Search

ABSTRACT In Europe, before acceptance to a country's national list, crop variety candidates must undergo testing for value for cultivation and use (VCU) in multi‐environment trials. Once a variety is accepted to the national list of a country, it can be marketed in that county. Moreover, it may be marketed in other European countries as well, even if it has not been tested for VCU, and hence, there is no performance assessment in those other countries. This paper investigates how VCU trialling systems of two countries can be connected and integrated so that performance can be assessed for both countries without altering the capacity of the trialling systems. Our statistical calculations based on VCU trial data for maize from Germany and Poland highlight the benefit of a joint analysis of data from two countries. Moreover, we show how the efficiency of each country's VCU testing system can be improved—without altering the overall capacity of the systems in terms of the number of trials and the number of plots per trial—by ensuring that each variety is tested in both countries. [ABSTRACT FROM AUTHOR]

ABSTRACT Check varieties are used in plant breeding and variety testing for a number of reasons. One important use of checks is to provide connectivity between years, which facilitates comparison among genotypes of interest that are tested in different years. When long‐term data are available, such comparisons allow an assessment of realized genetic gain (RGG). Here, we consider the question of how many check varieties are needed per cycle for a reliable assessment of RGG. We propose an approach that makes use of variance component estimates for relevant random effects in a linear mixed model and plugs them into an analysis of dummy datasets set up to represent the design options being considered. Our results show that it is useful to employ a larger number of checks and to keep the replacement rate low. Furthermore, there is intercycle information to be recovered, especially when there are few checks and replacement rates are high, so modelling the cycle main effect as random pays off. [ABSTRACT FROM AUTHOR]

The study’s objectives were to assess the genetic parameters of reproductive characteristics in Sangsari sheep. A data set of reproductive information with pedigree details from 1995 to 2016 was used. Studied traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB) and total litter weight at weaning (TLWW). Test significance of the environmental factors to be included in the model was conducted using the general linear model procedure of the SAS program. All traits were significantly affected by the year of lambing and ewe age at lambing (P < 0.01). The AI-REML procedure of the Wombat program was used to evaluate genetic parameters. A series of bivariate animal models were employed to calculate genetic (rg) and phenotypic (rp) correlations between traits. The total least square means ± standard error of LSB and LSW were 1.04 ± 0.05 and 0.96 ± 0.03, while LMWLB, LMWLW, TLWB, and TLWW were 3.12 ± 0.08, 15.40 ± 0.13, 3.28 ± 0.04, and 19.31 ± 0.16 kg, respectively. The estimates of h2 were relatively low (P < 0.01) and ranged from 0.063 ± 0.028 for LSW to 0.181 ± 0.063 for TLWW. Repeatability estimates varied from 0.101 for LSW to 0.241 for TLWW. The sire service effects for LMWLB, TLWB, and TLWW were 0.012 ± 0.004, 0.023 ± 0.006, and 0.039 ± 0.009, respectively. The traits studied showed a greater magnitude of genetic correlation than phenotypic correlation, with values ranging from − 0.59 (LSB-LMWLB) to 0.87 (LMWLB-TLWB). It appears that focusing on TLWW for selection could result in more significant improvements in the reproductive performance of Sangsari ewes. [ABSTRACT FROM AUTHOR]

The present study aimed to estimate variance components and genetic parameters of the average daily gains from birth to 3-months (ADG1), 3- to 6-months (ADG2), 6- to 9-months (ADG3) and 9- to 12-months (ADG4) of age and corresponding Kleiber ratios (KR1, KR2, KR3 and KR4, respectively) in Jersey crossbred calves. Data for this study were collected from National Dairy Research Institute, Eastern Regional Station, Kalyani, West Bengal, India during 2013 to 2021. Genetic parameters for the traits were estimated using Bayesian procedure through Gibbs sampling by fitting six animal models. The deviance information criterion (DIC) was employed to determine the most appropriate model for each trait under investigation. Direct heritability estimates for ADGs and KRs ranged from 0.19 (ADG3) to 0.59 (ADG2) and 0.14 (KR3) to 0.72 (KR1), respectively. Significant maternal heritability ( m 2 ) was observed for average daily gains (17–24%) and Kleiber ratios (13–18%) at different age intervals under the best model. Estimates of permanent environmental effects ( c 2 ) of dam for ADG1, ADG2, KR2 and KR4 were only 1–4% of the total phenotypic variance in this study. Large negative estimates of correlations, ranging from -0.87 to -0.98, between direct and maternal (r a , m ) effects for ADGs and KRs (except KR3) at different age intervals were observed. Total heritability ( h t 2 ) and maternal repeatability (t m ) of the studied traits ranged 0.05–0.28 and 0.01–0.14, respectively. The moderate to high heritability estimates for all traits in this study indicate the possibility of genetic progress for these traits through selective breeding program. [ABSTRACT FROM AUTHOR]

Hāpuku (Polyprion oxygeneios) is a promising candidate for aquaculture production in New Zealand. Methods for spawning, juvenile production, and growout to harvest entirely on land, where water quality, pathogens, environmental impacts, and genetic "pollution" can be tightly controlled, have been developed, and genetic improvement to optimise land-based production is the obvious next step. However, estimates of genetic parameters are required to design a rigorous, disciplined, and effective selective breeding program. By using existing data consisting of irregularly spaced repeated measurements of fork length and live body weight collected on wild-collected founders and two generations of captively reared progeny, we evaluated the species' genetic potential for improvement in growth. We first tested a range of univariate random regression models to identify the best-fitting models for these data. Subsequently, using a bivariate model, we estimated variance components for growth trajectories of fork length and whole body weight. With one to six records available per fish, the best-fitting univariate models included only a fixed effect for contemporary groups and fixed and random genetic third-order Legendre polynomials. More complex models that included full-sib family and/or permanent environmental effects produced unacceptable constrained and/or non-positive-definite solutions. Both traits are moderately heritable at all stages of the growout phase (~0.4–0.5), and the genetic correlation patterns between daily breeding values estimated via the covariance function are different for length and weight. Genetic correlations for length between all pairs of age-specific breeding values are positive and strong (>0.7) and change gradually and smoothly with increasing temporal separation. For weight, these correlations deteriorate more rapidly with increasing time lags between measurements and become negative for some age pairings. We conclude that random regression analyses are a valuable tool for extracting genetic information from irregularly spaced repeated measurements of fish size, speculate that emerging technologies for high-throughput genotyping and phenotyping will add to the value of this approach in the near future, and reason that a breeding strategy that rigorously takes into account the potentially unfavourable genetic correlations between breeding values for weight at some ages will further adapt hāpuku to land-based systems and enhance the profitability commercial-scale production. [ABSTRACT FROM AUTHOR]

