Analysing quantitative parent-of-origin effects with examples from ultrasonic measures of body composition In Australian beef cattle

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.

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.

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.

Genomic imprinting variances of beef carcass traits and physiochemical characteristics in Japanese Black cattle

The objective of this study was to estimate variance components related to imprinting for carcass traits and physiochemical characteristics in Japanese Black cattle. The carcass records obtained from 4,220 Japanese Black feedlot cattle included carcass weight (CW), rib eye area (REA), rib thickness, subcutaneous fat thickness, and beef marbling score (BMS), and the physiochemical characteristics were fat, moisture, glycogen per proportion of moisture content, oleic acid, and monounsaturated fatty acids (MUFA). To detect gametic effects, an imprinting model was fitted. High additive heritabilities were estimated for all traits (from 0.516 for glycogen to 0.853 for fat) and were reduced in Mendelian heritability. The range of the differences was from 0.002 (CW) to 0.331 (fat and moisture), and the reductions were due to their imprinting variances. The ratio of the imprinting variance to the total additive genetic variance for REA (0.374), BMS (0.291), fat (0.387), moisture (0.388), and MUFA (0.337) were large (p < 0.05). These imprinting variances were due to the maternal contribution and suggested the existence of maternally expressed genomic imprinting effects on the traits in Japanese Black cattle. Therefore, maternal gametic effects should be considered in breeding programs for Japanese Black cattle.

Effects of parent-of-origin models with different pedigree information on beef carcass traits and fatty acid composition in Japanese Black cattle

Genomic imprinting should be considered in animal breeding systems to avoid lead in bias in genetic parameter estimation. The objective of this study was to clarify the effects of pedigree information on imprinting variances for carcass traits and fatty acid composition in Japanese Black cattle. Carcass records [carcass weight, rib eye area, rib thickness (RT), subcutaneous fat thickness and beef marbling score (BMS)] and fatty acid composition were obtained for 11,855 Japanese Black feedlot cattle. To estimate and compare the imprinting variances for the traits, two imprinting models with different pedigree information [the sire-dam gametic relationship matrix (Model 1) and the sire-maternal grandsire (MGS) numerator relationship matrix (Model 2)] were fitted. The ratio of the imprinting variance to the total additive genetic variance for RT (6.33%) and BMS (19.00%) was significant in Model 1, but only that for BMS (21.09%) was significant in Model 2. This study revealed that fitting the sire-MGS model could be useful in estimating imprinting variance under certain conditions, such as when restricted pedigree information is available. Furthermore, the present result suggested that the maternal gametic effects on BMS should be included in breeding programmes for Japanese Black cattle to avoid selection bias caused by imprinting effects.

Assessment of maternal and parent of origin effects in genetic variation of economic traits in Iranian native fowl

1. The objective of the study was to investigate the influence of maternal and parent of origin effects (POE) on genetic variation of Iranian native fowl on economic traits. 2. Studied traits were body weights at birth (BW0), at eight (BW8) and 12 weeks of age (BW12), age (ASM) and weight at sexual maturity (WSM), egg number (EN) and average egg weight (AEW). 3. Several models, including additive, maternal additive genetics, permanent environmental effects and POE were compared using Wombat software. Bayesian Information Criterion (BIC) was used to identify the best model for each trait. The chance of reranking of birds between models was investigated using Spearman correlation and Wilcoxon rank test. 4. Based on the best model, direct heritability estimates for BW0, BW8, BW12, ASM, WSM, EN and AEW traits were 0.05, 0.21, 0.23, 0.30, 0.39, 0.22 and 0.38, respectively. Proportion of variance due to paternal POE for BW8 was 4% and proportion of variance due to maternal POE for BW12 was 5%. 5. Estimated maternal heritability for BW0 was 0.30 and for BW8 and BW12 were 0.00 and 0.01, respectively, which shows that maternal heritability was reduced by age. 6. Based on the results, considering POE for BW8 and BW12 and maternal genetic effects for BW0 improved the accuracy of estimations and avoid reranking of birds for these traits.

