Estimates Of Additive Genetic Variance Research Articles

Evaluation of non-additive genetic effects on carcass and meat quality traits in Korean Hanwoo cattle using genomic models

The traditional genetic evaluation methods generally consider additive genetic effects only and often ignore non-additive (dominance and epistasis) effects that may have contributed to genetic variation of complex traits of livestock species. The available dense single nucleotide polymorphisms (SNPs) panels offer to investigate the potential benefits of including non-additive genetic effects in the genomic evaluation models. Data from 16 971 genotyped (Illumina Bovine 50 K SNP chip) Korean Hanwoo cattle were used to estimate genetic variance components and prediction accuracy of genomic breeding values (GEBVs) for four carcass and meat quality traits: carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS). Five different genetic models were evaluated through including additive, dominance and epistatic interactions (additive by additive, A × A; additive by dominance, A × D and dominance by dominance, D × D) successively in the models. The estimates of additive genetic variances and narrow sense heritabilities (ha2) were found similar across the evaluated models and traits except when additive interaction (A × A) was included. The dominance variance estimates relative to phenotypic variance ranged from 1.7–3.4% for CWT and MS traits, whereas, they were close to zero for EMA and BFT traits. The magnitude of A × A epistatic heritability (haa2) ranged between 14.8 and 27.7% in all traits. However, heritability estimates for A × D and D × D epistatic interactions (had2 and hdd2) were quite low compared to haa2 and were contributed only 0.0–9.7% of the total phenotypic variation. In general, broad sense heritability (hG2) estimates were almost twice (ranging between 0.54 and 0.68) the ha2 for all of the investigated traits. The inclusion of dominance effects did not improve the prediction accuracy of GEBV but improved 2.0–3.0% when epistatic effects were included in the model. More importantly, rank correlation revealed that partitioning of variance components considering dominance and epistatic effects in the model would enable to re-rank of top animals with better prediction of GEBV. The present result suggests that dominance and epistatic effects could be included in the genomic evaluation model for better estimates of variance components and more accurate prediction of GEBV for carcass and meat quality traits in Korean Hanwoo cattle.

The estimation of additive genetic variance of body size in a wild passerine is sensitive to the method used to estimate relatedness among the individuals.

Assessing additive genetic variance is a crucial step in predicting the evolutionary response of a target trait. However, the estimated genetic variance may be sensitive to the methodology used, e.g., the way relatedness is assessed among the individuals, especially in wild populations where social pedigrees can be inaccurate. To investigate this possibility, we investigated the additive genetic variance in tarsus length, a major proxy of skeletal body size in birds. The model species was the collared flycatcher (Ficedula albicollis), a socially monogamous but genetically polygamous migratory passerine. We used two relatedness matrices to estimate the genetic variance: (1) based solely on social links and (2) a genetic similarity matrix based on a large array of single-nucleotide polymorphisms (SNPs). Depending on the relatedness matrix considered, we found moderate to high additive genetic variance and heritability estimates for tarsus length. In particular, the heritability estimates were higher when obtained with the genetic similarity matrix instead of the social pedigree. Our results confirm the potential for this crucial trait to respond to selection and highlight methodological concerns when calculating additive genetic variance and heritability in phenotypic traits. We conclude that using a social pedigree instead of a genetic similarity matrix to estimate relatedness among individuals in a genetically polygamous wild population may significantly deflate the estimates of additive genetic variation.

Risk factors and genetic parameters of longevity in Spanish dairy goat breeds using a Weibull proportional hazards model

The aim of this study was to assess the most important risk factors that influence productive life (PL) and to estimate genetic parameters for PL using a Weibull proportional hazards model in two Spanish dairy goat breeds belonging to different production systems. The dataset consisted of 70,695 and 49,176 lactation records of Florida and Payoya females, respectively, collected over the last two decades. A total of 6,227 (24.21%) Florida and 3,452 (20.42%) Payoya females had censored information. The pedigree included a total of 56,901 animals for Florida and 59,957 for Payoya. The effects included in the model for both breeds were the age at first kidding and the herd-year-season of birth of the doe as time-independent effects; and the age at kidding, the herd-year-season of kidding, the within-herd class of milk production deviation and the combination between the lactation number and the duration of lactation as time-dependent effects. Estimation of genetic parameters was made using a Weibull proportional hazards model using Survival Kit V6.0 software. The results from the Weibull animal models gave estimates of additive genetic variances of 0.99 ± 0.0003 and 0.156 ± 0.021, which corresponds to a heritability of 0.43 ± 0.025 and 0.11 ± 0.021 for Florida and Payoya, respectively. The magnitude of heritability obtained for PL suggests that a substantial response to selection may be expected for longevity, especially in the Florida breed. These results may contribute to the development of a routine genetic evaluation to improve PL in Spanish dairy goat breeds.