A useful strategy to examine serial correlation in panel regression models is to adopt serially correlated error terms. The random effect is also present to address the individual-specific heterogeneity. Familiar transformations can restructure the AR(1) process into a serially uncorrelated dynamic model. It may result in unfitting estimation results once the statistical inference is carried on the conditional likelihood. We discuss in the article the consequence of this action. In particular, in the case of large cross-section units and short time-sequences, we provide analytical expressions for the asymptotic bias of maximum likelihood estimates when the analyst naïvely neglects the initial conditions problem. Under several conditions, we compute the size of biases and measure the mean squared errors to illustrate this violation. We also construct a joint regression model of the initial and subsequent responses to handle the initial conditions. Then, we conduct simulation studies to determine the asymptotic behavior. [ABSTRACT FROM AUTHOR]

Estimating heritabilities with large genomic models by established methods such as restricted maximum likelihood (REML) or Bayesian via Gibbs sampling is computationally expensive. Alternatively, heritability can be estimated indirectly by method R and by maximum predictivity, referred to as MaxPred here, at a much lower computing cost. By method R, the heritability used for predictions with whole and partial data is considered the best estimate when the predictions based on partial data are unbiased relative to those with the complete data. By MaxPred, the heritability estimate is the one that maximizes predictivity. This study compared heritability estimation with genomic information using average information REML (AI–REML), method R and MaxPred. A simulated population was generated with ten generations of 5000 animals each and an effective population size of 80. Each animal had one record for a trait with a heritability of 0.3, a phenotypic variance of 10.0 and was genotyped at 50 k SNP. In method R, the heritability estimate is found when the expectation of a regression coefficient is equal to one. The regression is the EBV of selection candidates calculated with the whole dataset regressed on the EBV of candidates calculated from a partial dataset. In this study, we used the GBLUP framework and therefore, GEBV was calculated. The partial dataset was created by removing the last generation of phenotypes. Predictivity was defined as the correlation between the adjusted phenotypes of the selection candidates and their GEBV calculated from the partial data. We estimated the heritability for populations that included between three and 10 generations. In every scenario, predictivity increased as more data was used and was the highest at the simulated heritability. However, the predictivity for all data subsets and all heritabilities compared did not differ more than 0.01, suggesting MaxPred is not the best indication for heritability estimation. For the whole dataset, the heritability was estimated as 0.30 ± 0.01, 0.26 ± 0.01 and 0.30 ± 0.04 for AI–REML without genomics, AI–REML with genomics and method R with genomics, respectively. Heritability estimation with genomics by method R reduced timing by 83%, implying a reduction in computing time from 9.5 to 1.6 h, on average, compared to AI–REML with genomics. Method R has the potential to estimate heritabilities with large genomic information at a low cost when many generations of animals are present; however, the standard error can be high when only a few iterations are used. [ABSTRACT FROM AUTHOR]

We assessed the impact of environmental conditions and agronomic traits on maize grain quality parameters. The study was conducted using genotypes with distinct genetic constitutions developed specifically for late sowing in semi-arid environments. We evaluated the agronomic, physical, and chemical characteristics of eight maize open-pollinated varieties, six inbred lines, and three commercial hybrids. The yield of the open-pollinated varieties showed a positive correlation with protein content (r = 0.33), while it exhibited a negative correlation with the carbohydrate percentage (r = −0.36 and −0.42) in conjunction with the inbred lines. The flotation index of the hybrids was influenced primarily by the environmental effect (50.15%), whereas in the inbred lines it was nearly evenly divided between the genotype effect (45.51%) and the environmental effect (43.15%). In the open-pollinated varieties, the genotype effect accounted for 35.09% and the environmental effect for 42.35%. The characteristics of plant structure were associated with grain quality attributes relevant for milling, including hardness and test weight. Inbred lines exhibited significant genotype contributions to grain hardness, protein, and carbohydrate content, distinguishing them from the other two germplasm types. These associations are crucial for specific genotypes and for advancing research and development of cultivars for the food industry. [ABSTRACT FROM AUTHOR]

Abstract Background Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium. Results Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low ( 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively). Conclusions Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

Background: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium. Results: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively). Conclusions: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used. [ABSTRACT FROM AUTHOR]

Testing zero variance components is of utmost importance in various applications empowered by the use of mixed-effects models. Focusing on generalized linear models, this article proposes a permutation test using an analogue of the ANOVA test statistic that merely requires fitting the null model with independent observations. Monte Carlo simulations reveal that the new test has correct Type-I error rate and that its power compares favorably to an existing bootstrap score test. A real data application illustrates the advantageous capability of the proposed test in detecting the need for random effects. [ABSTRACT FROM AUTHOR]

This paper considers the problem of testing statistical hypotheses about the variance components under linear models with crossed random effects. The objective here is two-fold. First, new derivations of exactly distributed F test statistics are presented. Second, an alternative Monte Carlo permutation procedure is proposed to approximate the distribution of the F statistics, which shows its usefulness when the error components distributions depart from normality. Transformations that uniquely decompose the covariance structure of the response vector and do not sacrifice any part of the data, as existing methods do, are presented in matrix form. The suggested transformations highlight the exchangeable covariance structure of the model under the null hypotheses of interest and thus motivate the use of the permutation procedure. Comments on the performance of the proposed procedure compared to existing tests as well as using a real data example are provided. [ABSTRACT FROM AUTHOR]

ABSTRACT: Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of −0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

Generalizability theory (GT) has been used throughout the scientific literature to ensure validity, reliability, and generalizability in different sport contexts. However, there is a small number of studies examining the measurement of psychological profiles in sport from this perspective. Therefore, this study's main goal is the sources of variability and the optimal measurement design estimation for a good assessment of the psychological profile in track and field. The sample consisted of 470 participants (age: Average= 32.1; Standar Desviation = 13.5). The analysis of variance and generalizability component analysis has been performed in order to test the reliability and generalizability of the sample. The profile included the following variables: flow, motivation (from Self-Determination Theory and Achievement Goals), self-confidence, and psychological skills. Results confirm that the sample has a high degree of reliability and generalizability in all the tested models. So, a detailed study on the validity, reliability, and generalizability of samples and measures should be an inherent element in the practice of psychological counseling in sports. [ABSTRACT FROM AUTHOR]

