Parent of origin genetic effects on milk production traits in a population of Iranian Holstein cows

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log-likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability (h mi 2 $$ {h}_{\mathrm{mi}}^2 $$ ) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability (h a 2 $$ {h}_{\mathrm{a}}^2 $$ ) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (rmi) were in a wide range between -0.75 ± 0.15 (P%-SCS) and 0.95 ± 0.11 (MY-ADM). Additive genetic correlations (ra) ranged between -0.54 ± 0.05 (MY-P%) and 0.99 ± 0.01 (MY-ADM) and phenotypic correlations (rp) ranged from -0.30 ± 0.01 (MY-F%) to 0.93 ± 0.01 (MY-ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top-ranked animals implying re-ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re-ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended.

A multivariate gametic model for the analysis of purebred and crossbred data. An example between two populations of Iberian pigs

Crossbreeding plays a pivotal role within pig breeding programmes, aiming to maximize heterosis and improve reproductive traits in crossbred maternal lines. Nevertheless, there is evidence indicating that the performance of reciprocal crosses between two genetic lines might exhibit variability. These variations in performance can be attributed to differences in the correlations between gametic effects, acting as either sire or dam, within purebred and crossbred populations. To address this issue, we propose a multivariate gametic model that incorporates up to four correlated gametic effects for each parental population. The model is employed on a data set comprising litter size data (total number of piglets born-TNB- and number of piglets born alive-NBA-) derived from a reciprocal cross involving two Iberian pig populations: Entrepelado and Retinto. The data set comprises 6933 records from 1564 purebred Entrepelado (EE) sows, 4995 records from 1015 Entrepelado × Retinto (ER) crosses, 2977 records from 756 Retinto × Entrepelado (RE) crosses and 7497 records from 1577 purebred Retinto (RR) sows. The data set is further supplemented by a pedigree encompassing 6007 individual-sire-dam entries. The statistical model also included the order of parity (with six levels), the breed of the service sire (five levels) and the herd-year-season effects (141 levels). Additionally, the model integrates random dominant and permanent environmental sow effects. The analysis employed a Bayesian approach, and the results revealed all the posterior estimates of the gametic correlations to be positive. The range of the posterior mean estimates of the correlations varied across different gametic effects and traits, with a range between 0.04 (gametic correlation between the paternal effects for purebred and the maternal for crossbred in Retinto) and 0.53 (gametic correlation between the paternal effects for purebred and the paternal for crossbred in Entrepelado). Furthermore, the posterior mean variance estimates of the maternal gametic effects were consistently surpassed those for paternal effects within all four populations. The results suggest the possible influence of imprinting effects on the genetic control of litter size, and underscore the importance of incorporating crossbred data into the breeding value predictions for purebred individuals.

Quantitative analysis of parent-of-origin effect in reproductive and morphological selection criteria in the Pura Raza Española horse

It is generally assumed that parents make a genetically equal contribution to their offspring, but this assumption might not always hold. This is because the expression of a gene can be blocked by methylation during gametogenesis, and the degree of methylation can depend on the origin of the parental gene (imprinting) or by preferential management associated with genetic merit. The first consequences of this for quantitative genetics is that the mean phenotypes of reciprocal heterozygotes need no longer be the same, as would be expected according to Mendelian heritage. We analysed three mare reproductive traits (reproductive efficiency, age at first foaling and foaling number) and three morphological traits (height at withers, thoracic circumference, and scapula-ischial length) in the Pura Raza Española (PRE) horse population, which possesses a deep and reliable pedigree, making it a perfect breed for analysing the quantitative effect of parent-of-origin. The number of animals analysed ranged from 44,038 to 144,191, all of them with both parents known. The model comparison between a model without parent-of-origin effects and three different models with parent-of-origin effects revealed that both maternal and paternal gametic effects influence all the analysed traits. The maternal gametic effect had a higher influence on most traits, accounting for between 3% and 11% of the total phenotypic variance, while the paternal gametic effect accounted for a higher proportion of variance in one trait, age at first foaling (4%). As expected, the Pearson's correlations between additive breeding values of models that consider parent-of-origin and that do not consider parent-of-origin were very high; however, the percentage of coincident animals slightly decreases when comparing animals with the highest estimated breeding values. Ultimately, this work demonstrates that parent-of-origin effects exist in horse gene transmission from a quantitative point of view. Additionally, including an estimate of the parent-of-origin effect within the PRE horse breeding program could be a great tool for a better parent's selection and that could be of interest for breeders, as this value will determine whether the animals acquire genetic categories and are much more highly valued.

