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The inheritance of racing performance in Thoroughbreds is of interest to breeders and geneticists. Therefore, the genetic parameters of racing performance have been investigated in various populations of Thoroughbreds. However, the predictability of the racing performance of a racehorse has not been assessed well. In this study, we built mixed‐effects models for Japanese Thoroughbreds and assessed their predictability of racing performance. We used the average velocity as an index of racing performance and treated each category of racecourse and distance as different traits. Model selection using the deviance information criterion showed that explanatory variables, such as race, age and jockey effects are important for racing performance. Using the selected models, the phenotypic values of horses born after 2009, adjusted using the entire dataset, were predicted with the breeding values estimated from a partial dataset until 2010. The correlation coefficients ranged from 0.000 to 0.235 (average of 0.084 ± 0.066) and were higher for longer distances. When predicting the graded race winners born after 2009 from the partial dataset until 2010, the area under the curve values ranged from 0.516 to 0.776 (average of 0.613 ± 0.073) and were also higher for longer distances. Although these results indicate the predictability of racing performance, further efforts, including exploring more suitable racing performance indices and refining statistical modelling, are required for improvement. [ABSTRACT FROM AUTHOR]

Motivation Genomic prediction is now an essential technique in breeding and medicine, and it is interesting to see how omics data can be used to improve prediction accuracy. Precedent work proposed a metabolic network-based method in biomass prediction of Arabidopsis; however, the method consists of multiple steps that possibly degrade prediction accuracy. Results We proposed a Bayesian model that integrates all steps and jointly infers all fluxes of reactions related to biomass production. The proposed model showed higher accuracies than methods compared both in simulated and real data. The findings support the previous excellent idea that metabolic network information can be used for prediction. Availability and implementation All R and stan scripts to reproduce the results of this study are available at https://github.com/Onogi/MetabolicModeling. [ABSTRACT FROM AUTHOR]

Summary An R package that can implement multiple linear learners, including penalized regression and regression with spike and slab priors, in a single model has been developed. Solutions are obtained with fast minorize-maximization algorithms in the framework of variational Bayesian inference. This package helps to incorporate multimodal and high-dimensional explanatory variables in a single regression model. Availability and implementation The R package VIGoR (Variational Bayesian Inference for Genome-wide Regression) is available at the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/) and at GitHub (https://github.com/Onogi/VIGoR). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract Background A Bayesian approach based on a Dirichlet process (DP) prior is useful for inferring genetic population structures because it can infer the number of populations and the assignment of individuals simultaneously. However, the properties of the DP prior method are not well understood, and therefore, the use of this method is relatively uncommon. We characterized the DP prior method to increase its practical use. Results First, we evaluated the usefulness of the sequentially-allocated merge-split (SAMS) sampler, which is a technique for improving the mixing of Markov chain Monte Carlo algorithms. Although this sampler has been implemented in a preceding program, HWLER, its effectiveness has not been investigated. We showed that this sampler was effective for population structure analysis. Implementation of this sampler was useful with regard to the accuracy of inference and computational time. Second, we examined the effect of a hyperparameter for the prior distribution of allele frequencies and showed that the specification of this parameter was important and could be resolved by considering the parameter as a variable. Third, we compared the DP prior method with other Bayesian clustering methods and showed that the DP prior method was suitable for data sets with unbalanced sample sizes among populations. In contrast, although current popular algorithms for population structure analysis, such as those implemented in STRUCTURE, were suitable for data sets with uniform sample sizes, inferences with these algorithms for unbalanced sample sizes tended to be less accurate than those with the DP prior method. Conclusions The clustering method based on the DP prior was found to be useful because it can infer the number of populations and simultaneously assign individuals into populations, and it is suitable for data sets with unbalanced sample sizes among populations. Here we presented a novel program, DPART, that implements the SAMS sampler and can consider the hyperparameter for the prior distribution of allele frequencies to be a variable.

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided., (© 2022. The Author(s).)

We investigated whether regular changes of the sire in a breeding farm of Hokkaido Native Horses (HKDs) enables the DNA-level genetic variation of the produced animals to be maintained. The genotypes of 31 microsatellite markers were identified and analyzed in 207 animals produced in a breeding farm in which the sire was replaced every 3 to 5 years. The mean allele number indicating the degree of genetic variation was 5.97 and was similar to those reported previously. The mean observed heterozygosity was 0.74 and was higher than the expected heterozygosity, 0.69; F IS was -0.07, indicating that the analyzed animals reflected frequent outbreeding and had maintained genetic variation. Based on genetic structural analysis, the number of genetic subpopulations of the animals was estimated to be as 6, and the majority (more than 50%) of each subpopulation corresponded to the progeny of one of the sires used in the breeding farm; these observations suggested that genetic variation in the analyzed animals reflected the genetic differences among sires. Pedigree records indicated that the average co-ancestry coefficient between sires used in the breeding farm was 0.015 corresponding to second cousin. This level of kinship among sires is acceptable for producing HKDs that maintain genetic variation., (© 2019 Japanese Society of Animal Science.)