Training Population Research Articles

Research on the Difficulty of 9th Level Dan Promotion Test of Taekwondo

Purpose: This study aims to analyze the essential Poomsae difficulty of Taekwondo 9th level Dan promotion test with the awareness of the problem. This will help standardize the Taekwondo curriculum in order to meet the Taekwondo education goals pursued by the Kukkiwon, the headquarters of the World Taekwondo. Method: The literature research and in-depth interviews with people to be tested were performed to analyze the difficulty of essential Poomsae of Taekwondo 9th Dan promotion test. Results: As a result of questioning and analyzing the level of difficulty of the 9th level Dan promotion winner, it was concluded that the level of difficulty of Illyo Poomsae of the 9th level Dan promotion test is not appropriate. Courses to be completed for Color and Black Belts must comply with the completion time of the essential course and the selection course. Basic courses for Black Belt (Poomsae, breaking, sparring, and KTA personality) must be completed as mandatory courses, and in-depth courses (KTA self-defence techniques, KTA practical hand skills, and KTA core exercises) must be assigned. Conclusion: It is thought that Taekwondo 9th level Dan, the highest stage of the competition held at the Kuk-kiwon of the World Taekwondo Headquarters, should be educated to learn, guide, and evaluate Taekwondo ac-cording to the standard curriculum. However, the standardized Taekwondo curriculum should be distributed in consideration of the current Taekwondo training population, where the standardized Taekwondo curriculum has not been established. Taekwondo leaders will contribute to the educational use of Taekwondo by analyzing the essential Poomsae and curriculum of Taekwondo 9th level Dan promotion test and presenting the evaluation rules based on the Taekwondo standard curriculum.

A bulked segregant analysis tool for out-crossing species (BSATOS) and QTL-based genomics-assisted prediction of complex traits in apple

IntroductionGenomic heterozygosity, self-incompatibility, and rich-in somatic mutations hinder the molecular breeding efficiency of outcrossing plants. ObjectivesWe attempted to develop an efficient integrated strategy to identify quantitative trait loci (QTLs) and trait-associated genes, to develop gene markers, and to construct genomics-assisted prediction (GAP) modes. MethodsA novel protocol, bulked segregant analysis tool for out-crossing species (BSATOS), is presented here, which is characterized by taking full advantage of all segregation patterns (including AB × AB markers) and haplotype information. To verify the effectiveness of the protocol in dealing with the complex traits of outbreeding species, three apple cross populations with 9,654 individuals were adopted. ResultsBy using BSATOS, 90, 60, and 77 significant QTLs were identified successfully and candidate genes were predicted for apple fruit weight (FW), fruit ripening date (FRD), and fruit soluble solid content (SSC), respectively. The gene-based markers were developed and genotyped for 1,396 individuals in a training population, including 145 Malus accessions and 1,251 F1 plants of the three full-sib families. GAP models were trained using marker genotype effect estimates of the training population. The prediction accuracy was 0.7658, 0.6455, and 0.3758 for FW, FRD, and SSC, respectively. ConclusionThe BSATOS and GAP models provided a convenient and efficient methodology for candidate gene mining and molecular breeding in out-crossing plant species. The BSATOS pipeline can be freely downloaded from: https://github.com/maypoleflyn/BSATOS.

Physical culture and sports in the life of the Russian population

Involvement of the population in physical training and sports determines the level of public health. The Russian government today has taken a firm course to involve Russians of all ages in physical culture and sports activities. However, the widespread introduction of physical culture and sports practices into the daily life of Russians requires a clear understanding of the motives and incentives of the population for systematical physical training and sports, as well as identification of the barriers to physical culture and sports activity. The article presents the results of a sociological study, the purpose of which was to identify the motives and incentives of the Russian population for systematic physical training and sports, as well as factors that hinder physical education. The analysis of the social situation in the field of physical culture and sports is based on the results of 16 focus groups conducted with various categories of population ranging from parents of children aged 3-5 to people over 60 living in various cities of Russia. It is shown that the vast majority of parents of children, adolescents, youth, people of middle and older ages have formed sufficiently stable attitudes for regular physical education. However, they are not always implemented due to a number of barriers. Among them are: high cost of sports sections, subscriptions to fitness and gyms, payment for a personal trainer; lack of specialized sports organizations, lack of necessary equipment in them; high engagement of the population in labor, educational and household activities; fears for the life and health of children, fear of great physical strain and chronic diseases; psychological barriers and stereotypes: adolescents experience embarrassment associated with the presence of unfamiliar spectators; middle-aged and older people are afraid of being judged by other people, they are afraid to look ridiculous. The results of the study can become the basis for development of measures to motivate the Russian population to systematic physical training and sports, as well as measures to overcome the barriers hindering physical culture and sports activity of Russians.