Estimation of additive genetic variance when there are gene–environment correlations: Pitfalls, solutions and unexplored questions

Abstract Estimating the genetic variation underpinning a trait is crucial to understanding and predicting its evolution. A key statistical tool to estimate this variation is the animal model. Typically, the environment is modelled as an external variable independent of the organism, affecting the focal phenotypic trait via phenotypic plasticity. We studied what happens if the environment is not independent of the organism because it chooses or adjusts its environment, potentially creating non‐zero genotype–environment correlations. We simulated a set of biological scenarios assuming the presence or absence of a genetic basis for a focal phenotypic trait and/or the focal environment (treated as an extended phenotype), as well as phenotypic plasticity (the effect of the environment on the phenotypic trait) and/or ‘environmental plasticity’ (the effect of the phenotypic trait on the local environment). We then estimated the additive genetic variance of the phenotypic trait and/or the environment by applying five animal models which differed in which variables were fitted as the dependent variable and which covariates were included. We show that animal models can estimate the additive genetic variance of the local environment (i.e. the extended phenotype) and can detect environmental plasticity. We show that when the focal environment has a genetic basis, the additive genetic variance of a phenotypic trait increases if there is phenotypic plasticity. We also show that phenotypic plasticity can be mistakenly inferred to exist when it is actually absent and instead environmental plasticity is present. When the causal relationship between the phenotype and the environment is misunderstood, it can lead to severe misinterpretation of the genetic parameters, including finding ‘phantom’ genetic variation for traits that, in reality, have none. We also demonstrate how using bivariate models can partly alleviate these issues. Finally, we provide the mathematical equations describing the expected estimated values. This study highlights that not taking gene–environment correlations into account can lead to erroneous interpretations of additive genetic variation and phenotypic plasticity estimates. If we aim to understand and predict how organisms adapt to environmental change, we need a better understanding of the mechanisms that may lead to gene–environment correlations.

Estimation of Additive and Dominance Genetic Variances for Growth and Multiple‐stress at Different Ages in Litopenaeus vannamei

AbstractThis study aimed to estimate the additive and dominance genetic variance in growth and multiple stress‐tolerance traits in 7 week and 15 week Litopenaeus vannamei shrimp. Four different animal models were studied by including all or different subsets of the following effects: additive genetic effects (A), additive genetic and common environmental effects (A+C), additive genetic and dominance effects (A+D), and additive, common environmental, and dominance effects (A+D+C). Variance components were estimated using the Average Information Restricted Maximum Likelihood (AIREML) method. In general, estimates of additive genetic variance were inflated under the simple model (A) and decreased remarkably under the more complex models (A+C, A+D, and A+D+C). The genetic parameters of two‐stage (7 week and 15 week shrimp) growth and multiple stress‐tolerance traits were more suitable for estimation using the A+C model. The additive effects (0.311~0.754) of seven traits in 7 week shrimp were greater than the dominance effects (4.950× 10‐7~0.201), whereas the additive effects of the same seven traits in adult shrimp ranged from 7.712×10‐8 to 0.468 and the dominance effects ranged from 0.074 to 0.577. Except for survival time, carapace length, and carapace width, the additive effects of the other traits were smaller than the corresponding dominance effects. Thus, better genetic improvement of 7 week and 15 week shrimp growth and multiple‐stress tolerance can be obtained with selective breeding than with cross‐breeding, and better genetic improvement of 7 week shrimp growth traits can be obtained with cross‐breeding than with selective breeding. Our study further enriches the database for enhancing growth and stress resistance in L. vannamei and provides reference information for growth improvement and multiple stress‐resistance breeding.