Flowering time and plant height are two critical determinants of yield potential in barley (Hordeum vulgare). Despite their role in plant physiological regulation, a complete overview of the genetic complexity of flowering time and plant height regulation in barley is still lacking. Using a double round-robin population originated from the crossings of 23 diverse parental inbred lines, we aimed to determine the variance components in the regulation of flowering time and plant height in barley as well as to identify new genetic variants by single and multi-population QTL analyses and allele mining. Despite similar genotypic variance, we observed higher environmental variance components for plant height than flowering time. Furthermore, we detected new QTLs for flowering time and plant height. Finally, we identified a new functional allelic variant of the main regulatory gene Ppd-H1. Our results show that the genetic architecture of flowering time and plant height might be more complex than reported earlier and that a number of undetected, small effect, or low-frequency genetic variants underlie the control of these two traits. [ABSTRACT FROM AUTHOR]

One aim was to compare four classes of ewes for test-day milk yield (TDMY). The classes are the local Menz breed, the Awassi breed, and their main crossbreds (50 and 75%). Another aim was to estimate variance components to facilitate the prediction of breeding values and their accuracy. A total of 1040 TDMY records of 211 ewes were used. A univariate repeatability model with Legendre polynomial coefficients were used to model lactation curves. The 100% Awassi ewes produced significantly (p < 0.05) more milk than the other studied ewe groups within 120 Days in milk (DIM). The Local ewes produced significantly less than the other groups. Estimates of heritability (h2) and repeatability (r) of TDMY were 0.10 ± 0.08 and 0.15 ± 0.03, respectively. The genetic variance indicated for TDMY could give a genetic gain in the dissemination program if recorded for all ewes at the two centres. [ABSTRACT FROM AUTHOR]

Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of −0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance. [ABSTRACT FROM AUTHOR]

We aimed to estimate genetic parameters and select sorghum lines for forage purposes based on agronomic and chemical traits. A total of 100 inbred lines, comprising the biomass/bmr, biomass, dual purpose (sweet/forage), and forage types, were evaluated in the 2020/2021 and 2021/2022 crop years. We analyzed the data using a linear mixed model approach. Genetic variance, line mean-based heritability, selection accuracy, genetic correlation between traits and crops and the coefficient of experimental variation were estimated. The breeding values of the lines were predicted, and the factor analysis and genotype-ideotype distance index based on BLUP (FAI-BLUP) were applied for multi-trait selection. Biomass production and cell wall composition traits were measured. There was genetic variation among the lines, with moderate to high heritability. The lines selected by the FAI-BLUP index resulted in significant genetic gains expected with selection for all traits. [ABSTRACT FROM AUTHOR]

This paper proves that in mixed linear model, the analysis of variance estimation (ANOVAE), the minimum norm quadratic unbiased estimation (MINQUE), the spectral decomposition estimation (SDE) and the restricted maximum likelihood estimation (RMLE) of variance components are the same under some conditions. Based on this result, we construct a linear Bayes estimation (LBE) for the parameter vector consisting of variance components and establish its superiorities. Numerical computations and an illustration show that the LBE is comparable to Lindley's approximation, Tierney and Kadane's approximation and the usual Bayes estimation (UBE) obtained by the MCMC method and easy to use as well. [ABSTRACT FROM AUTHOR]

This paper proposes a novel likelihood-based boosting method for the selection of the random effects in linear mixed models. The nonconvexity of the objective function to minimize, which is the negative profile log-likelihood, requires the adoption of new solutions. In this respect, our optimization approach also employs the directions of negative curvature besides the usual Newton directions. A simulation study and a real-data application show the good performance of the proposal. [ABSTRACT FROM AUTHOR]

Health traits have high economic values in dairy cattle breeding, which can cause considerable financial loss through involuntary culling. In this study, fourteen health traits were analysed, including five composite health traits: reproductive disorders, udder health (UH), digestive disorders, metabolic disorders, locomotory diseases (LD), and nine independent health traits: gestation disorders and peripartum disorders, irregular estrus cycle and sterility, metritis (ME), mastitis (MA), abomasal displacement (AD), enteritis (EN), and ketosis, claw diseases (CD), laminitis complex. This study analysed variance components for health traits through both single and bivariate repeatability animal models. All health traits showed low heritability, ranging from 0.001 to 0.025. Most of the health traits in five categories showed negative genetic correlations, ranging from −0.012 (CD and EN) to −0.634 (ME and EN). Strong positive genetic correlations appeared within the same category, ranging from 0.469 (EN and AD) to 0.994 (UH and MA, LD and CD). Furthermore, approximate genetic correlations were evaluated between health traits and routinely collected traits (longevity, fertility, production, and conformation). In general, the low to moderate approximate genetic correlations were estimated between health traits and routinely collected traits. The estimated correlations between health traits and longevity, fertility, production, and conformation traits could provide an indirect reference for disease−resistance breeding in Holstein cattle.

IntroductionIn Iran, as in most poor and developing countries, wheat is the most important food in the diet of the people. Therefore, the field of study and research on various aspects of this important and strategic crop is still open to relevant researchers and experts. Given that the achievement of yield potential in crops, including wheat, is influenced by genetics, environmental conditions, their interaction and the application of proper management in the optimal use of available resources, so the genetic capacity of the existing germplasm should be used to produce suitable breeding lines in order to make the most of natural and climatic resources. Therefore, screening genotypes, selecting the proper parents, and modifying them through conventional and new methods can still be helpful. Estimating genetic effects and studying the inheritance pattern of important agronomic traits and in general knowledge of germplasm genetic information and knowledge of the genetic system of the studied trait is one of the most useful tools in designing breeding methods to improve target traits. This study aimed to estimate the genetic model controlling important agronomic traits and also to estimate heterosis and heritability in two bread wheat cross under normal and terminal drought stress conditions using generation mean analysis method and multivariate regression analysis.Materials and methodsA field investigation materials consisted of basic generations obtained from the crosses of a local cultivar “Marvdasht” (female parent) with two cultivars “Sistan” and “Norstar” (male parents). Both the crosses and resultant progenies were performed and developed under field conditions. All the six generations derived from the above two crosses were sown under two distinct conditions, normal (non-stress) and terminal drought stress in a randomized complete blocks design with three replicates at the Research Farm of Razi University, Kermanshah, Iran during the next cropping season (2015-2016). Terminal drought stress was imposed in Mid-May; while non-stress plots were irrigated whenever required. Weeds were controlled manually when necessary. The rainfall in 2015-2016 was 653 mm. Data from 10 plants in each of P1, P2 and F1 generations, 30 plants in F2 and 15 plants in each of BC1 and BC2 were randomly recorded per replication. Statistical analyses were done using different methods and software.Results and discussionSignificant differences between the generations were found for most of the traits which indicated the presence of genetic variation in the plant populations and scope of improvement through breeding methods such as selection and expression of heterosis. The scaling and joint scaling tests revealed that the simple additive-dominance model was not sufficient to explain the genetic variation in the crosses for all the studied traits apart from the number of spike per plant, main stem diameter and number of spikelet per spike at Marvdasht × Sistan, and hundred kernel weight and peduncle length at Marvdasht × Norstar. It could be concluded that the inheritance of these traits is governed by epistasis gene action. The results of analysis of variance obtained by regression method showed that fixable genetic effects had the highest relative contribution of the generation sum of squares for most of the traits in both crosses and under both normal and drought stress conditions. High estimates of broad-sense heritability for hundred kernel weight (0.67) in normal conditions at Marvdasht × Sistan cross and plant height (normal: 0.76 and stress: 0.67) and peduncle length (normal: 0.72 and stress: 0.67) at the Marvdasht × Norstar cross shows that the effects of dominance have a greater role in the inheritance pattern of these traits and therefore it is possible to produce hybrid varieties to improve these traits.ConclusionThe results of generation mean analysis showed that the type and action of genetic effects were variable in both crosses and for different traits and indicate the need to adopt a special breeding method to improve them. The presence of duplicate mode of gene interactions signified the involvement of epistatic effects for most of the traits. Based on the present investigation, it could be inferred that the genetic control model was somewhat similar for most of the traits under both conditions at both crosses, and it has not been largely affected by drought stress.