Relative contribution of Imprinting, X chromosome and Litter effects to phenotypic variation in economic traits of sheep

Data on Zandi sheep were analysed to quantify maternal and paternal imprinting, X chromosome and litter effects' contribution to phenotypic variation in birth weight (BW), weaning weight (WW), growth rate (GR), Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR). To this end, a two-step approach was adopted. In the first step, each trait was analysed with a series of 16 animal models, which were identical for fixed and autosomal additive genetic effects but differed for combinations of maternal permanent environmental, maternal genetic, X chromosome and litter effects. For each trait, the best model was selected by the Akaike information criterion (AIC) and likelihood ratio tests (LRTs). In the second step, three additional models were fitted by adding maternal imprinting, paternal imprinting or both (models 17, 18 and 19) to the best model selected in the first step. Estimators of bias, dispersion and accuracy of breeding values estimated within 19 models with whole, and partial data were used to evaluate how well were the 19 models in estimating breeding values for the animals when their records were masked. For all traits studied, fitting the litter effect led to a better data fit. Also, it resulted in noticeable decreases in residual variance and other maternal variances. For growth traits, models containing the X-linked effects fitted the data substantially better than corresponding models without the X-linked effects. For BW, WW and GR, estimates of X-linked heritability ( h s 2 $$ {h}_s^2 $$ ) ranged between 0.09 (GR) and 0.14 (BW). Ignoring X-linked effects from the genetic evaluation model resulted in significant inflated autosomal additive genetic variance. For BW, WW, EF and RGR, models containing the imprinting effects provided a better fit of the data than otherwise identical models. Imprinting effects contributed significantly to the phenotypic variation of these traits in a range between 5% (RGR) and 8% (BW, WW). A sharp decline was observed in autosomal additive genetic variance following including imprinting effects in the model (27% to 40% depending on the trait). The least bias and dispersion, as well as greater accuracies for breeding values of focal animals, were for a model which included imprinting, X-linked and litter effects. It was concluded that imprinting, X-linked and litter effects need to be included in the genetic evaluation models for growth and efficiency-related traits of Zandi lambs.

Genetic and genomic analysis of oxygen consumption in mice

We estimated genetic parameters for oxygen consumption (OC), OC per metabolic body weight (OCMBW) and body weight at three through 8 weeks of age in divergently selected mice populations, with an animal model considering maternal genetic, common litter environmental and cytoplasmic inheritance effects. Cytoplasmic inheritance was considered based on maternal lineage information. With respect to OC, estimated direct heritability was moderate (0.32) and the estimated proportion of the variance of cytoplasmic inheritance effects to the phenotypic variance was very low (0.01), implying that causal genes for OC could be located on autosomes. To assess this hypothesis, we attempted to identify possible candidate causal genes through selective signature detection with the results of pooled whole-genome resequencing using pooled DNA samples from high and low OC mice. We made a list of possible candidate causal genes for OC, including those relating to electron transport chain and ATP-binding proteins (Ndufa12, Sdhc, Atp10b, etc.), Prr16 encoding Largen protein, Cry1 encoding a key component of the circadian core oscillator and so on. The results, although careful interpretation must be required, could contribute to elucidate the genetic mechanism of OC, an indicator for maintenance energy requirement, and therefore feed efficiency.

Generalized gametic relationships for flexible analyses of parent-of-origin effects

A class of epigenetic inheritance patterns known as genomic imprinting allows alleles to influence the phenotype in a parent-of-origin-specific manner. Various pedigree-based parent-of-origin analyses of quantitative traits have attempted to determine the share of genetic variance that is attributable to imprinted loci. In general, these methods require four random gametic effects per pedigree member to account for all possible types of imprinting in a mixed model. As a result, the system of equations may become excessively large to solve using all available data. If only the offspring have records, which is frequently the case for complex pedigrees, only two averaged gametic effects (transmitting abilities) per parent are required (reduced model). However, the parents may have records in some cases. Therefore, in this study, we explain how employing single gametic effects solely for informative individuals (i.e., phenotyped individuals), and only average gametic effects otherwise, significantly reduces the complexity compared with classical gametic models. A generalized gametic relationship matrix is the covariance of this mixture of effects. The matrix can also make the reduced model much more flexible by including observations from parents. Worked examples are present to illustrate the theory and a realistic body mass data set in mice is used to demonstrate its utility. We show how to set up the inverse of the generalized gametic relationship matrix directly from a pedigree. An open-source program is used to implement the rules. The application of the same principles to phased marker data leads to a genomic version of the generalized gametic relationships.

Incorporating parent-of-origin effects in whole-genome prediction of complex traits

Background

Parent-of-origin effects are due to differential contributions of paternal and maternal lineages to offspring phenotypes. Such effects include, for example, maternal effects in several species. However, epigenetically induced parent-of-origin effects have recently attracted attention due to their potential impact on variation of complex traits. Given that prediction of genetic merit or phenotypic performance is of interest in the study of complex traits, it is relevant to consider parent-of-origin effects in such predictions. We built a whole-genome prediction model that incorporates parent-of-origin effects by considering parental allele substitution effects of single nucleotide polymorphisms and gametic relationships derived from a pedigree (the POE model). We used this model to predict body mass index in a mouse population, a trait that is presumably affected by parent-of-origin effects, and also compared the prediction performance to that of a standard additive model that ignores parent-of-origin effects (the ADD model). We also used simulated data to assess the predictive performance of the POE model under various circumstances, in which parent-of-origin effects were generated by mimicking an imprinting mechanism.