Predicting milk protein fractions using infrared spectroscopy and a gradient boosting machine for breeding purposes in Holstein cattle

In recent years, increasing attention has been focused on the genetic evaluation of protein fractions in cow milk with the aim of improving milk quality and technological characteristics. In this context, advances in high-throughput phenotyping by Fourier transform infrared (FTIR) spectroscopy offer the opportunity for large-scale, efficient measurement of novel traits that can be exploited in breeding programs as indicator traits. We took milk samples from 2,558 Holstein cows belonging to 38 herds in northern Italy, operating under different production systems. Fourier transform infrared spectra were collected on the same day as milk sampling and stored for subsequent analysis. Two sets of data (i.e., phenotypes and FTIR spectra) collected in 2 different years (2013 and 2019-2020) were compiled. The following traits were assessed using HPLC: true protein, major casein fractions [αS1-casein (CN), αS2-CN, β-CN, κ-CN, and glycosylated-κ-CN], and major whey proteins (β-lactoglobulin and α-lactalbumin), all of which were measured both in grams per liter (g/L) and proportion of total nitrogen (% N). The FTIR predictions were calculated using the gradient boosting machine technique and tested by 3 different cross-validation (CRV) methods. We used the following CRV scenarios: (1) random 10-fold, which randomly split the whole into 10-folds of equal size (9-folds for training and 1-fold for validation); (2) herd/date-out CRV, which assigned 80% of herd/date as the training set with independence of 20% of herd/date assigned as the validation set; (3) forward/backward CRV, which split the data set in training and validation set according with the year of milk sampling (FTIR and gold standard data assessed in 2013 or 2019-2020) using the "old" and "new" databases for training and validation, and vice-versa with independence among them; (4) the CRV for genetic parameters (CRV-gen), where animals without pedigree as assigned as a fixed training population and animals with pedigree information was split in 5-folds, in which 1-fold was assigned to the fixed training population, and 4-folds were assigned to the validation set (independent from the training set). The results (i.e., measures and predictions) of CRV-gen were used to infer the genetic parameters for gold standard laboratory measurements (i.e., proteins assessed with HPLC) and FTIR-based predictions considering the CRV-gen scenario from a bi-trait animal model using single-step genomic BLUP. We found that the prediction accuracies of the gradient boosting machine equations differed according to the way in which the proteins were expressed, achieving higher accuracy when expressed in g/L than when expressed as % N in all CRV scenarios. Concerning the reproducibility of the equations over the different years, the results showed no relevant differences in predictive ability between using "old" data as the training set and "new" data as the validation set and vice-versa. Comparing the additive genetic variance estimates for milk protein fractions between the FTIR predicted and HPLC measures, we found reductions of -19.7% for milk protein fractions expressed in g/L, and -21.19% expressed as % N. Although we found reductions in the heritability estimates, they were small, with values ranging from -1.9 to -7.25% for g/L, and -1.6 to -7.9% for % N. The posterior distributions of the additive genetic correlations (ra) between the FTIR predictions and the laboratory measurements were generally high (>0.8), even when the milk protein fractions were expressed as % N. Our results show the potential of using FTIR predictions in breeding programs as indicator traits for the selection of animals to enhance milk protein fraction contents. We expect acceptable responses to selection due to the high genetic correlations between HPLC measurements and FTIR predictions.

Conceptualizing the evolutionary quantitative genetics of phenological life-history events: Breeding time as a plastic threshold trait

Successfully predicting adaptive phenotypic responses to environmental changes, and predicting resulting population outcomes, requires that additive genetic (co)variances underlying microevolutionary and plastic responses of key traits are adequately estimated on appropriate quantitative scales. Such estimation in turn requires that focal traits, and their underlying quantitative genetic architectures, are appropriately conceptualized. Here, we highlight that directly analyzing observed phenotypes as continuously distributed quantitative traits can potentially generate biased and misleading estimates of additive genetic variances and individual‐by‐environment and gene‐by‐environment interactions, and hence of forms of plasticity and genetic constraints, if in fact the underlying biology is best conceptualized as an environmentally sensitive threshold trait. We illustrate this scenario with particular reference to the key phenological trait of seasonal breeding date, which has become a focus for quantifying joint microevolutionary, plastic, and population responses to environmental change, but has also become a focus for highlighting that predicted adaptive outcomes are not always observed. Specifically, we use simple simulations to illustrate how potentially misleading inferences on magnitudes of additive genetic variance, and forms of environmental interactions, can arise by directly analyzing observed breeding dates if the transition to breeding in fact represents a threshold trait with latent‐scale plasticity. We summarize how existing and new datasets could be (re)analyzed, potentially providing new insights into how critical microevolutionary and plastic phenological responses to environmental variation and change can arise and be constrained.

