Direct Genetic Variance Research Articles

Indirect genetic effects should make group size more evolvable than expected.

Group size is an important trait for many ecological and evolutionary processes. However, it is not a trait possessed by individuals but by social groups, and as many genomes contribute to group size understanding its genetic underpinnings and so predicting its evolution is a conceptual challenge. Here I suggest how group size can be modelled as a joint phenotype of multiple individuals, and so how models for evolution accounting for indirect genetic effects are essential for understanding the genetic variance of group size. This approach makes it clear that (a) group size should have a larger genetic variance than initially expected as indirect genetic effects always contribute exactly as much as direct genetic effects and (b) the response to selection of group size should be faster than expected based on direct genetic variance alone as the correlation between direct and indirect effects is always at the maximum positive limit of 1. Group size should therefore show relatively rapid evolved increases and decreases, the consequences of which and evidence for I discuss.

Trait-Specific Indirect Effects Underlie Variation in the Response of Spiders to Cannibalistic Social Partners.

AbstractIn cannibalistic species, selection to avoid conspecifics may stem from the need to avoid being eaten or to avoid competition. Individuals may thus use conspecific cues to modulate their behavior to such threats. Yet the nature of variation for such cues remains elusive. Here, we use a half-sib/full-sib design to evaluate the contribution of (indirect) genetic or environmental effects to the behavioral response of the cannibalistic wolf spider Lycosa fasciiventris (Dufour, 1835) toward conspecific cues. Spiders showed variation in relative occupancy time, activity, and velocity on patches with or without conspecific cues, but direct genetic variance was found only for occupancy time. These three traits were correlated and could be lumped in a principal component: spiders spending more time in patches with conspecific cues moved less and more slowly in those areas. Genetic and/or environmental components of carapace width and weight loss in the social partner, which may reflect the quality and/or quantity of cues produced, were significantly correlated with this principal component, with larger partners causing focal individuals to move more slowly. Therefore, environmental and genetic trait variation in social partners may maintain trait diversity in focal individuals, even in the absence of direct genetic variation.

Influences of maternal factors on the estimate of genetic parameters for goat feed efficiency traits.

The objective of the present study was to estimate the genetic parameters for the feed efficiency traits in Barbari goats. The data records of 9332 progenies born to 413 sires and 2580 dams were collected with respect to the average daily weight gain (ADG), i.e., ADG1 (birth to weaning), ADG2 (weaning to 6months), ADG3 (6 to 12months), as well as derived trait Kleiber ratio (KR), i.e., KR1 (ADG1/3MW0.75), KR2 (ADG2/6MW0.75), and KR3 (ADG3/12MW0.75). The data were corrected for fixed covariates like period of kidding, the season of birth, sex, type of birth, and parity. Univariate and multivariate animal models with an average information function of restricted maximum likelihood (REML) were used to estimate genetic factors for these traits. The best model was evaluated based on the likelihood ratio test. The direct heritability estimates were 0.21 ± 0.03, 0.17 ± 0.03, 0.23 ± 0.04, 0.22 ± 0.04, 0.16 ± 0.04, and 0.26 ± 0.04 for ADG1, ADG2, ADG3, KR1, KR2, and KR3, respectively. However, they were inflated due to high and negative estimates of covariance between direct animal and maternal genetic effects. Moderate estimates of heritability augur the scope for improvement for feed efficiency traits. The maternal genetic effects (m2) significantly contributed to 3-12% of the total phenotypic variance. The realized heritability of mass selection, which takes into account direct and maternal genetic variance together, shows a low to moderate estimate of genetic variance for ADG and KR. The genetic correlation ranged from - 0.48 ± 0.11 (ADG1-KR3) to 0.95 ± 0.00 (ADG1-KR1), phenotypic correlation ranged from - 0.28 ± 0.01 (ADG2-KR3) to 0.94 ± 0.01 (ADG1-KR1), maternal genetic correlation ranged from - 0.22 (KR2-KR3) to 0.96 (ADG1-KR1) and - 0.69 (ADG1-KR3) to 0.95 (ADG1-KR1) for the maternal permanent environment, respectively. Kids can be indirectly chosen for higher feed efficiency since ADG and their associated KR have substantial genetic correlations. It is suggested that the KR should be used as a selection criterion for Barbari goats for improving feed efficiency.