Strategies of preserving genetic diversity while maximizing genetic response from implementing genomic selection in pulse breeding programs

Key messageGenomic selection maximizes genetic gain by recycling parents to germplasm pool earlier and preserves genetic diversity by restricting the number of fixed alleles and the relationship in pulse breeding programs.Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at F1 or F2 stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F1 intercross (F1i) families and keeping the total number of progeny of F1i families constant, we observed a decrease in genetic gain and increase in genetic diversity, whereas increasing the number of progeny per F1i family while keeping a constant number of F1i families increased the rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F2 family phenotypes to the training population increased the accuracy of genomic breeding values (GEBVs) and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program.

Pulse-frequency-dependent resonance in a population of pyramidal neuron models.

Stochastic resonance is known as a phenomenon whereby information transmission of weak signal or subthreshold stimuli can be enhanced by additive random noise with a suitable intensity. Another phenomenon induced by applying deterministic pulsatile electric stimuli with a pulse frequency, commonly used for deep brain stimulation (DBS), was also shown to improve signal-to-noise ratio in neuron models. The objective of this study was to test the hypothesis that pulsatile high-frequency stimulation could improve the detection of both sub- and suprathreshold synaptic stimuli by tuning the frequency of the stimulation in a population of pyramidal neuron models. Computer simulations showed that mutual information estimated from a population of neural spike trains displayed a typical resonance curve with a peak value of the pulse frequency at 80-120 Hz, similar to those utilized for DBS in clinical situations. It is concluded that a "pulse-frequency-dependent resonance" (PFDR) can enhance information transmission over a broad range of synaptically connected networks. Since the resonance frequency matches that used clinically, PFDR could contribute to the mechanism of the therapeutic effect of DBS.

Optimizing Plant Breeding Programs for Genomic Selection

Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) was proposed to estimate all markers simultaneously, thereby capturing all their effects. However, breeding programs are still struggling to identify the best strategy to implement it into their programs. Traditional breeding programs need to be optimized to implement GS effectively. This review explores the optimization of breeding programs for variety release based on aspects of the breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing the number of lines evaluated, and leveraging the large amount of genomic and phenotypic data collected across different growing seasons and environments to increase heritability estimates, selection intensity, and selection accuracy. Breeding programs can leverage their phenotypic and genotypic data to maximize genetic gain and selection accuracy through GS methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, and deep learning approaches. Overall, this review describes various methods that plant breeders can utilize to increase genetic gains and effectively implement GS in breeding.

Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations.

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

Prediction of Energy Demand by Socio-Economic and Climatic Indicators Using ANN optimized by Developed Search and Rescue (DSAR) Algorithm

With the highest energy growth rate and the fastest economic growth, China has more serious concerns about supplying energy to its economy. Therefore, one of the methods of energy management in this country is to forecast energy demand for the coming years. The main purpose of this study is to predict energy demand under the influence of socio-economic indicators by Artificial Neural Network (A = NN). More accurate forecasts make more reliable decisions to manage energy. For this purpose, in this study, innovation in the ANN method has been presented. This innovation is to use a developed version of a newly introduced metaheuristic. This developed algorithm increases the accuracy of prediction by solving optimization problems. This algorithm is a newly developed version of the Search and Rescue (SAR) algorithm. The results of predictions showed that the parameters population and GDP (per capita) have the highest correlation in train and test steps. The correlation between population and GDP (per capita) in training are R2 = 0.675 and R2 = 0.652, respectively. Also, the correlation between population and GDP in testing are R2= 0.624 and R2 = 0.519, respectively. The results also showed that in the coming years, society will face an upward trend in demand. Therefore, with the knowledge of this increase in demand, it is possible to manage energy demand in the coming years by managing energy in the current years.