Hāpuku (Polyprion oxygeneios) is a promising candidate for aquaculture production in New Zealand. Methods for spawning, juvenile production, and growout to harvest entirely on land, where water quality, pathogens, environmental impacts, and genetic “pollution” can be tightly controlled, have been developed, and genetic improvement to optimise land-based production is the obvious next step. However, estimates of genetic parameters are required to design a rigorous, disciplined, and effective selective breeding program. By using existing data consisting of irregularly spaced repeated measurements of fork length and live body weight collected on wild-collected founders and two generations of captively reared progeny, we evaluated the species’ genetic potential for improvement in growth. We first tested a range of univariate random regression models to identify the best-fitting models for these data. Subsequently, using a bivariate model, we estimated variance components for growth trajectories of fork length and whole body weight. With one to six records available per fish, the best-fitting univariate models included only a fixed effect for contemporary groups and fixed and random genetic third-order Legendre polynomials. More complex models that included full-sib family and/or permanent environmental effects produced unacceptable constrained and/or non-positive-definite solutions. Both traits are moderately heritable at all stages of the growout phase (~0.4–0.5), and the genetic correlation patterns between daily breeding values estimated via the covariance function are different for length and weight. Genetic correlations for length between all pairs of age-specific breeding values are positive and strong (>0.7) and change gradually and smoothly with increasing temporal separation. For weight, these correlations deteriorate more rapidly with increasing time lags between measurements and become negative for some age pairings. We conclude that random regression analyses are a valuable tool for extracting genetic information from irregularly spaced repeated measurements of fish size, speculate that emerging technologies for high-throughput genotyping and phenotyping will add to the value of this approach in the near future, and reason that a breeding strategy that rigorously takes into account the potentially unfavourable genetic correlations between breeding values for weight at some ages will further adapt hāpuku to land-based systems and enhance the profitability commercial-scale production.

We assessed the impact of environmental conditions and agronomic traits on maize grain quality parameters. The study was conducted using genotypes with distinct genetic constitutions developed specifically for late sowing in semi-arid environments. We evaluated the agronomic, physical, and chemical characteristics of eight maize open-pollinated varieties, six inbred lines, and three commercial hybrids. The yield of the open-pollinated varieties showed a positive correlation with protein content (r = 0.33), while it exhibited a negative correlation with the carbohydrate percentage (r = −0.36 and −0.42) in conjunction with the inbred lines. The flotation index of the hybrids was influenced primarily by the environmental effect (50.15%), whereas in the inbred lines it was nearly evenly divided between the genotype effect (45.51%) and the environmental effect (43.15%). In the open-pollinated varieties, the genotype effect accounted for 35.09% and the environmental effect for 42.35%. The characteristics of plant structure were associated with grain quality attributes relevant for milling, including hardness and test weight. Inbred lines exhibited significant genotype contributions to grain hardness, protein, and carbohydrate content, distinguishing them from the other two germplasm types. These associations are crucial for specific genotypes and for advancing research and development of cultivars for the food industry.

Rabbits are an attractive meat livestock species that can efficiently convert human-indigestible plant biomass, and have been commonly used in biological and medical researches. Yet, transcriptomic landscape in muscle tissue and association between gene expression level and growth traits have not been specially studied in meat rabbits. In this study Oxford Nanopore Technologies (ONT) long-read sequencing technology was used for comprehensively exploring transcriptomic landscape in Longissimus dorsi for 115 rabbits at 84 days of age, and transcriptome-wide association studies (TWAS) were performed for growth traits, including body weight at 84 days of age and average daily gain during three growth periods. The statistical analysis of TWAS was performed using a mixed linear model, in which polygenic effect was fitted as a random effect according to gene expression level-based relationships. A total of 18,842 genes and 42,010 transcripts were detected, among which 35% of genes and 47% of transcripts were novel in comparison with the reference genome annotation. Furthermore, 45% of genes were widely expressed among more than 90% of individuals. The proportions (±SE) of phenotype variance explained by genome-wide gene expression level ranged from 0.501 ± 0.216 to 0.956 ± 0.209, and the similar results were obtained when explained by transcript expression level. In contrast, neither gene nor transcript was detected by TWAS to be statistically significantly associated with these growth traits. In conclusion, these novel genes and transcripts that have been extensively profiled in a single muscle tissue using long-read sequencing technology will greatly improve our understanding on transcriptional diversity in rabbits. Our results with a relatively small sample size further revealed the important contribution of global gene expression to phenotypic variation on growth performance, but it seemed that no single gene has an outstanding effect; this knowledge is helpful to include intermediate omics data for implementing genetic evaluation of growth traits in meat rabbits. [ABSTRACT FROM AUTHOR]