Inclusion of sire by herd interaction effect in the genomic evaluation for weaning weight of American Angus.

A spurious negative genetic correlation between direct and maternal effects of weaning weight (WW) in beef cattle has historically been problematic for researchers and industry. Previous research has suggested the covariance between sires and herds may be contributing to this relationship. The objective of this study was to estimate the variance components (VC) for WW in American Angus with and without sire by herd (S×H) interaction effect when genomic information is used or not. Five subsets of ~100k animals for each subset were used. When genomic information was included, genotypes were added for 15,637 animals. Five replicates were performed. Four different models were tested, namely, M1: without S×H interaction effect and with covariance between direct and maternal effect () ≠ 0; M2: with S×H interaction effect and ≠ 0; M3: without S×H interaction effect and with = 0; M4: with S×H interaction effect and = 0. VC were estimated using the restricted maximum likelihood (REML) and single-step genomic REML (ssGREML) with the average information algorithm. Breeding values were computed using single-step genomic BLUP for the models above and one additional model, which had the covariance zeroed after the estimation of VC (M5). The ability of each model to predict future breeding values was investigated with the linear regression method. Under REML, when the S×H interaction effect was added to the model, both direct and maternal genetic variances were greatly reduced, and the negative covariance became positive (i.e., when moving from M1 to M2). Similar patterns were observed under ssGREML, but with less reduction in the direct and maternal genetic variances and still a negative covariance. Models with the S×H interaction effect (M2 and M4) had a better fit according to the Akaike information criteria. Breeding values from those models were more accurate and had less bias than the other three models. The rankings and breeding values of artificial insemination sires (N = 1,977) greatly changed when the S×H interaction effect was fit in the model. Although the S×H interaction effect accounted for 3% to 5% of the total phenotypic variance and improved the model fit, this change in the evaluation model will cause severe reranking among animals.

Genetic parameters and genomic regions associated with calving ease in primiparous Nellore heifers

The aim of this study was to estimate genetic parameters and to identify genomic regions associated with the calving ease (CE) in precocious Nellore heifers. A total of 1,277 CE phenotypes were collected and scored into two categories: i- non assisted calving, categorized as success (1) and ii- assisted calving where heifers required any form of assistance or intervention to give birth, categorized as failure (2). A pedigree structure containing the identification of the animal's sire and dam was used, with the relationship matrix comprising a total of 6,511 animals. Genotypic data from 1,201 animals were obtained using low-density panel (Clarifide Nelore 3.1) encompassing over 29,001 single nucleotide polymorphisms (SNP) markers. A threshold sire-maternal grandsire model (S-MGS) was used to estimate the genetic parameters, which included sire, maternal grandsire and residual effects as random effects and the fixed effects of contemporary groups (farm and year of calving, sex) and birth weight of the calf as covariable (linear effect). Genomic breeding values were estimated using an animal model with the direct and maternal genetic variances which were previously obtained by means of a S-MGS threshold model. The direct and maternal heritability estimates for CE were obtained considering the covariance of direct and maternal effects fixed as zero. Regions that accounted for more than 0.5% of the additive genetic variance were used. The direct and maternal heritability estimates for CE were low (0.18) and moderate (0.39) respectively, indicating that genetic progress for this trait is feasible, and so, it would respond favorably to direct selection. Genes identified within the significant windows, such as CA8, FAM110B, TOX, ARID1A, RBM15, HSF1 and PLAG1 were found to be related with maternal and direct effects on CE. Gene enrichment analysis revealed processes that might directly influence fetal processes involved in female pregnancy and stress response. These results should help to better understand the genetic and physiological mechanisms regulating placenta development and fetal development, and this information might be useful for future genomic studies in Nellore cattle.