Prioritizing the Vitality of the Athletic Training Profession.

Exploring concepts and constructs related to the work-life balance of athletic trainers has been a priority of ours for years, and it has been an honor to serve as guest editors for this special issue of the Journal of Athletic Training. In 2019, the Strategic Alliance released its prioritized research agenda for the athletic training profession and included the exploration of solutions to improve work-life balance.1 The prioritized research agenda came on the heels of the National Athletic Trainers' Association position statement on facilitating work-life balance for the athletic trainer,2 a statement that not only advocated for improved quality of life but also recognized the need for more research in work-life balance. Work-life balance is one possible individual outcome of the work-life interface, or the overlap between work and personal roles.3 It has always been our belief that in order to suggest and implement solutions to address concerns related to the work-life interface, we need a better understanding of what those concerns are, which is what prompted us to advocate for and create this special issue.The athletic training literature related to the work-life interface is robust and has roots dating back to the late 1980s, before athletic training was recognized as an allied health profession by the American Medical Association in 1990. We acknowledge that work-life balance is not unique to athletic trainers and our profession; however, the nature of our job does result in some unique causative factors. In 1986, Capel4 found that role conflict, role ambiguity, locus of control, number of hours in direct contact with patients, and number of patients in the athletic trainer's direct care were significantly related to burnout, an antecedent contributing to career departure.5,6 In recent years, the multifactorial nature of the work-life interface has been explored in the literature,3,7 and the effects of societal (gender ideology, cultural norms), organizational (work hours, organizational culture), and individual (sex, personality) factors on numerous individual- and organizational-level outcomes, including work-life balance, turnover, and job performance, have been studied.Over the last decade, we have learned that every athletic trainer, at some point in his or her career, likely faces challenges with work-life balance, regardless of sex or marital or family status. In a recent systematic review,8 burnout was observed in all subsets of the athletic training population, including students, graduate assistants, staff, and faculty. We do, however, know that different causative factors may be responsible for the challenges, which can include various personality traits, societal norms, and the ideologies associated with gender roles (pressure for men to be the breadwinner or women feeling guilty about not being at home). Athletic trainers work in a variety of settings, yet work-life balance challenges occur. Although the factors contributing to the challenges may be setting dependent, conflicts result.The need for more research stems from potential negative outcomes such as job burnout, job dissatisfaction, and turnover, as they are likely to have indirect effects on patient care. Moreover, athletic trainers' inability to find balance has implications for their personal lives, as they experience guilt for not being able to give as much to their nonwork roles, especially parenting. As investigators, we have only scratched the surface of the work-life interface phenomenon and recognize that there is more to learn and understand. A special issue dedicated to this research is a testament to the importance of the topic.

Genomic prediction models for traits differing in heritability for soybean, rice, and maize

BackgroundGenomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures.ResultsCorrelations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions.ConclusionsWe conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.

The effect of flywheel training on strength and physical capacities in sporting and healthy populations: An umbrella review.

The aim of this umbrella review was to provide a detailed summary of how flywheel training enhances strength and physical capacities in healthy and athletic populations. The eleven reviews included were analyzed for methodological quality according to the Assessing the Methodological Quality of Systematic Review 2 (AMSTAR 2) and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. Two were systematic reviews, six were systematic reviews with meta-analyses and three were narrative reviews. Although the included reviews support use of flywheel training with athletic and healthy populations, the umbrella review highlights disparity in methodological quality and over-reporting of studies (38 studies were included overall). Flywheel post-activation performance enhancement protocols can effectively enhance strength and physical capacities acutely with athletes and healthy populations. All relevant reviews support flywheel training as a valid alternative to traditional resistance training for enhancing muscular strength, power, and jump performance with untrained and trained populations alike. Similarly, reviews included report flywheel training enhances change of direction performance—although conclusions are based on a limited number of investigations. However, the reviews investigating the effect of flywheel training on sprint performance highlight some inconsistency in attained improvements with elite athletes (e.g., soccer players). To optimize training outcomes, it is recommended practitioners individualize (i.e., create inertia-power or inertia-velocity profiles) and periodize flywheel training using the latest guidelines. This umbrella review provides an analysis of the literature’s strengths and limitations, creating a clear scope for future investigations.