English

This study aimed to estimate the genetic parameters and breeding values of milk yield traits of Holstein cows in Shandong Province using the best model identified by a comparison between a numbers of alternative random regression test day models (RRMs). The data included 585,702 test day records of milk yield in the first lactation of 88,215 Holstein cows, covering 219 cattle farms in Shandong Province during the period from 2005 to 2016. Different models were investigated, which differed in the number of knots of Spline functions to improve the fitting of population lactation curve and in orders (2, 3, or 4) of Legendre polynomials to fit additive genetic effect and permanent environmental effect. The optimal test day model was screened out by Akaike information criterion (AIC) and Bayesian information criterion (BIC) criteria. Detailed analysis of genetic parameters and accuracy of estimation of breeding values were performed using the optimal model. In the results, the optimal model (Sp15-La4-Lp3) for analyzing the milk yield data was the one with 15 knots of Splines, 4 orders of Legendre polynomials for additive genetic effect and 3 orders of Legendre polynomials for permanent environmental effect. Using the optimal model, estimates of additive genetic variances of milk yield at different days in milk (DIM) during the whole lactation ranged from 8.54 to 15.39, the permanent environmental variance ranged from 17.65 to 31.42. Correspondingly, the heritability ranged from 0.20 to 0.30, and repeatability ranged from 0.43 to 0.54. Rank correlations between EBV of bull with different number of daughters and the bull’s parent average ranged from 0.79 to 0.94, and the correlations between EBV of bulls and the sire-maternal grandsire index ranged from 0.48 to 0.86. In conclusion, Sp15-La4-Lp3 could be the optimal model for estimation of genetic parameters and prediction of breeding values of milk in Shandong Holstein population. The amount of progeny information is critical to the conventional genetic evaluation of bulls. © 2021 Friends Science Publishers

Correspondence between Breeding Values of the Same Pinus taeda L. Genotypes from Clonal Trials and Half-Sib Seedling Progeny Trials

AbstractThe correspondence between breeding values of 65 loblolly pine (Pinus taeda L.) genotypes from clonal genetic tests and half-sib seedling progeny tests was studied in the southern United States. The two experiments were established separately, 10 years apart. Additive genetic variance estimates from clonal tests were larger compared with the estimates from the half-sib progeny tests, regardless of the covariance structure used in the statistical models and the traits. However, clone-mean and half-sib family-mean heritability estimates were comparable for all traits, ranging between 0.88 and 0.99. Based on the independent analysis, the correlation between the breeding values of the same genotypes from two propagule types was moderate (0.59) for tree height and stem volume. The combined analysis resulted in a strong genetic correlation (>0.93) between the breeding values of two propagule types. Herein the large discrepancy is mainly the outcome of different data analytical approaches. Conclusively, selecting genotypes for deployment based on clonal testing may not be optimal, but forest tree breeders can use the results from clonal tests to make some informed decisions.

Partitioning variation in measurements of beef carcass traits using ultrasound.

Ultrasound technology provides cattle breeders with a quick, noninvasive, and inexpensive way to measure carcass data on live animals. Ultrasound data are used as indicator traits in cattle genetic evaluations for economically relevant carcass traits. Ultrasound cattle genetic evaluations assume homogeneous additive genetic and residual variance. Thus, the objective was to partition phenotypic variance in ultrasound carcass measurements into components for additive genetic effects, technicians, contemporary groups within technicians, and residual and to examine the homogeneity of these variances among image interpretation laboratories. Records of longissimus muscle area (LMA), percentage of intramuscular fat (IMF), and subcutaneous fat depth (SFD), measured using ultrasound, were provided by the American Angus Association (n = 65,967), American Hereford Association (n = 43,182), and American Simmental Association (n = 48,298). The data also included contemporary group, technician, imaging lab, and a three-generation pedigree for each animal. Variance components for ultrasound carcass measurements were first estimated with univariate animal models for each breed and imaging laboratory using derivative-free restricted maximum likelihood. Then, treating data from each imaging laboratory as separate traits, genetic correlations between laboratories for LMA, percentage of IMF, and subcutaneous fat were estimated with trivariate animal models. The technician explained 12–27%, 5–23%, and 4–26% of the variance for IMF, SFD, and LMA, respectively, across all three breeds. Variance due to technician was often greater than variance due to additive genetic effects but almost always less than that explained by the contemporary group. Within breeds, estimates of additive genetic variance for LMA, SFD, and IMF differed (range divided by mean) among laboratories by 4.5%, 21.5%, and 39.4 % (Angus); 31.6%, 15.0%, and 49.1% (Hereford); and 19.9%, 46.6%, and 55.3% (Simmental), respectively. Likewise, estimates of residual variance for LMA, SFD, and IMF differed among laboratories by 43.4%, 22.9%, and 43.3% (Angus); 24.9%, 15.2%, and 79.2% (Hereford); and 26.4%, 32.5%, and 46.2% (Simmental), respectively. Genetic correlations between labs across breeds ranged from 0.79 to 0.95 for IMF, 0.26 to 0.94 for SFD, and 0.78 to 0.98 for LMA. The impact of the observed heterogeneity of variance between labs on genetic evaluation requires further study.