The estimation of heritability is a common practice in the field of ecology and evolution. Heritability of the traits is often estimated using one single measurement per individual, although many traits (especially behavioural and physiological traits) are characterized by large within-individual variance, and ideally a large number of within individual measurements can be obtained. Importantly, the effect of the within-individual variance and the rate at which this variance is sampled on the estimation of heritability has not been thoroughly tested. We fill this gap of knowledge with a simulation study, and assess the effect of within- and between-individual sample size, and the true value of the variance components on the estimation of heritability. In line with previous studies we found that the accuracy and precision of heritability estimation increased with sample size and accuracy with higher values of additive genetic variance. When the sample size was above 500 accuracy and power of heritability estimates increased in the models including repeated measurements, especially when within-individual variance was high. We thus suggest to use a sample of more than 100 individuals and to include more than two repeated measurements per individual in the models to improve estimation when investigating heritability of labile traits. Significance statement: Heritability reflects the part of the trait's phenotypic variation underlined by genetic variation. Despite the difficulties of heritability calculation (high number of individuals is needed with known relatedness), it is a widely used measure in evolutionary studies. However, not every factor potentially affecting the quality of heritability estimation is well understood. We thus investigated with a comprehensive simulation study how the number of repeated measurements per individuals and the amount of within-individual variation influence the goodness of heritability estimation. We found that although the previously described effect of the number of studied individuals was the most important, including repeated measurements also improved the reliability of the heritability estimates, especially when within-individual variation was high. Our results thus highlight the importance of including repeated measurements when investigating the heritability of highly plastic traits, such as behavioural or physiological traits. [ABSTRACT FROM AUTHOR]

Linear mixed models and data generated from repeated measurements have found substantial applications in many disciplines. This paper considers the estimation problem of fixed effects and variance components in the system of two seemingly unrelated linear mixed models. We first propose the covariance adjustment estimator of the fixed effects and then construct consistent estimators of its unknown parameters and further study the small sample performance of the two-stage estimator. It is shown that the proposed two-stage covariance adjustment estimator is preferred and feasible based on its theoretical properties and some numerical illustrations. Finally, we investigate the effects of covariance adjustment on the variance components by plugging the two-stage estimator into the restricted log-likelihood of the variance components, and the results imply that seemingly unrelated linear mixed models may only improve the estimation of the fixed effects. • Propose the covariance adjustment estimator of the fixed effects. • Construct consistent estimators of its unknown parameters. • Study the small sample performance of the two-stage estimator. • Investigate the effects of covariance adjustment on the variance components. [ABSTRACT FROM AUTHOR]

Copyright of Revista U.D.C.A Actualidad & Divulgación Científica is the property of Universidad de Ciencias Aplicadas y Ambientales U.D.C.A and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimation of variance components is essential in data analysis based on a linear mixed model. Among different estimation methods, the most commonly used in practice are likelihood-based approaches: maximum likelihood and restricted maximum likelihood. These methods do not guarantee positive-unbiased estimators, however. The method of moments ensures unbiasedness but may yield negative estimates, which can cause problems when estimating fixed effects by generalized least squares. The paper develops an algorithm to derive strictly positive unbiased estimators with minimum variance in a well-defined class. The key idea is to take the method-of-moments estimator, given by a quadratic form of a symmetric matrix A , as a starting point and modify it using the class of square non-singular regularization matrices Q while preserving unbiasedness in addition to ensuring positivity. Different subclasses of structured Q are possible for convenience instead of all possible Q matrices. A search algorithm then finds a local or global optimal matrix A depending on Q and the corresponding optimal variance component estimator by minimizing the variance, a function of the unknown variance components and kurtosis parameters in the model. Availability of an optimal matrix A means that the theoretical and numerical properties of the estimator can be conveniently studied, also in small samples. The proposed method further allows finding matrices A leading to quadratic forms closely approximating the corresponding numerical values of the likelihood-based estimates. The paper investigates the dependence of variance functions on the unknown model parameters. Using two illustrative examples, Examples I and II, the paper also illustrates the use of the matrix Q and the determination of the kurtosis parameters in the search for the optimal variance component estimators by keeping the bias zero and the variance trim for the bias–variance trade-offs. [ABSTRACT FROM AUTHOR]

Generalized additive mixed models (GAMMs) extend generalized linear mixed models (GLMMs) to allow the covariates to be nonparametrically associated with the response. Estimation of such models for correlated binary data is challenging and estimation techniques often yield contrasting results. Via simulations, we compared the performance of the Bayesian and likelihood-based methods for estimating the components of GAMMs under a wide range of conditions. For the Bayesian method, we also assessed the sensitivity of the results to the choice of prior distributions of the variance components. In addition, we investigated the effect of multicollinearity among covariates on the estimation of the model components. We then applied the methods to the Bangladesh Demographic Health Survey data to identify the factors associated with the malnutrition of children in Bangladesh. While no method uniformly performed best in estimating all components of the model, the Bayesian method using half-Cauchy priors for variance components generally performed better, especially for small cluster size. The overall curve fitting performance was sensitive to the prior selection for the Bayesian methods and also to the extent of multicollinearity. [ABSTRACT FROM AUTHOR]

The structure of the fixed and random parts of the genetic evaluation model plays a significant role in fitting data and the estimation of genetic parameters for economic traits in livestock. The present study was conducted to investigate the effect of different fixed and random effects combinations in an animal model framework on the general properties of the model and estimates of the genetic parameters for milk production traits. Traits studied were 305-day milk production (305-MY, 15920 records), fat percentage (FP, 27954 records), protein percentage (PP, 26183 records), average daily milk production (ADM, 30954 records) and milk somatic cell score (SCS, 25408 records) in Isfahan Holstein cows. In general, 54 scenarios were studied which differed in fixed and random parts of the model. Variance components were estimated using the animal model fitting restricted maximum likelihood (REML) procedure. The best model for each trait was selected based on the Bayesian Information Criterion (BIC). Results showed that for all traits studied, models in which the effect of contemporary groups Herd-Year-Season (HYS) or Herd-Year-Month (HYM) were fitted as the random or fixed effect together with age at the first calving and inbreeding as a classified fixed effect or covariate lead to a significantly better data fit instead of fitting herd, year, season and month of calving separately. For each trait, a wide range of heritability was obtained by fitting 54 models. Based on the best models, the estimates of heritability for 305-MY, ADM, FP, PP and SCS were 0.33, 0.28, 0.21, 0.16 and 0.61, respectively. It was concluded that a single model should not be used for analyzing all milk production traits and that for each trait a series of models which differ in random and fixed parts should be tested to find the most suitable model which describes the data best. Fitting the effects of herd and year, season and month of calving as contemporary groups HYS or HYM was recommended for genetic evaluation of milk production traits as resulted in better data fit. Depending on the trait, inbreeding and age at first calving can be fitted as a classified fixed effect or as a covariate. [ABSTRACT FROM AUTHOR]