Protein prediction for trait mapping in diverse populations.

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f. sp. tritici) are commonly recorded in ordinal scales and percentages. Disease severity (SEV) and infection type (IT) data in germplasm screening nurseries generally do not follow these assumptions. On this regard, researchers may ignore the lack of normality, transform the phenotypes, use generalized linear models, or use supervised learning algorithms and classification models with no restriction on the distribution of response variables, which are less sensitive when modeling ordinal scores. The goal of this research was to compare classification and regression genomic selection models for skewed phenotypes using stripe rust SEV and IT in winter wheat. We extensively compared both regression and classification prediction models using two training populations composed of breeding lines phenotyped in 4 years (2016–2018 and 2020) and a diversity panel phenotyped in 4 years (2013–2016). The prediction models used 19,861 genotyping-by-sequencing single-nucleotide polymorphism markers. Overall, square root transformed phenotypes using ridge regression best linear unbiased prediction and support vector machine regression models displayed the highest combination of accuracy and relative efficiency across the regression and classification models. Furthermore, a classification system based on support vector machine and ordinal Bayesian models with a 2-Class scale for SEV reached the highest class accuracy of 0.99. This study showed that breeders can use linear and non-parametric regression models within their own breeding lines over combined years to accurately predict skewed phenotypes.

Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array

Key messageWe developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding.Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.

Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system

Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R2val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R2val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R2val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R2val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.

Pregnancy status predicted using milk mid-infrared spectra from dairy cattle

Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.

Improving the accuracy of genomic predictions in an outcrossing species with hybrid cultivars between heterozygote parents: a case study of oil palm (Elaeis guineensis Jacq.).

Genomic selection (GS) is a method of marker-assisted selection revolutionizing crop improvement, but it can still be optimized. For hybrid breeding between heterozygote parents of different populations or species, specific aspects can be considered to increase GS accuracy: (1) training population genotyping, i.e., only genotyping the hybrid parents or also a sample of hybrid individuals, and (2) marker effects modeling, i.e., using population-specific effects of single nucleotide polymorphism alleles model (PSAM) or across-population SNP genotype model (ASGM). Here, this was investigated empirically for the prediction of the performances of oil palm hybrids for yield traits. The GS model was trained on 352 hybrid crosses and validated on 213 independent hybrid crosses. The training and validation hybrid parents and 399 training hybrid individuals were genotyping by sequencing. Despite the small proportion of hybrid individuals genotyped and low parental heterozygosity, GS prediction accuracy increased on average by 5% (range 1.4-31.3%, depending on trait and model) when training was done using genomic data on hybrids and parents compared with only parental genomic data. With ASGM, GS prediction accuracy increased on average by 3% (- 10.2 to 40%, depending on trait and genotyping strategy) compared with PSAM. We conclude that the best GS strategy for oil palm is to aggregate genomic data of parents and hybrid individuals and to ignore the parental origin of marker alleles (ASGM). To gain a better insight into these results, future studies should examine the respective effect of capturing genetic variability within crosses and taking segregation distortion into account when genotyping hybrid individuals, and investigate the factors controlling the relative performances of ASGM and PSAM in hybrid crops.