Genetic evaluation of tropical climate-adapted sheep for carcass traits including genomic information

Abstract Santa Ines is a locally adapted hair sheep breed that is under large expansion in Brazil and other Tropical countries. This study aimed to evaluate the effect of different relationship matrices (A or H) on the estimation of genetic parameters and accuracy of (genomic) breeding values [(G)EBVs] for carcass traits measured in vivo in Santa Ines sheep. A total of 977, 890, 894, and 882 records for loin eye area (LEA), marbling score (MLE), subcutaneous fat thickness (SFT), and leg circumference (LEC), respectively, were included in this study. There were 1637 animals in the numerator relationship matrix (A). A total of 389 animals with phenotypic information were genotyped using the OvineSNP50 BeadChip. After the genotyping quality control, 388 samples and 42,748 SNPs remained in the dataset. The (co)variance components were estimated via Bayesian inference through single- and multi-trait analyses using the animal model with either the A or H matrix. The theoretical accuracies of (G)EBVs, Pearson correlation between breeding values estimated including or not genomic information, and Spearman rank correlation were used to evaluate the feasibility of incorporating genomic information in the analyses. Heritability estimates ranged from 0.12 ± 0.06 (SFT, using the A matrix) to 0.29 ± 0.07 (LEA, using the H matrix), in the single-trait analyses, and from 0.17 ± 0.05 (SFT, using A) to 0.33 ± 0.07 (LEA, using H), in a multi-trait setting. Direct genetic additive variance and heritability estimates were higher when including genomic information. Unfavorable and high genetic correlation was observed between LEA and SFT. The estimates of theoretical accuracy of (G)EBVs were higher in all the scenarios when genomic information was incorporated in the model. Gains in accuracy ranged from 0.013 to 0.039 units when genomic information was included in the analyses. Thus, if available, genomic information should be incorporated in the estimation of genetic parameters and breeding values for carcass traits measured in vivo in Santa Ines sheep.

Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1.

Mixing of pigs into new social groups commonly induces aggressive interactions that result in skin lesions on the body of the animals. The relationship between skin lesions and aggressive behavioral interactions in group-housed pigs can be analyzed within the framework of social genetic effects (SGE). This study incorporates the quantification of aggressive interactions between pairs of animals in the modeling of SGE for skin lesions in different regions of the body in growing pigs. The dataset included 792 pigs housed in 59 pens. Skin lesions in the anterior, central, and caudal regions of the body were counted 24 h after pig mixing. Animals were video-recorded for 9 h postmixing and trained observers recorded the type and duration of aggressive interactions between pairs of animals. The number of seconds that pairs of pigs spent engaged in reciprocal fights and unilateral attack behaviors were used to parametrize the intensity of social interactions (ISI). Three types of models were fitted: direct genetic additive model (DGE), traditional social genetic effect model (TSGE) assuming uniform interactions between dyads, and an intensity-based social genetic effect model (ISGE) that used ISI to parameterize SGE. All models included fixed effects of sex, replicate, lesion scorer, weight at mixing, premixing lesion count, and the total time that the animal spent engaged in aggressive interactions (reciprocal fights and unilateral attack behaviors) as a covariate; a random effect of pen; and a random direct genetic effect. The ISGE models recovered more direct genetic variance than DGE and TSGE, and the estimated heritabilities (h^D2) were highest for all traits (P < 0.01) for the ISGE with ISI parametrized with unilateral attack behavior. The TSGE produced estimates that did not differ significantly from DGE (P > 0.5). Incorporating the ISI into ISGE, even in a small dataset, allowed separate estimation of the genetic parameters for direct and SGE, as well as the genetic correlation between direct and SGE (r^ds), which was positive for all lesion traits. The estimates from ISGE suggest that if behavioral observations are available, selection incorporating SGE may reduce the consequences of aggressive behaviors after mixing pigs.