Can dominance genetic variance be ignored in evolutionary quantitative genetic analyses of wild populations?

Accurately estimating genetic variance components is important for studying evolution in the wild. Empirical work on domesticated and wild outbred populations suggests that dominance genetic variance represents a substantial part of genetic variance, and theoretical work predicts that ignoring dominance can inflate estimates of additive genetic variance. Whether this issue is pervasive in natural systems is unknown, because we lack estimates of dominance variance in wild populations obtained in situ. Here, we estimate dominance and additive genetic variance, maternal variance, and other sources of nongenetic variance in eight traits measured in over 9000 wild nestlings linked through a genetically resolved pedigree. We find that dominance variance, when estimable, does not statistically differ from zero and represents a modest amount (2-36%) of genetic variance. Simulations show that (1) inferences of all variance components for an average trait are unbiased; (2) the power to detect dominance variance is low; (3) ignoring dominance can mildly inflate additive genetic variance and heritability estimates but such inflation becomes substantial when maternal effects are also ignored. These findings hence suggest that dominance is a small source of phenotypic variance in the wild and highlight the importance of proper model construction for accurately estimating evolutionary potential.

Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle.

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.

37 Genetic aspects of livestock adaptation

Abstract Livestock adaptation to less than favorable ambient has a genetic basis. Estimates of additive genetic variance and narrow sense heritability for % intramuscular fat in Hereford varied across United States geography quantifications evaluated in random regression analyses. Shedding and regrowth of winter coats of Angus cows in subtropical areas may indicate differential adaptation in cattle not adapted to the subtropics. Acquired adaptation to local conditions (multiple generations across more than 50 years) may was evidenced by performance of Florida Angus relative to prominent U.S. Angus in subtropical Florida; later sexual maturation may be a prominent component of such adaptation, and increasing milk production may be antagonistic to adaptation in natural conditions. Cattle temperament may be indicative of adaptation and is highly heritable; however, results from random regression analyses suggest that the additive genetic component appears to decrease in importance and the permanent environmental component of phenotype appears to become more important as calves age. Crossbreeding represents a proven strategy to improve adaptation almost immediately. Heterosis influences cattle body temperature maintenance, reproduction, survival, and, to a lesser extent, temperament in subtropical or other stressful environmental conditions (for example, in toxic fescue). Prenatal stress alters patterns of methylation (and likely other epigenetic mechanisms) and thereby encourages or inhibits gene expression to promote postnatal fitness. Brahman exposed to prenatal stress exhibited substantially different patterns of methylation across the genome in lymphocytes in both male and female calves; those patterns differed by sex. Female longevity may be the ultimate adaptation trait, as annual compliance to reproductive standards may be an appropriate assessment of a combination of attributes that represent adaptation. Longevity has documented heterotic influence; the additive genetic component is less well characterized but real. A simple, effective way to improve longevity may be to select bulls from aged, proven cows.

Bias in estimates of variance components in populations undergoing genomic selection: a simulation study

BackgroundAfter the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno1: phenotypes from only the conventional phase (1–20 years); (2) Pheno1 + 2: phenotypes from both the conventional phase and GS phase (1–35 years); (3) Pheno2: phenotypes from only the GS phase (21–35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base).ResultsIn general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno2), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno2) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated.ConclusionsThe results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.