Genomics-Enabled Management of Genetic Resources in Radiata Pine

Traditional tree improvement is cumbersome and costly. Our main objective was to assess the extent to which genomic data can currently accelerate and improve decision making in this field. We used diameter at breast height (DBH) and wood density (WD) data for 4430 tree genotypes and single-nucleotide polymorphism (SNP) data for 2446 tree genotypes. Pedigree reconstruction was performed using a combination of maximum likelihood parentage assignment and matching based on identity-by-state (IBS) similarity. In addition, we used best linear unbiased prediction (BLUP) methods to predict phenotypes using SNP markers (GBLUP), recorded pedigree information (ABLUP), and single-step “blended” BLUP (HBLUP) combining SNP and pedigree information. We substantially improved the accuracy of pedigree records, resolving the inconsistent parental information of 506 tree genotypes. This led to substantially increased predictive ability (i.e., by up to 87%) in HBLUP analyses compared to a baseline from ABLUP. Genomic prediction was possible across populations and within previously untested families with moderately large training populations (N = 800–1200 tree genotypes) and using as few as 2000–5000 SNP markers. HBLUP was generally more effective than traditional ABLUP approaches, particularly after dealing appropriately with pedigree uncertainties. Our study provides evidence that genome-wide marker data can significantly enhance tree improvement. The operational implementation of genomic selection has started in radiata pine breeding in New Zealand, but further reductions in DNA extraction and genotyping costs may be required to realise the full potential of this approach.

The impact of genetic relationship between training and validation populations on genomic prediction accuracy in Atlantic salmon

The potential of genomic selection (GS) to improve production traits has been widely demonstrated in many aquaculture species. Atlantic salmon breeding programmes typically consist of sibling testing schemes, where traits that cannot be measured on the selection candidates are measured on the candidates’ siblings. While annual testing on close relatives is effective, it is expensive due to high genotyping and phenotyping costs. Accurate prediction of breeding values in distant relatives could significantly reduce the cost of GS. This study aimed to evaluate the impact of decreasing the genomic relationship between the training and validation populations on the accuracy of genomic prediction for two key traits; body weight and resistance to sea lice; and to assess the interaction of genetic relationship with SNP density. Phenotype and genotype data from two year classes of a commercial breeding population of Atlantic salmon were used. The accuracy of genomic predictions were close to zero when the prediction was performed across year class, albeit this may reflect a lack of genetic correlation between the same traits measured in the different year classes. Within a year class, systematically reducing the relatedness between the training and validation populations resulted in decreasing accuracy of genomic prediction; when the training and validation populations were set up to contain no relatives with genomic relationships > 0.3, the accuracies decreased by 44% for sea lice count and by 53% for body weight. Less related training and validation populations also tended to result in highly biased predictions. No clear interaction between decreasing SNP density and relatedness between training and validation population was found. These results confirm the importance of close genetic relationships between training and selection populations in salmon breeding programmes, and suggests that prediction across generations using existing approaches would severely compromise the efficacy of GS.

Genomic breeding values\u2019 prediction including populational selfing rate in an open-pollinated Eucalyptus globulus breeding population

In forest tree breeding programs, open-pollinated families are frequently used to estimate genetic parameters and evaluate genetic merit of individuals. However, the presence of selfing events not documented in the pedigree affects the estimation of these parameters. In this study, 194 open-pollinated families of Eucalyptus globulus Labill. trees were used to compare the precision of estimated genetic parameters and accuracies of predicted breeding values with the conventional pedigree-based model (ABLUP) and the pedigree-genomic single-step model (ssGBLUP). The available genetic information for pairwise parent-offspring allows us to estimate an actual populational selfing rate of 5.4%. For all the growth and disease resistance traits evaluated, the inclusion of selfing rate was effective in reducing the upward bias, between 7 and 30%, in heritability estimates. The predictive abilities for ssGBLUP models were always higher than those for ABLUP models. In both cases, a considerable reduction of predictive abilities was observed when relatedness between training and validation populations was removed. We proposed a straightforward approach for the estimation of the actual selfing rate in a breeding population. The incorporation of this parameter allows for more reliable estimation of genetic parameters. Furthermore, our results proved that ssGBLUP was effective for the accurate estimation of genetic parameters and to improve the prediction of breeding values in presence of selfing events, thus a valuable tool for genomic evaluations in Eucalyptus breeding programs.