Parent-of-origin effects on carcass traits in Japanese Black cattle

Variances caused by the differential expression of paternally and maternally imprinted genes controlling carcass traits in Japanese Black cattle were estimated in this study. Data on marbling score (BMS), carcass weight, rib thickness, rib-eye area (REA) and subcutaneous fat thickness (SFT) were collected from a total of 13,115 feedlot steers and heifers in a commercial population. A sire-maternal grandsire model was used to analyse the data, and then, imprinting parameters were derived by replacing the genetic effect of the dam with the effect of the maternal grandsire in the imprinting model to calculate the genetic parameter estimates. The proportions of the total genetic variance attributable to imprinted genes ranged from 8.7% (SFT) to 35.2% (BMS). The remarkably large imprinting variance of BMS was mainly contributed by maternally expressed inheritance because the maternal contribution of the trait was much larger than that of the paternal trait. The parent-of-origin effect originating from maternal gene expression was also observed for REA. The results suggested the existence of genomic imprinting effects on the traits of the Japanese Black cattle. Hence, the parent-of-origin effect should be considered for the genetic evaluation of Japanese Black cattle in breeding programmes.

Scanning the genomes of parents for imprinted loci acting in their un-genotyped progeny

Depending on their parental origin, alleles at imprinted loci are fully or partially inactivated through epigenetic mechanisms. Their effects contribute to the broader class of parent-of-origin effects. Standard methodology for mapping imprinted quantitative trait loci in association studies requires phenotypes and parental origin of marker alleles (ordered genotypes) to be simultaneously known for each individual. As such, many phenotypes are known from un-genotyped offspring in ongoing breeding programmes (e.g. meat animals), while their parents have known genotypes but no phenotypes. By theoretical considerations and simulations, we showed that the limitations of standard methodology can be overcome in such situations. This is achieved by first estimating parent-of-origin effects, which then serve as dependent variables in association analyses, in which only imprinted loci give a signal. As a theoretical foundation, the regression of parent-of-origin effects on the number of B-alleles at a biallelic locus — representing the un-ordered genotype — equals the imprinting effect. The applicability to real data was demonstrated for about 1800 genotyped Brown Swiss bulls and their un-genotyped fattening progeny. Thus, this approach unlocks vast data resources in various species for imprinting analyses and offers valuable clues as to what extent imprinted loci contribute to genetic variability.

Parsimonious model for analyzing parent-of-origin effects related to beef traits in dual-purpose Simmental

Genomic imprinting occurs when allelic effects depend on their parental origin. These parent-of-origin effects (POE) occur because of epigenetic DNA modifications during gametogenesis according to the sex of an animal. Animal breeding programs give little consideration to imprinting, although its relationship to important traits has been shown in different agricultural species. To incorporate imprinting, a previously proposed model (imprinting model) contains the genetic effects of the sire and dam, and it provides an estimate of the variance component due to POE, which is referred to as imprinting variance. Large volumes of data are sometimes available for commercial populations, so the dimension of mixed-model equations can become very large or even excessively large when estimating imprinting variances and other genetic parameters. To address this issue, we replaced the genetic effect as dam with the effect of the maternal grandsire in the imprinting model. When combined with appropriate weightings of the observations, this replacement yields an imprinting model with a parsimonious number of genetic effects for male parents and ancestors of slaughter animals, and it enables the inclusion of large volumes of data. In addition, we derived an equivalent model to facilitate the direct estimation of POE and their prediction error variances. We applied the parsimonious model to 1,366,160 fattening bulls as well as a pedigree of 2,637,761 ancestors to investigate the relevance of POE for beef performance in dual-purpose Simmental. We analyzed the killing-out percentage, net BW gain, carcass muscularity, and fat score as slaughter traits. The parsimonious model was applied as both linear and generalized linear versions with a logit-link function. The proportions of the total genetic variance attributable to POE ranged between 8.6% and 17.1%. For 3 of the 4 traits, the maternal gamete accounted for a greater proportion of the imprinting variance. The effects of POE and their reliabilities were estimated for up to 27,567 bulls and all traits, where the reliabilities ranged between 0.38 and 0.99. Thus, our new parsimonious model is appropriate for estimating the imprinting variance using large pedigree data sets. Our results highlight the need to consider POE in genetic evaluations.