Variance in reproductive success (sk2, with k = number of offspring) plays a large role in determining the rate of genetic drift and the scope within which selection acts. Various frameworks have been proposed to parse factors that contribute to sk2, but none has focused on age‐specific values of ϕ=sk2/k¯, which indicate the degree to which reproductive skew is overdispersed (compared to the random Poisson expectation) among individuals of the same age and sex. Instead, within‐age effects are generally lumped with residual variance and treated as "noise." Here, an ANOVA sums‐of‐squares framework is used to partition variance in annual and lifetime reproductive success into between‐group and within‐group components. For annual reproduction, the between‐age effect depends on age‐specific fecundity (bx), but relatively few empirical data are available on the within‐age effect, which depends on ϕx. By defining groups by age‐at‐death rather than age, the same ANOVA framework can be used to partition variance in lifetime reproductive success (LRS) into between‐group and within‐group components. Analytical methods are used to develop null‐model expectations for random contributions to within‐group and between‐group components. For analysis of LRS, random variation in longevity appears as part of the between‐group variance, and effects (if any) of skip breeding and persistent individual differences contribute to the within‐group variance. Simulations are used to show that the methods for variance partitioning are asymptotically unbiased. Practical application is illustrated with empirical data for annual reproduction in American black bears and lifetime reproduction in Dutch great tits. Results show that overdispersed within‐age variance (1) dominates annual sk2 in both male and female black bears, (2) is the primary factor that reduces annual effective size to a fraction of the number of adults, and (3) represents most of the opportunity for selection. In contrast, about a quarter of the variance in LRS in great tits can be attributed to random variation in longevity, and most of the rest is due to modest differences in fecundity with age estimated for a single cohort of females. R code is provided that reads generic input files for annual and lifetime reproductive success and allows users to conduct variance partitioning with their own data. [ABSTRACT FROM AUTHOR]

Simple Summary: Quantitative genetics theory postulates that genetic variance in closed populations under directional selection can considerably decrease. As genetic variance directly influences selection response, animal breeders are actively monitoring its changes over time to ensure the sustainability of their breeding programmes. Here, we evaluated three different approaches to computing variance components over a period of twenty-three years in a commercial broiler (meat-type chicken) population undergoing multi-trait selection. Our results showed that the trajectory of variance components fluctuated, but no overall decline trend was detected. In light of these findings, we discuss the implications for the long-term sustainability of broiler breeding programmes. Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a "double trait covariances" analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999–2001 to 2020–2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance. [ABSTRACT FROM AUTHOR]

The primary objective of soybean-breeding programs is to develop cultivars that offer both high grain yield and a maturity cycle tailored to the specific soil and climatic conditions of their cultivation. Therefore, predicting the genetic value is essential for selecting and advancing promising genotypes. Among the various analytical approaches available, deep machine learning emerges as a promising choice due to its capability to predict the genetic component of phenotypes assessed under field conditions, thereby enhancing the precision of breeding decisions. This study aimed to determine the efficiency of artificial neural networks (ANNs) in predicting the genetic values of soybean genotypes belonging to populations derived from crosses between parents of different relative maturity groups (RMGs). We characterized populations with broad and restricted genetic bases for RMG traits. Data from three soybean populations, evaluated over three different agricultural years, were used. Genetic values were predicted using the multilayer perceptron (MLP) artificial neural network and compared to those obtained using the best unbiased linear prediction from variance components using restricted maximum likelihood (RR-BLUP). The MLP neural network efficiently predicted genetic values for the relative maturity group trait for genotypes belonging to populations of broad and restricted crosses, with an R2 of 0.999 and root-mean-square error (RMSE) of 0.241, and for grain yield, there was an R2 of 0.999 and an RMSE of 0.076. While the percentage of coincident superior genotypes remained relatively consistent, a significant difference was observed in their ranking order. The genetic gain with selection estimated using MLP was higher by 30–110% compared to RR-BLUP for the relative maturity group trait and 90–500% for grain yield. Artificial neural networks (ANNs) showed higher efficiency than RR-BLUP in predicting the genetic values of the soybean population. Local selection at intermediate latitudes is conducive to developing lines adaptable for regions at higher and lower latitudes. [ABSTRACT FROM AUTHOR]

Background The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered. Development We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(r Δ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors. Application For female breast cancer, VALID quantified how much variance in risk is explained—at different ages—by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors. Conclusion VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk. [ABSTRACT FROM AUTHOR]

Methodologists have often acknowledged that, in multilevel contexts, level-1 variables may have distinct within-cluster and between-cluster effects. However, a prevailing notion in the literature is that separately estimating these effects is primarily important when there is specific interest in doing so. Consequently, in practice, researchers uninterested in disaggregating these effects (or unaware of their difference) routinely fit models that conflate them. Furthermore, even researchers who properly disaggregate the fixed components in a model (avoid fixed conflation) may still inadvertently and unknowingly conflate the random effects (fail to avoid random conflation). The purpose of this article is to elucidate an unappreciated consequence of such fixed or random conflation, namely, that it can cause systematic distortion in all variance components, yielding uninterpretable variances that adversely affect the entire model. In this article, I provide novel mathematical derivations, simulations, and pedagogical illustrations of such variance distortion, showing how it leads to several aberrant consequences: (1) error variances at level-1 and level-2 can systematically increase (in the population) with the addition of predictors; (2) there can be a large apparent degree of between-cluster random-effect variability in cases in which there is actually no between-cluster outcome variability; (3) Rsquared measures of explained variance can be severely biased, uninterpretable, and well below the logical bound of 0; and (4) inference for all fixed components of the model--not just the conflated slopes themselves--can be compromised. I conclude with recommendations for practice, including cautionary notes on interpreting results from prior research that had specified conflated slopes. [ABSTRACT FROM AUTHOR]

We consider three problems in high-dimensional linear mixed models. Without any assumptions on the design for the fixed effects, we construct asymptotic statistics for testing whether a collection of random effects is zero, derive an asymptotic confidence interval for a single random effect at the parametric rate n , and propose an empirical Bayes estimator for a part of the mean vector in ANOVA type models that performs asymptotically as well as the oracle Bayes estimator. We support our theoretical results with numerical simulations and provide comparisons with oracle estimators. The procedures developed are applied to the Trends in International Mathematics and Sciences Study (TIMSS) data. for this article are available online. [ABSTRACT FROM AUTHOR]

The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method to estimate the variation in a complex trait that can be attributed to additive (additive heritability) and dominance deviation (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of additive and dominance heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency [MAF] > 1%) and 50 quantitative traits measured in 291,273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no statistically significant evidence for dominance heritability (p