Indirect genetic effects clarify how traits can evolve even when fitness does not

AbstractThere are many situations in nature where we expect traits to evolve but not necessarily for mean fitness to increase. However, these scenarios are hard to reconcile simultaneously with Fisher's fundamental theorem of natural selection (FTNS) and the Price identity (PI). The consideration of indirect genetic effects (IGEs) on fitness reconciles these fundamental theorems with the observation that traits sometimes evolve without any adaptation by explicitly considering the correlated evolution of the social environment, which is a form of transmission bias. Although environmental change is often assumed to be absent when using the PI, here we show that explicitly considering IGEs as change in the social environment with implications for fitness has several benefits: (1) it makes clear how traits can evolve while mean fitness remains stationary, (2) it reconciles the FTNS with the evolution of maladaptation, (3) it explicitly includes density-dependent fitness through negative social effects that depend on the number of interacting conspecifics, and (4) it allows mean fitness to evolve even when direct genetic variance in fitness is zero, if related individuals interact and/or if there is multilevel selection. In summary, considering fitness in the context of IGEs aligns important theorems of natural selection with many situations observed in nature and provides a useful lens through which we might better understand evolution and adaptation.

Genetic parameter estimates for growth traits in Katahdin sheep using different models

Se estimaron parámetros genéticos para características de crecimiento en corderos Katahdin, usando seis variantes del modelo animal. Se usó información de pesos al nacimiento (BW; n= 13,099), al destete ajustado a 75 d (WW; n= 11,509) y posdestete ajustado a 120 d (AW; n= 6,886) tomada durante 7 años (2004-2010) en 20 estados de la República Mexicana. Los análisis se hicieron ignorando o incluyendo efectos maternos. El modelo más sencillo incluyó el efecto genético aditivo directo como el único efecto aleatorio. El modelo más completo incluyó los efectos genéticos directo y materno, la covarianza entre ellos, y el efecto del ambiente permanente materno. Para seleccionar el mejor modelo se usó la prueba de razón de verosimilitud. Cuando los efectos maternos no fueron incluidos en el modelo, los estimadores de la heredabilidad directa y de la varianza genética directa resultaron sobreestimados. Las heredabilidades directas con el mejor modelo fueron 0.18 ± 0.03, 0.30 ± 0.04 y 0.20 ± 0.05 para BW, WW y AW, respectivamente. Las heredabilidades maternas también variaron dependiendo del modelo, de 0.05 a 0.23, 0.00 a 0.12, y 0.09 a 0.25 para BW, WW y AW. El ignorar los efectos maternos en el modelo resultaría en una evaluación genética equivocada para las características de crecimiento en borregos Katahdin.

Estimates of covariance functions for growth of Kordi sheep in Iran using random regression models