Lactation and test day random regression models for genetic evaluation of Murrah buffaloes

In this study the variance components, genetic parameters and breeding values for 305 day or less milk yield were estimated using lactation model and random regression models for the first three lactations in Murrah buffaloes. Random regression model were taken both as random regression model with homogeneous residual variance (RRMHOM) and heterogeneous residual variance (RRM-HET). The estimates of additive genetic variance using RRM were higher than lactation model in all the three lactations. RRM-HET gave higher estimates of additive genetic variance than RRM-HOM in first lactation while RRM-HOM gave higher estimates in second and third lactation. From RRM, it was possible to account for permanent environmental variance arising due to individual milk yield variations during lactation. The heritability estimates were comparable in all the three models. However, in first lactation, the heritability estimates from lactation model RRM-HOM and RRM-HET were 0.319, 0.296 and 0.305, respectively. Likewise in second and third lactations these estimates were 0.004, 0.137, 0.135 and 0.520, 0.315, 0.264, respectively. The breeding value rank correlation was high in all the lactations. More sires were common for each model among the top 10 ranked sires in all three lactations. In conclusion, RRM can be an alternative to lactation model owing to high accuracy, early evaluation, high additive genetic variance, comparable heritability and high rank correlation for breeding values.

Estimates of Genetic Parameters for Body Weight in Turkish Holstein Bulls using Random Regression Model

The objective of this study was to estimate genetic parameters for the body weights of Turkish Holstein bulls using the random regression model. The data set consists of 1475 body weight records from 395 Holstein bulls raised in the same herd. Body weight records of bulls aged between 32 and 725 days old were collected at approximately two-month intervals from December 2013 to October 2014. In the study body weight measurements made on the same day were accepted as a group and the bulls were grouped into 10 different groups according to their age. The additive genetic and permanent environmental effects were estimated using DFREML algorithm by third order Legendre polynomials. The additive genetic variance estimates ranged from 10.73 to 4867.07, the phenotypic variance estimates ranged from 382.84 to 5514.86 and permanent environmental variance estimates ranged from 0.33 to 63.27. The heritability values were estimated between 0.03 to 0.90. The phenotypic and additive genetic correlations between body weights were positively estimated between 0.085 to 0.89 and 0.53 to 0.94, respectively. It was concluded that use of body weight at an earlier age will give advantage in breeding studies for body weight at slaughter.

Sex differences in the genetic architecture of aggressiveness in a sexually dimorphic spider.

Sex differences in the genetic architecture of behavioral traits can offer critical insight into the processes of sex‐specific selection and sexual conflict dynamics. Here, we assess genetic variances and cross‐sex genetic correlations of two personality traits, aggression and activity, in a sexually size‐dimorphic spider, Nuctenea umbratica. Using a quantitative genetic approach, we show that both traits are heritable. Males have higher heritability estimates for aggressiveness compared to females, whereas the coefficient of additive genetic variation and evolvability did not differ between the sexes. Furthermore, we found sex differences in the coefficient of residual variance in aggressiveness with females exhibiting higher estimates. In contrast, the quantitative genetic estimates for activity suggest no significant differentiation between males and females. We interpret these results with caution as the estimates of additive genetic variances may be inflated by nonadditive genetic effects. The mean cross‐sex genetic correlations for aggression and activity were 0.5 and 0.6, respectively. Nonetheless, credible intervals of both estimates were broad, implying high uncertainty for these estimates. Future work using larger sample sizes would be needed to draw firmer conclusions on how sexual selection shapes sex differences in the genetic architecture of behavioral traits.

Evidence of nutrient by genotype interaction in meat quail strains through reaction norm models

Quails raised under different nutrition levels could lead to genetic variation affecting the genetic gain due to the genotype by environment (GxE) interaction. Thus, reaction norm models were carried out to evaluate the GxE interaction of two meat quails’ strains (LF1 and LF2) raised under different tryptophan:lysine ratios for body weight and carcass traits. A total of 590 and 587 meat quails for LF1 and LF2, respectively, were raised under five different tryptophan:lysine ratios (0.15, 0.20, 0.25, 0.30 and 0.35) in final growth phase diet. The tryptophan:lysine ratios exhibited an important influence on the genetic parameters of body weight and carcass traits. Heritability and additive genetic variance estimates increased as tryptophan: lysine ratios increased, except for body weight at 28 and 35 days in LF2 strain. Lower correlation estimates and re-ranking of estimated breeding values were observed for slaughter weight, breast weight and thigh + leg yield in LF2 strain and for carcass yield in LF1 strain. It indicated a striking effect of amino acid ratios in quail carcass traits. The genetic evaluation and selection for carcass trait in quail strains raised under different tryptophan:lysine ratios should be considered in quail breeding programs.