Determining the proportion of a population at risk of inadequate or excessive nutrient intake is a crucial step in planning and managing nutrition intervention programs. Multiple days of 24-h dietary intake data per subject allow for adjustment of modeled usual nutrient intake distributions for the proportion of total variance in intake attributable to within-individual variation (WIV:total). When only single-day dietary data are available, an external adjustment factor can be used; however, WIV:total may vary by population, and use of incorrect WIV:total ratios may influence the accuracy of prevalence estimates and subsequent program impacts. WIV:total values were compiled from publications and from reanalyses of existing datasets to describe variation in WIV:total across populations and settings. The potential impact of variation in external WIV:total on estimates of prevalence of inadequacy was assessed through simulation analyses using the National Cancer Institute 1-d method. WIV:total values were extracted from 40 publications from 24 countries, and additional values were calculated from 15 datasets from 12 nations. Wide variation in WIV:total (from 0.02 to 1.00) was observed in publications and reanalyses. Few patterns by population characteristics were apparent, but WIV:total varied by age in children (< vs. >1 y) and between rural and urban settings. Simulation analyses indicated that estimates of the prevalence of inadequate intake are sensitive to the selected ratio in some cases. Selection of an external WIV:total estimate should consider comparability between the reference and primary studies with regard to population characteristics, study design, and statistical methods. Given wide variation in observed ratios with few discernible patterns, the collection of ≥2 days of intake data in at least a representative subsample in population dietary studies is strongly encouraged. In the case of single-day dietary studies, sensitivity analyses are recommended to determine the robustness of prevalence estimates to changes in the variance ratio.

Generalizability theory (GT) has been used throughout the scientific literature to ensure validity, reliability, and generalizability in different sport contexts. However, there is a small number of studies examining the measurement of psychological profiles in sport from this perspective. Therefore, this study’s main goal is the sources of variability and the optimal measurement design estimation for a good assessment of the psychological profile in track and field. The sample consisted of 470 participants (age: Average= 32.1; Standar Desviation = 13.5). The analysis of variance and generalizability component analysis has been performed in order to test the reliability and generalizability of the sample. The profile included the following variables: flow, motivation (from Self-Determination Theory and Achievement Goals), self-confidence, and psychological skills. Results confirm that the sample has a high degree of reliability and generalizability in all the tested models. So, a detailed study on the validity, reliability, and generalizability of samples and measures should be an inherent element in the practice of psychological counseling in sports.

Abstract Variance in reproductive success (sk2, with k = number of offspring) plays a large role in determining the rate of genetic drift and the scope within which selection acts. Various frameworks have been proposed to parse factors that contribute to sk2, but none has focused on age‐specific values of ϕ=sk2/k¯, which indicate the degree to which reproductive skew is overdispersed (compared to the random Poisson expectation) among individuals of the same age and sex. Instead, within‐age effects are generally lumped with residual variance and treated as “noise.” Here, an ANOVA sums‐of‐squares framework is used to partition variance in annual and lifetime reproductive success into between‐group and within‐group components. For annual reproduction, the between‐age effect depends on age‐specific fecundity (bx), but relatively few empirical data are available on the within‐age effect, which depends on ϕx. By defining groups by age‐at‐death rather than age, the same ANOVA framework can be used to partition variance in lifetime reproductive success (LRS) into between‐group and within‐group components. Analytical methods are used to develop null‐model expectations for random contributions to within‐group and between‐group components. For analysis of LRS, random variation in longevity appears as part of the between‐group variance, and effects (if any) of skip breeding and persistent individual differences contribute to the within‐group variance. Simulations are used to show that the methods for variance partitioning are asymptotically unbiased. Practical application is illustrated with empirical data for annual reproduction in American black bears and lifetime reproduction in Dutch great tits. Results show that overdispersed within‐age variance (1) dominates annual sk2 in both male and female black bears, (2) is the primary factor that reduces annual effective size to a fraction of the number of adults, and (3) represents most of the opportunity for selection. In contrast, about a quarter of the variance in LRS in great tits can be attributed to random variation in longevity, and most of the rest is due to modest differences in fecundity with age estimated for a single cohort of females. R code is provided that reads generic input files for annual and lifetime reproductive success and allows users to conduct variance partitioning with their own data.

The efficiency with which a dairy cow utilises feed for the various physiological and metabolic processes can be evaluated by metrics that contrast realised feed intake with expected feed intake. In this study, we presented a new metric – regression on expected feed intake (ReFI). This metric is based on the idea of regressing DM intake (DMI) on expected DMI using a random regression model, where energy requirement formulations are applied for the calculation of expected DMI covariables. We compared this new metric with the metrics residual feed intake (RFI) and genetic residual feed intake (gRFI), by applying them on 18 581 feed efficiency records from 654 primiparous Nordic Red dairy cows. We estimated variance components for the three metrics and their respective genetic correlations with intake and production traits. In addition, we examined the phenotypes of superior cows. With ReFI, we estimated for feed efficiency a higher genetic variation (4.7%) and heritability (0.23) compared to applying RFI or gRFI. The ReFI metric was genetically uncorrelated with DMI and negatively correlated within energy-corrected milk (ECM), whereas the RFI metric was genetically positively correlated with DMI and metabolic BW. The gRFI metric was genetically positively correlated with DMI and uncorrelated with energy sink traits. Overall, the estimated SE were large. The ReFI metric resulted in a different ranking of cows compared to those based on RFI or gRFI and was superior in selecting the most efficient animals. When the selection was based on ReFI breeding values, then the 10% most efficient cows produced 12.3% more ECM per unit metabolisable energy intake, whereas the corresponding values were only 4.3 or 5.9% when using RFI or gRFI breeding values, respectively. Based on ReFI, superior cows had also higher milk production, whereas based on RFI or gRFI milk production either decreased or was unaffected, respectively. The superiority of the ReFI metric in selecting efficient cows was due to a better modelling of the expected feed intake. The ReFI metric simplified modelling of feed utilisation efficiency in dairy cattle and resulted in breeding values that are equal to percentages of feed saved.

The increasing incidence of bovine congestive heart failure (BCHF) in feedlot cattle poses a significant challenge to the beef industry from economic loss, reduced performance, and reduced animal welfare attributed to cardiac insufficiency. Changes to cardiac morphology as well as abnormal pulmonary arterial pressure (PAP) in cattle of mostly Angus ancestry have been recently characterized. However, congestive heart failure affecting cattle late in the feeding period has been an increasing problem and tools are needed for the industry to address the rate of mortality in the feedlot for multiple breeds. At harvest, a population of 32,763 commercial fed cattle were phenotyped for cardiac morphology with associated production data collected from feedlot processing to harvest at a single feedlot and packing plant in the Pacific Northwest. A sub-population of 5,001 individuals were selected for low-pass genotyping to estimate variance components and genetic correlations between heart score and the production traits observed during the feeding period. At harvest, the incidence of a heart score of 4 or 5 in this population was approximately 4.14%, indicating a significant proportion of feeder cattle are at risk of cardiac mortality before harvest. Heart scores were also significantly and positively correlated with the percentage Angus ancestry observed by genomic breed percentage analysis. The heritability of heart score measured as a binary (scores 1 and 2 = 0, scores 4 and 5 = 1) trait was 0.356 in this population, which indicates development of a selection tool to reduce the risk of congestive heart failure as an EPD (expected progeny difference) is feasible. Genetic correlations of heart score with growth traits and feed intake were moderate and positive (0.289-0.460). Genetic correlations between heart score and backfat and marbling score were -0.120 and -0.108, respectively. Significant genetic correlation to traits of high economic importance in existing selection indexes explain the increased rate of congestive heart failure observed over time. These results indicate potential to implement heart score observed at harvest as a phenotype under selection in genetic evaluation in order to reduce feedlot mortality due to cardiac insufficiency and improve overall cardiopulmonary health in feeder cattle. [ABSTRACT FROM AUTHOR]