A new model for parent-of-origin effect analyses applied to Brown Swiss cattle slaughterhouse data

Genomic imprinting is a phenomenon that arises when the expression of genes depends on the parental origin of alleles. Epigenetic mechanisms may induce the full or partial suppression of maternal or paternal alleles, thereby leading to different types of imprinting. However, imprinting effects have received little consideration in animal breeding programmes, although their relevance to some agricultural important traits has been demonstrated. A recently proposed model (imprinting model) with two path-of-transmission (male and female)-specific breeding values for each animal accounts for all types of imprinting simultaneously (paternal, maternal, full and partial). Imprinting effects (or more generally: parent-of-origin effects (POE)) are determined by taking the difference between the two genetic effects in each animal. However, the computation of their prediction error variance (PEV) is laborious; thus, we propose a new model that is equivalent to the aforementioned imprinting model, which facilitates the direct estimation of imprinting effects instead of taking the differences and the PEV is readily obtained. We applied the new model to slaughterhouse data for Brown Swiss cattle, among which imprinting has never been investigated previously. Data were available for up to 173 051 fattening bulls, where the pedigrees contained up to 428 710 animals representing the entire Brown Swiss population of Austria and Germany. The traits analysed comprised the net BW gain, fat score, EUROP class and killing out percentage. The analysis demonstrated that the net BW gain, fat score and EUROP class were influenced significantly by POE. After estimating the POE, the new model yielded estimates with reliabilities ranging between 0.4 and 0.9. On average, the imprinting variances accounted for 9.6% of the total genetic variance, where the maternal gamete was the main contributor. Moreover, our results agreed well with those obtained using linear models when the EUROP class and fat score were treated as categorical traits by applying a GLMM with a logit link function.

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.

Genomic imprinting effects on complex traits in domesticated animal species

Monoallelically expressed genes that exert their phenotypic effect in a parent-of-origin specific manner are considered to be subject to genomic imprinting, the most well understood form of epigenetic regulation of gene expression in mammals. The observed differences in allele specific gene expression for imprinted genes are not attributable to differences in DNA sequence information, but to specific chemical modifications of DNA and chromatin proteins. Since the discovery of genomic imprinting some three decades ago, over 100 imprinted mammalian genes have been identified and considerable advances have been made in uncovering the molecular mechanisms regulating imprinted gene expression. While most genomic imprinting studies have focused on mouse models and human biomedical disorders, recent work has highlighted the contributions of imprinted genes to complex trait variation in domestic livestock species. Consequently, greater understanding of genomic imprinting and its effect on agriculturally important traits is predicted to have major implications for the future of animal breeding and husbandry. In this review, we discuss genomic imprinting in mammals with particular emphasis on domestic livestock species and consider how this information can be used in animal breeding research and genetic improvement programs.

Genomic best linear unbiased prediction method including imprinting effects for genomic evaluation

BACKGROUND

Genomic best linear unbiased prediction (GBLUP) is a statistical method used to predict breeding values using single nucleotide polymorphisms for selection in animal and plant breeding. Genetic effects are often modeled as additively acting marker allele effects. However, the actual mode of biological action can differ from this assumption. Many livestock traits exhibit genomic imprinting, which may substantially contribute to the total genetic variation of quantitative traits. Here, we present two statistical models of GBLUP including imprinting effects (GBLUP-I) on the basis of genotypic values (GBLUP-I1) and gametic values (GBLUP-I2). The performance of these models for the estimation of variance components and prediction of genetic values across a range of genetic variations was evaluated in simulations.

RESULTS

Estimates of total genetic variances and residual variances with GBLUP-I1 and GBLUP-I2 were close to the true values and the regression coefficients of total genetic values on their estimates were close to 1. Accuracies of estimated total genetic values in both GBLUP-I methods increased with increasing degree of imprinting and broad-sense heritability. When the imprinting variances were equal to 1.4% to 6.0% of the phenotypic variances, the accuracies of estimated total genetic values with GBLUP-I1 exceeded those with GBLUP by 1.4% to 7.8%. In comparison with GBLUP-I1, the superiority of GBLUP-I2 over GBLUP depended strongly on degree of imprinting and difference in genetic values between paternal and maternal alleles. When paternal and maternal alleles were predicted (phasing accuracy was equal to 0.979), accuracies of the estimated total genetic values in GBLUP-I1 and GBLUP-I2 were 1.7% and 1.2% lower than when paternal and maternal alleles were known.

CONCLUSIONS

This simulation study shows that GBLUP-I1 and GBLUP-I2 can accurately estimate total genetic variance and perform well for the prediction of total genetic values. GBLUP-I1 is preferred for genomic evaluation, while GBLUP-I2 is preferred when the imprinting effects are large, and the genetic effects differ substantially between sexes.