In the current paper, restricted maximum likelihood estimates of covariance functions for growth records from birth to 360 days of age in Kordi sheep population were estimated using random regression models (RRM). Kordi sheep is an important breed known for adaptability and resistance to unfavorable climatic conditions and suitable feed efficiency in mountainous regions of Iran. Data included 23,720 body weight records of 6730 Kordi lambs collected from 1989 to 2012 at Hossein Abad Kordi sheep Breeding Station in Shirvan city, North Khorasan province, Iran. Fixed effects in the model were lamb sex, birth type, and birth year as well as dam age. Legendre polynomials (LP) of order 3 was used for age to illustrate the average growth curve. In the random part, the functions with different orders were included to model variances associated to direct and maternal effects. Results showed that a model with LP of order 4,3,4,3 for direct additive genetic effects, animal permanent environmental effects, maternal additive genetic and maternal permanent environmental effects was sufficient to model changes in (co)variances with age, according to Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC). Direct additive genetic and animal permanent environment variances increased with age. Direct heritability (h2a) increased with age from 0.133 ± 0.021 at birth to 0.391 ± 0.034 at 360 days of age. The ratio of animal permanent environmental variances to phenotypic variance (pe2) was 0.047 ± 0.083 at birth, increased with age and reached 0.537 ± 0.034 at the end of the growth trajectory. Maternal heritability (h2m) was 0.152 ± 0.025 at birth decreased to 0.067 ± 0.016 until weaning and then fluctuated between 0.06 and 0.10. The ratio of maternal permanent environmental variances to phenotypic variance (mpe2) was 0.082 ± 0.02 at birth; however, it gradually decreased until the end of trajectory. The first eigenvalues of the coefficient matrices explained 69% and 91% of the total direct additive variance and maternal genetic variance, respectively. Direct genetic correlations ranged from low and negative to positively high, and generally decreased as the distance between tests increased. Current study has demonstrated that RRM have potential for modeling growth of Kordi sheep and a greater response to selection would be expected, if selection focuses on body weights from 30 days of age to 120 days of age.

Random regressions to model growth in Nilagiri sheep of South India

Random regression models (RRM) have been recognised as suitable for analysis of longitudinal data like growth. Growth in Nilagiri sheep was modeled using RRM to estimate genetic parameters and results obtained were compared with values already estimated using conventional methods A mixed RRM with contemporary group of year and season of birth, sex, litter size and age of dam at lambing, as fixed effects and direct additive, maternal genetic, maternal permanent environmental and individual permanent environmental factors, as random effect was used in different orders of fit. Age at recording of weight was included as the control variable. The RRM with fourth order for all random effects and heterogenous error variance (9 classes) was found to have the best fit. The pattern of variation over ages was similar for direct and maternal genetic variance, while maternal permanent environmental variance showed contrasting trend. Direct additive and maternal genetic variance showed a parabolic trend up to 36 months of age with maximum variance between 12 and 18 months. Phenotypic and individual permanent environmental variance showed similar increasing trend with age. The heritability estimates obtained through RRM for 3 month (3W), 6 month (6W), 9 month (9W), 12 month (12W), 18 month (18W) and 24 month (24W) body weights were 0.131, 0.240, 0.275, 0.254, 0.131 and 0.068, respectively. The values for 3W and 18W were similar to those obtained through univariate analysis, while other estimates were on the higher side. The trend of estimates over ages was similar between RRM and conventional methods Values of direct and maternal genetic correlations were positive and high. This finding of high positive genetic correlation among body weight at different ages was also evident from the trend of Eigen functions estimated from genetic (co)variance matrix. The correlation between breeding values estimated from RRM and conventional REML methods: were 0.904, 0.331, 0.774 and 0.771 for 3W, 6W, 9W and 12W, respectively. The estimates of heritability obtained were precise for ages up to 24 months and values indicate good scope for improvement through selection.

Is my study system good enough? A case study for identifying maternal effects.

In this paper, we demonstrate how simulation studies can be used to answer questions about identifiability and consequences of omitting effects from a model. The methodology is presented through a case study where identifiability of genetic and/or individual (environmental) maternal effects is explored. Our study system is a wild house sparrow (Passer domesticus) population with known pedigree. We fit pedigree‐based (generalized) linear mixed models (animal models), with and without additive genetic and individual maternal effects, and use deviance information criterion (DIC) for choosing between these models. Pedigree and R‐code for simulations are available. For this study system, the simulation studies show that only large maternal effects can be identified. The genetic maternal effect (and similar for individual maternal effect) has to be at least half of the total genetic variance to be identified. The consequences of omitting a maternal effect when it is present are explored. Our results indicate that the total (genetic and individual) variance are accounted for. When an individual (environmental) maternal effect is omitted from the model, this only influences the estimated (direct) individual (environmental) variance. When a genetic maternal effect is omitted from the model, both (direct) genetic and (direct) individual variance estimates are overestimated.

