precocity traits from 4081 Nellore cattle born between 2009 and 2015 were used. The covariance components, heritabilities and correlations were estimated using the restricted maximum likelihood method, available in the BLUPF90 package, in single- and multiple-trait animal mixed models. Key results Estimates of heritability and standard errors for scrotal circumference at 365 and 450 days of age, gestation length, days open, calving interval, real fertility, cumulative productivity, calf weight:cow weight ratio, age at first conception (AFCo) and age at first calving (AFCa) were 0.33 ± 0.03, 0.33 ± 0.01, 0.23 ± 0.03, 0.34 ± 0.11, 0.23 ± 0.12, 0.21 ± 0.16, 0.23 ± 0.08, 0.25 ± 0.10, 0.21 ± 0.08 and 0.24 ± 0.08 respectively. The genetic correlations estimated between AFCo and the other reproductive traits ranged from –0.61 to 0.14 (standard error 0.1–0.21), and between AFCa and the other reproductive traits ranged from –0.60 to 0.16 (standard error 0.1–0.19), all of them in a favourable direction. Conclusions Selection for sexual precocity based on AFCo and AFCa may promote improvement in reproductive efficiency and fertility, except for the calf weight:cow weight ratio, whose correlation was close to zero. Implications The estimates of heritabilities and genetic correlations suggest that selection programs for reproductive traits and indicators of sexual precocity traits for the Nellore breed may provide genetic gain. In addition, considering the genetic correlation obtained between AFCo and AFCa (0.96), when the selection objective is to increase the sexual precocity of heifers, we could use the age of the first conception as criterion, as the measurement of this trait occurs at a lower age at first calving, which implies reducing the time required for animal evaluation and decision-making.

From mass selection to genomic selection: one century of breeding for quantitative yield components of oil palm (Elaeis guineensis Jacq.)

More efficient methods are required to breed oil palm (Elaeis guineensis Jacq.) for yield maximization in order to meet the increased demand for palm oil while limiting environmental impacts. This review article analyzes the evolution of breeding schemes for oil palm yield and its quantative components and the changes expected to take place with genomic selection (GS). Genetic improvement of oil palm yield started in the 1920s through mass selection. Later, several disruptive improvements dramatically increased the rate of genetic progress: (1) understanding the heredity of fruit form and the adoption of tenera, with thicker mesocarp, in plantations; (2) the discovery of hybrid vigor and the adoption of modified reciprocal recurrent selection; and (3) clonal selection, exploiting intra-hybrid variability. In addition, the use of linear mixed models to estimate genetic values has made selection more efficient. Today, GS appears to be a new disruptive improvement that can speed up breeding schemes by avoiding field trials in some cycles and increase selection intensity by evaluating more candidates. The genetic potential for oil palm yield has increased considerably over one century of breeding. GS is expected to bring the rate of genetic progress to a previously unprecedented level. The future studies on oil palm GS will aim at making it efficient for all yield components. For this purpose, they should focus in particular on the optimization of training populations and on the improvement of prediction models. Minimizing environmental impacts will also require improvement in other aspects (resistance to diseases, cultural practices, etc.).

Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods. It was demonstrated that, in rice, the predictions using a combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including least absolute shrinkage and selection operator (LASSO), stochastic search variable selection (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least squares regression (PLS). This study has provided guidelines for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability. The answer to these questions will benefit academic research and will also greatly reduce the operative cost for the industry which specializes in breeding and selection.

Genetic Parameters of Body Length and Response to Selection for Growth Across Four Generations of Artemia sinica

To investigate the genetic components of growth in the brine shrimp Artemia sinica, we estimated the genetic parameters of body length and the response to selection using a fully pedigreed population of A. sinica. The base population was generated from four wild founder populations. We tested 4160 offspring in 360 families over four generations for growth and survival performance. Across four generations, we produced full- and half-sib families with nested mating, where two dams were mated to the same sire. Individual body length was measured for each nauplius at day 20 post-hatching. Heritability of body length was estimated across four generations with the restricted maximum likelihood method. The heritability of body length in A. sinica was low (0.14 ± 0.05), and the common environmental effect was 0.14 ± 0.02. We estimated the response to selection for body length by calculating the difference in the mean breeding values between different generations. The accumulated genetic gain in body length was 278.94 μm after three generations of selection. This low response to selection was probably caused by the low heritability of body length, small sample size, and the low selection intensity (50%). The results suggest that A. sinica selective breeding programs must be changed to generate any substantial, sustainable genetic increases in body length. We suggest that optimal genetic gains could be achieved by introducing wild strains into the nuclear breeding population to increase genetic variation, and by increasing the size of the breeding population to allow for increased selection intensity.

Optimizing female allocation to reproductive technologies considering merit, inbreeding and cost in nucleus breeding programmes with genomic selection.

Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro fertilization and embryo transfer (JIVET) have been shown to accelerate genetic gain by increasing selection intensity and decreasing generation interval. Genomic selection (GS) increases the accuracy of selection of young candidates which can further accelerate genetic gain. Optimal contribution selection (OCS) is an effective method of keeping the rate of inbreeding at a sustainable level while increasing genetic merit. OCS could also be used to selectively and optimally allocate reproductive technologies in mate selection while accounting for their cost. This study uses stochastic simulation to simulate breeding programmes that use a combination of artificial insemination (AI) or natural mating (N), MOET and JIVET with GS. OCS was used to restrict inbreeding to 1.0% increase per generation and also to optimize use of reproductive technologies, considering their effect on genetic gain as well as their cost. Two Australian sheep breeding objectives were used as an example to illustrate the methodology-a terminal sire breeding objective (A) and a dual-purpose self-replacing breeding objective (B). The objective function used for optimization considered genetic merit, constrained inbreeding and cost of technologies where costs were offset by a premium paid to the seedstock breeder investing in female reproductive technologies. The premium was based on the cumulative discounted expression of genetic merit in the progeny of a commercial tier in the breeding programme multiplied by the proportion of that benefit received by the breeder. With breeding objective B, the highest premium of 64% paid to the breeder resulted in the highest allocation of reproductive technologies (4%-10% for MOET and 19%-54% for JIVET) and hence the highest annual genetic gain. Conversely, breeding objective A, which had a lower dollar value of the breeding objective and a maximum of 5% mating types for JIVET and zero for MOET were optimal, even when highest premiums were paid. This study highlights that the level of investment in breeding technologies to accelerate genetic gain depends on the investment of genetic improvement returned to the breeder per index point gain achieved. It also demonstrates that breeding programmes can be optimized including allocation of reproductive technologies at the individual animal level. Accounting for revenue to the breeder and cost of the technologies can facilitate more practical decision support for beef and sheep breeders.