Accuracy of genomic-polygenic estimated breeding value for milk yield and fat yield in the Thai multibreed dairy population with five single nucleotide polymorphism sets

ObjectiveThe objectives were to compare variance components, genetic parameters, prediction accuracies, and genomic-polygenic estimated breeding value (EBV) rankings for milk yield (MY) and fat yield (FY) in the Thai multibreed dairy population using five single nucleotide polymorphism (SNP) sets from GeneSeek GGP80K chip.MethodsThe dataset contained monthly MY and FY of 8,361 first-lactation cows from 810 farms. Variance components, genetic parameters, and EBV for five SNP sets from the GeneSeek GGP80K chip were obtained using a 2-trait single-step average-information restricted maximum likelihood procedure. The SNP sets were the complete SNP set (all available SNP; SNP100), top 75% set (SNP75), top 50% set (SNP50), top 25% set (SNP25), and top 5% set (SNP5). The 2-trait models included herd-year-season, heterozygosity and age at first calving as fixed effects, and animal additive genetic and residual as random effects.ResultsThe estimates of additive genetic variances for MY and FY from SNP subsets were mostly higher than those of the complete set. The SNP25 MY and FY heritability estimates (0.276 and 0.183) were higher than those from SNP75 (0.265 and 0.168), SNP50 (0.275 and 0.179), SNP5 (0.231 and 0.169), and SNP100 (0.251and 0.159). The SNP25 EBV accuracies for MY and FY (39.76% and 33.82%) were higher than for SNP75 (35.01% and 32.60%), SNP50 (39.64% and 33.38%), SNP5 (38.61% and 29.70%), and SNP100 (34.43% and 31.61%). All rank correlations between SNP100 and SNP subsets were above 0.98 for both traits, except for SNP100 and SNP5 (0.93 for MY; 0.92 for FY).ConclusionThe high SNP25 estimates of genetic variances, heritabilities, EBV accuracies, and rank correlations between SNP100 and SNP25 for MY and FY indicated that genotyping animals with SNP25 dedicated chip would be a suitable to maintain genotyping costs low while speeding up genetic progress for MY and FY in the Thai dairy population.

Dissecting total genetic variance into additive and dominance components of purebred and crossbred pig traits

The partition of the total genetic variance into its additive and non-additive components can differ from trait to trait, and between purebred and crossbred populations. A quantification of these genetic variance components will determine the extent to which it would be of interest to account for dominance in genomic evaluations or to establish mate allocation strategies along different populations and traits. This study aims at assessing the contribution of the additive and dominance genomic variances to the phenotype expression of several purebred Piétrain and crossbred (Piétrain × Large White) pig performances. A total of 636 purebred and 720 crossbred male piglets were phenotyped for 22 traits that can be classified into six groups of traits: growth rate and feed efficiency, carcass composition, meat quality, behaviour, boar taint and puberty. Additive and dominance variances estimated in univariate genotypic models, including additive and dominance genotypic effects, and a genomic inbreeding covariate allowed to retrieve the additive and dominance single nucleotide polymorphism variances for purebred and crossbred performances. These estimated variances were used, together with the allelic frequencies of the parental populations, to obtain additive and dominance variances in terms of genetic breeding values and dominance deviations. Estimates of the Piétrain and Large White allelic contributions to the crossbred variance were of about the same magnitude in all the traits. Estimates of additive genetic variances were similar regardless of the inclusion of dominance. Some traits showed relevant amount of dominance genetic variance with respect to phenotypic variance in both populations (i.e. growth rate 8%, feed conversion ratio 9% to 12%, backfat thickness 14% to 12%, purebreds-crossbreds). Other traits showed higher amount in crossbreds (i.e. ham cut 8% to 13%, loin 7% to 16%, pH semimembranosus 13% to 18%, pH longissimus dorsi 9% to 14%, androstenone 5% to 13% and estradiol 6% to 11%, purebreds-crossbreds). It was not encountered a clear common pattern of dominance expression between groups of analysed traits and between populations. These estimates give initial hints regarding which traits could benefit from accounting for dominance for example to improve genomic estimated breeding value accuracy in genetic evaluations or to boost the total genetic value of progeny by means of assortative mating.