An analysis of indirect genetic effects on adult body weight of the Pacific white shrimp Litopenaeus vannamei at low rearing density.

BackgroundOur aim was to estimate the genetic parameters for the direct genetic effect (DGE) and indirect genetic effects (IGE) on adult body weight in the Pacific white shrimp. IGE is the heritable effect of an individual on the trait values of its group mates.MethodsTo examine IGE on body weight, 4725 shrimp from 105 tagged families were tested in multiple small test groups (MSTG). Each family was separated into three groups (15 shrimp per group) that were randomly assigned to 105 concrete tanks with shrimp from two other families. To estimate breeding values, one large test group (OLTG) in a 300 m2 circular concrete tank was used for the communal rearing of 8398 individuals from 105 families. Body weight was measured after a growth-test period of more than 200 days. Variance components for body weight in the MSTG programs were estimated using an animal model excluding or including IGE whereas variance components in the OLTG programs were estimated using a conventional animal model that included only DGE. The correlation of DGE between MSTG and OLTG programs was estimated by a two-trait animal model that included or excluded IGE.ResultsHeritability estimates for body weight from the conventional animal model in MSTG and OLTG programs were 0.26 ± 0.13 and 0.40 ± 0.06, respectively. The log likelihood ratio test revealed significant IGE on body weight. Total heritable variance was the sum of direct genetic variance (43.5 %), direct–indirect genetic covariance (2.1 %), and indirect genetic variance (54.4 %). It represented 73 % of the phenotypic variance and was more than two-fold greater than that (32 %) obtained by using a classical heritability model for body weight. Correlations of DGE on body weight between MSTG and OLTG programs were intermediate regardless of whether IGE were included or not in the model.ConclusionsOur results suggest that social interactions contributed to a large part of the heritable variation in body weight. Small and non-significant direct–indirect genetic correlations implied that neutral or slightly cooperative heritable interactions, rather than competition, were dominant in this population but this may be due to the low rearing density.

Estimation of genetic parameters related to eggshell strength using random regression models

This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen’s laying life using random regression.The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens.Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week.The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials.The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67.The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues.The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

Direct and maternal genetic components of variance for growth traits in Nilagiri and Sandyno Sheep of South India

The Nilagiri sheep is known for its prolificacy and adaptability to high altitudes of the Nilgiri hills. This breed was crossed with exotic sheep such as Rambouillet and Merino to develop a new synthetic sheep called Sandyno at Sheep Breeding Research Station, Sandynallah (Tamil Nadu). Both these breeds have been maintained as closed flocks in this station and growth data from 1992 to 2011 were utilized to estimate direct and maternal genetic (co)variance components for various growth traits viz., weight at birth (BW), 3 (3W), 6 (6W), 9 (9W), 12 (12W) and 18 (18W) months of age. An animal model including maternal effects was used to obtain restricted maximum likelihood estimates of (co)variances. The h2 values for BW, 3W, 6W, 9W, 12W and 18W were 0.140, 0.130, 0.074, 0.168, 0.129 and 0.166, respectively in Nilagiri sheep and 0.147, 0.114, 0.098, 0.141, 0.144 and 0.115 in Sandyno sheep. In Nilagiri sheep, maternal influence was significant (P<0.05) for BW, 3W, 6W and 9W and the maternal heritability values for the traits were 0.211, 0.050, 0.126 and 0.098, respectively. In Sandyno sheep, the maternal heritability values were significant (P<0.05) for BW, 3W, 6W, 9W and 18W with values of 0.285, 0.035, 0.054, 0.058 and 0.110, respectively. BW and 6W in Sandyno sheep were the only traits for which models with direct and maternal genetic correlation (σam) were significant (P<0.05). Genetic correlations among body weight traits were positive and moderate to high and ranged from 0.375 to 0.994 in Nilagiri and 0.553 to 0.952 in Sandyno sheep. Maternal effects were important with respect to growth traits in Nilagiri and Sandyno sheep. Moderate estimates of heritabilities and higher genetic correlations provide scope for further improvement in growth traits.