Changes in fish length-at-age can be modeled by adding the age, year-class, and year effects together, or tracking cohort-specific growth with time-dependent parameters. We implemented both approaches to better understand lake trout (Salvelinus namaycush) length-at-age variations in US waters of Lake Huron, 1977–2019. We directly compared length-at-age over time using our linear-mixed model, where the expected length-at-age on logscale was represented by the sum of a fixed age effect, a fixed year-class effect (same for all ages of a cohort), and a random year effect (same for all ages within a year). The age, year-class, and year effects together indicated a general decline in length-at-age between the two time-periods of 1977–1997 and 1998–2019. The random year effect had local temporal trends that differed between the two time-periods, while the pattern of decreases in the fixed year-class effect appeared to reflect changes in growth conditions that were represented eventually by the largest prey-biomass decline during 1995–1996 and the final alewife collapse in 2003. We also predicted annual averages of length-at-age by tracking cohort-specific growth histories. We modified the Ford-Walford plot to illustrate how the growth processes are both cohort-specific and time-dependent. The estimates of year-specific asymptotic length indicated the decline in lake trout growth potential between the two time-periods specified above. Our two models differed fundamentally in their statistical assumptions, but they both fitted the data well and explained the growth decline between the two time-periods, which was also consistent with our understanding of the trophic shift in the ecosystem. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the genetics of imported semen and assess the genetic trend of production traits in Holstein cows during their first lactation in Iran. The data was collected from 28 different herds in Isfahan province between 2011 and 2020. Variance-covariance components were estimated by the restricted maximum likelihood method and the single and multi-trait animal model. The correlation between breeding values for bulls reported in catalogs and estimated in this was calculated. The mean of the least squares by countries shows that the daughters of Spanish bulls have the highest average for milk production, and the daughters of German, French, Spanish, and American bulls have the highest average percentage of fat and protein and the amount of fat and protein, respectively. Estimated heritability for milk production, fat, and protein percentage, and the amount of fat and protein were 0.34±0.011, 0.48±0.021, 0.41±0.016, 0.40±0.090, and 0.39±0.010 respectively. The mean genetic trend of milk production, fat percentage, protein percentage, fat content, and protein content were 92, 0.010, 0.004, 1.73, and 2.52, respectively. The correlation between the estimated and reported breeding value of bulls for milk production trait, percentage of fat and protein, and the amount of fat and protein was estimated at 0.48, 0.67, 0.69, 0.14, and 0.26, and all of the estimated correlations are statistically significant at the level of 0.05. Based on the results for the most critical production trait in Isfahan herds, milk production, American bulls have the best performance and genetic trend.

Twenty-six selected genotypes of Phalaris aquatica L. from Iranian germplasm were evaluated to evaluate the genetic diversity of some agromorphological characteristics. Each selected genotype was cloned and planted as spaced single plants according to a randomized complete block design (RCBD) with four replications in 2016 and 2017. Large and significant (p < 0.01) differences among genotypes were found for all traits. The estimates of broad sense heritability were low to high (hb 2 = 0.16 - 0.75) for studied traits. High heritability estimates for dry matter yield (DMY) indicate a sufficient genetic variability for further improvement. There was the highest heritability rate and genetic advance for plant height and DMY. Selection for traits with high heritability and genetic advance can be successful. The dry matter yield cut 1 (DMY1) correlation with the number of days to flowering, number of days to pollination, plant height, and crown diameter cut 2 was positive and significant. In stepwise multiple linear regression, the plant height, number of stems per plant, and spike length explained more than 84% of the total variation of DMY1. The plant height was the most crucial component of DMY1. The path analysis showed that plant height positively affected DMY1 (0.83). The highest negative indirect effects on DMY1 were observed for plant height via the number of stems per plant (-0.238). Axes 1 and 2 of the principal coordinate analysis explained 40.94% and 18.48% of the variation, respectively, and, although not entirely separated, general groupings can be determined. In factor analysis, component 1 was positively associated with the traits dry matter yield cut 2 (DMY2) (0.855), dry matter yield (0.821), and plant height (0.755). Therefore, the first component can be called the biomass component. Finally, selection would be more effective in improving forage yield based on yield components. In conclusion, the results indicated appropriate genetic variability in the studied genotypes. However, the studied genotypes will deliver a valuable germplasm to employ in breeding programs for forage cultivar production. This progress is crucial in introducing these selected genotypes to develop a core collection of Phalaris aquatica germplasm in Iran. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the genetics of imported semen and assess the genetic trend of production traits in Holstein cows during their first lactation in Iran. The data was collected from 28 different herds in Isfahan province between 2011 and 2020. Variance-covariance components were estimated by the restricted maximum likelihood method and the single and multi-trait animal model. The correlation between breeding values for bulls reported in catalogs and estimated in this was calculated. The mean of the least squares by countries shows that the daughters of Spanish bulls have the highest average for milk production, and the daughters of German, French, Spanish, and American bulls have the highest average percentage of fat and protein and the amount of fat and protein, respectively. Estimated heritability for milk production, fat, and protein percentage, and the amount of fat and protein were 0.34±0.011, 0.48±0.021, 0.41±0.016, 0.40±0.090, and 0.39±0.010 respectively. The mean genetic trend of milk production, fat percentage, protein percentage, fat content, and protein content were 92, 0.010, 0.004, 1.73, and 2.52, respectively. The correlation between the estimated and reported breeding value of bulls for milk production trait, percentage of fat and protein, and the amount of fat and protein was estimated at 0.48, 0.67, 0.69, 0.14, and 0.26, and all of the estimated correlations are statistically significant at the level of 0.05. Based on the results for the most critical production trait in Isfahan herds, milk production, American bulls have the best performance and genetic trend. [ABSTRACT FROM AUTHOR]