Results

The POE model did not predict better than the ADD model in the real data analysis, probably due to overfitting, since the POE model had far more parameters than the ADD model. However, when applied to simulated data, the POE model outperformed the ADD model when the contribution of parent-of-origin effects to phenotypic variation increased. The superiority of the POE model over the ADD model was up to 8 % on predictive correlation and 5 % on predictive mean squared error.

Conclusions

The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted. Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence. Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

Use of Genomic Tools to Improve Cattle Health in the Context of Infectious Diseases

Although infectious diseases impose a heavy economic burden on the cattle industry, the etiology of many disorders that affect livestock is not fully elucidated, and effective countermeasures are often lacking. The main tools available until now have been vaccines, antibiotics and antiparasitic drugs. Although these have been very successful in some cases, the appearance of parasite and microbial resistance to these treatments is a cause of concern. Next-generation sequencing provides important opportunities to tackle problems associated with pathogenic illnesses. This review describes the rapid gains achieved to track disease progression, identify the pathogens involved, and map pathogen interactions with the host. Use of novel genomic tools subsequently aids in treatment development, as well as successful creation of breeding programs aimed toward less susceptible livestock. These may be important tools for mitigating the long term effects of combating infection and helping reduce the reliance on antibiotic treatment.

Consequences of paternally inherited effects on the genetic evaluation of maternal effects

BACKGROUND

Mixed models are commonly used for the estimation of variance components and genetic evaluation of livestock populations. Some evaluation models include two types of additive genetic effects, direct and maternal. Estimates of variance components obtained with models that account for maternal effects have been the subject of a long-standing controversy about strong negative estimates of the covariance between direct and maternal effects. Genomic imprinting is known to be in some cases statistically confounded with maternal effects. In this study, we analysed the consequences of ignoring paternally inherited effects on the partitioning of genetic variance.

RESULTS

We showed that the existence of paternal parent-of-origin effects can bias the estimation of variance components when maternal effects are included in the evaluation model. Specifically, we demonstrated that adding a constraint on the genetic parameters of a maternal model resulted in correlations between relatives that were the same as those obtained with a model that fits only paternally inherited effects for most pairs of individuals, as in livestock pedigrees. The main consequence is an upward bias in the estimates of the direct and maternal additive genetic variances and a downward bias in the direct-maternal genetic covariance. This was confirmed by a simulation study that investigated five scenarios, with the trait affected by (1) only additive genetic effects, (2) only paternally inherited effects, (3) additive genetic and paternally inherited effects, (4) direct and maternal additive genetic effects and (5) direct and maternal additive genetic plus paternally inherited effects. For each scenario, the existence of a paternally inherited effect not accounted for by the estimation model resulted in a partitioning of the genetic variance according to the predicted pattern. In addition, a model comparison test confirmed that direct and maternal additive models and paternally inherited models provided an equivalent fit.

CONCLUSIONS

Ignoring paternally inherited effects in the maternal models for genetic evaluation can lead to a specific pattern of bias in variance component estimates, which may account for the unexpectedly strong negative direct-maternal genetic correlations that are typically reported in the literature.

The use of epigenetic phenomena for the improvement of sheep and cattle

This review considers the evidence for inheritance across generations of epigenetic marks and how this phenomenon could be exploited in the cattle and sheep industries. Epigenetic marks are chemical changes in the chromosomes that affect the expression of genes and hence the phenotype of the cell and are passed on during mitosis so that the daughter cells have the same chemical changes or epigenetic marks as the parent cell. Although most epigenetic marks are wiped clean in the process of forming a new zygote, some epigenetic marks (epimutations) may be passed on from parent to offspring. The inheritance of epigenetic marks across generations is difficult to prove as there are usually alternative explanations possible. There are few well documented cases, mainly using inbred strains of mice. The epimutations are unstable and revert to wild type after a few generations. Although, there are no known cases in sheep or cattle, it is likely that inherited epimutations occur in these species but it is unlikely that they explain a large part of the inherited or genetic variation. There is limited evidence in mice and rats that an environmental treatment can cause a change in the epigenetic marks of an animal and that this change can be passed on the next generation. If inherited epimutations occur in sheep and cattle, they will already be utilized to some extent by existing genetic improvement programs. It would be possible to modify the statistical models used in the calculation of estimated breeding values to better recognize the variance controlled by epimutations, but it would probably have, at best, a small effect on the rate on genetic (inherited) gain achieved. Although not a genetic improvement, the inheritance of epigenetic marks caused by the environment experienced by the sire offers a new opportunity in sheep and cattle breeding. However, at present we do not know if this occurs or, if it does, what environmental treatment might have a beneficial effect.