Use of robust multivariate linear mixed models for estimation of genetic parameters for carcass traits in beef cattle.

Assumptions of normality of residuals for carcass evaluation may make inferences vulnerable to the presence of outliers, but heavy-tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residual effects. We compare estimates of genetic parameters by fitting multivariate Normal (MN) or heavy-tail distributions (multivariate Student's t and multivariate Slash, MSt and MS) for residuals in data of hot carcass weight (HCW), longissimus muscle area (REA) and 12th to 13th rib fat (FAT) traits in beef cattle using 2475 records from 2007 to 2008 from a large commercial operation in Nebraska. Model comparisons using deviance information criteria (DIC) favoured MSt over MS and MN models, respectively. The posterior means (and 95% posterior probability intervals, PPI) of v for the MSt and MS models were 5.89±0.90 (4.35, 7.86) and 2.04±0.18 (1.70, 2.41), respectively. Smaller values of posterior densities of v for MSt and MS models confirm that the assumption of normally distributed residuals is not adequate for the analysis of the data set. Posterior mean (PM) and posterior median (PD) estimates of direct genetic variances were variable with MSt having the highest mean value followed by MS and MN, respectively. Posterior inferences on genetic variance were, however, comparable among the models for FAT. Posterior inference on additive heritabilities for HCW, REA and FAT using MN, MSt and MS models indicated similar and moderate heritability comparable with the literature. Posterior means of genetic correlations for carcass traits were variable but positive except for between REA and FAT, which showed an antagonistic relationship. We have demonstrated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals.

Very low levels of direct additive genetic variance in fitness and fitness components in a red squirrel population.

A trait must genetically correlate with fitness in order to evolve in response to natural selection, but theory suggests that strong directional selection should erode additive genetic variance in fitness and limit future evolutionary potential. Balancing selection has been proposed as a mechanism that could maintain genetic variance if fitness components trade off with one another and has been invoked to account for empirical observations of higher levels of additive genetic variance in fitness components than would be expected from mutation–selection balance. Here, we used a long-term study of an individually marked population of North American red squirrels (Tamiasciurus hudsonicus) to look for evidence of (1) additive genetic variance in lifetime reproductive success and (2) fitness trade-offs between fitness components, such as male and female fitness or fitness in high- and low-resource environments. “Animal model” analyses of a multigenerational pedigree revealed modest maternal effects on fitness, but very low levels of additive genetic variance in lifetime reproductive success overall as well as fitness measures within each sex and environment. It therefore appears that there are very low levels of direct genetic variance in fitness and fitness components in red squirrels to facilitate contemporary adaptation in this population.

Determination of the best model for estimating heritability of economic traits and their genetic and phenotypic trends in Iranian native fowl

Abstract. The objective of the present study was to estimate the heritability and to assess the existence of maternal effects for economic traits in Iranian native fowl. Variance components were estimated for body weight at hatch (BW0), 8 (BW8) and 12 (BW12) weeks of age, age at sexual maturity and weight at sexual maturity, egg number and average egg weight at 28th, 30th and 32nd weeks using restricted maximum likelihood method and six animal models. The best model was determined using the Akaike information criterion for each trait. For age at sexual maturity, the basic model consisting of direct genetic effects was superior. For weight at sexual maturity and egg number, a model consisting of maternal permanent environmental effects in addition to direct genetic effects was the best. For average egg weight at 28th, 30th and 32nd weeks, the model with direct and maternal genetic effects assuming no covariance between them was the best. For BW0, BW8 and BW12, the model including maternal genetic and permanent environmental effects in addition to direct genetic effects was the most appropriate. The estimates of direct heritability ranged from 0.05 (BW0) to 0.35 (weight at sexual maturity). Direct genetic variance and heritability were overestimated if maternal effects were ignored in the statistical model for all traits except ASM. The results indicated that the evaluation of direct and maternal genetic and phenotypic trends based on the best model for each trait was carried out. Maternal genetic trends for BW0, BW8, BW12 and average egg weight at 28th, 30th and 32nd weeks were significantly positive. Present results indicated favourable effects of the performed breeding program for all traits except BW0, during generations.

Using pooled data to estimate variance components and breeding values for traits affected by social interactions

BackgroundThrough social interactions, individuals affect one another’s phenotype. In such cases, an individual’s phenotype is affected by the direct (genetic) effect of the individual itself and the indirect (genetic) effects of the group mates. Using data on individual phenotypes, direct and indirect genetic (co)variances can be estimated. Together, they compose the total genetic variance that determines a population’s potential to respond to selection. However, it can be difficult or expensive to obtain individual phenotypes. Phenotypes on traits such as egg production and feed intake are, therefore, often collected on group level. In this study, we investigated whether direct, indirect and total genetic variances, and breeding values can be estimated from pooled data (pooled by group). In addition, we determined the optimal group composition, i.e. the optimal number of families represented in a group to minimise the standard error of the estimates.MethodsThis study was performed in three steps. First, all research questions were answered by theoretical derivations. Second, a simulation study was conducted to investigate the estimation of variance components and optimal group composition. Third, individual and pooled survival records on 12 944 purebred laying hens were analysed to investigate the estimation of breeding values and response to selection.ResultsThrough theoretical derivations and simulations, we showed that the total genetic variance can be estimated from pooled data, but the underlying direct and indirect genetic (co)variances cannot. Moreover, we showed that the most accurate estimates are obtained when group members belong to the same family. Additional theoretical derivations and data analyses on survival records showed that the total genetic variance and breeding values can be estimated from pooled data. Moreover, the correlation between the estimated total breeding values obtained from individual and pooled data was surprisingly close to one. This indicates that, for survival in purebred laying hens, loss in response to selection will be small when using pooled instead of individual data.ConclusionsUsing pooled data, the total genetic variance and breeding values can be estimated, but the underlying genetic components cannot. The most accurate estimates are obtained when group members belong to the same family.

Application of multivariate heavy-tailed distributions to residuals in the estimation of genetic parameters of growth traits in beef cattle1

Assumptions of normality in most animal breeding applications may make inferences vulnerable to the presence of outliers. Heavy-tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residual effects. Our objective is to compare estimates of genetic parameters by fitting multivariate normal (MN) or heavy-tail distributions [multivariate Student's t (MSt) and multivariate slash (MS)] for residuals in data of body birth weight (BBW), weaning (WW), and yearling (YW) weight traits in beef cattle. A total of 17,019 weight records for BBW, WW, and YW from 1998 through 2010 from a large commercial cow/calf operation in the sand hills of Nebraska were analyzed. Models included fixed effects of contemporary group and sire breed whereas animal and maternal effects were random and the degrees of freedom (v) was treated as unknown for MSt and MS. Model comparisons using deviance information criteria (DIC) favored MSt over MS and MN models, respectively. The posterior means [and 95% posterior probability intervals (PPI)] of v for the MSt and MS models were 5.28 (4.80, 5.85) and 1.88 (1.76, 2.00), respectively. Smaller values of posterior densities of v for MSt and MS models confirm that the assumption of normally distributed residuals is not adequate for the analysis of BBW, WW, and YW datasets. Posterior mean (PM) and posterior median (PD) estimates of direct and maternal genetic variances were the same and posterior densities of these parameters were found to be symmetric. The 95% PPI estimates from MN and MSt models for BBW did not overlap, which indicates significant difference between PM estimates from MN or MSt models. The observed antagonistic relationship between additive direct and additive maternal effects indicated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals.