Microbial signature inferred from genomic breeding selection on milk urea concentration and its relation to proxies of nitrogen-utilization efficiency in Holsteins.

Increasing the nitrogen-utilization efficiency (NUE) of dairy cows by breeding selection would offer advantages from nutritional, environmental, and economic perspectives. Because data collection of NUE phenotypes is not feasible in large cow cohorts, the cow individual milk urea concentration (MU) has been suggested as an indicator trait. Considering the symbiotic interplay between dairy cows and their rumen microbiome, individual MU was thought to be influenced by host genetics and by the rumen microbiome, the latter in turn being partly attributed to host genetics. To enhance our knowledge of MU as an indicator trait for NUE, we aimed to identify differential abundant rumen microbial genera between Holstein cows with divergent genomic breeding values for MU (GBVMU; GBVHMU vs. GBVLMU, where H and L indicate high and low MU phenotypes, respectively). The microbial genera identified were further investigated for their correlations with MU and 7 additional NUE-associated traits in urine, milk, and feces in 358 lactating Holsteins. Statistical analysis of microbial 16S rRNA amplicon sequencing data revealed significantly higher abundances of the ureolytic genus Succinivibrionaceae UCG-002 in GBVLMU cows, whereas GBVHMU animals hosted higher abundances of Clostridia unclassified and Desulfovibrio. The entire discriminating ruminal signature of 24 microbial taxa included a further 3 genera of the Lachnospiraceae family that revealed significant correlations to MU values and were therefore proposed as considerable players in the GBVMU-microbiome-MU axis. The significant correlations of Prevotellaceae UCG-003, Anaerovibrio, Blautia, and Butyrivibrio abundances with MU measurements, milk nitrogen, and N content in feces suggested their contribution to genetically determined N-utilization in Holstein cows. The microbial genera identified might be considered for future breeding programs to enhance NUE in dairy herds.

Genomic characterization of the world's longest selection experiment in mouse reveals the complexity of polygenic traits.

BACKGROUND: Long-term selection experiments are a powerful tool to understand the genetic background of complex traits. The longest of such experiments has been conducted in the Research Institute for Farm Animal Biology (FBN), generating extreme mouse lines with increased fertility, body mass, protein mass and endurance. For >140 generations, these lines have been maintained alongside an unselected control line, representing a valuable resource for understanding the genetic basis of polygenic traits. However, their history and genomes have not been reported in a comprehensive manner yet. Therefore, the aim of this study is to provide a summary of the breeding history and phenotypic traits of these lines along with their genomic characteristics. We further attempt to decipher the effects of the observed line-specific patterns of genetic variation on each of the selected traits. RESULTS: Over the course of >140 generations, selection on the control line has given rise to two extremely fertile lines (>20 pups per litter each), two giant growth lines (one lean, one obese) and one long-distance running line. Whole genome sequencing analysis on 25 animals per line revealed line-specific patterns of genetic variation among lines, as well as high levels of homozygosity within lines. This high degree of distinctiveness results from the combined effects of long-term continuous selection, genetic drift, population bottleneck and isolation. Detection of line-specific patterns of genetic differentiation and structural variation revealed multiple candidate genes behind the improvement of the selected traits. CONCLUSIONS: The genomes of the Dummerstorf trait-selected mouse lines display distinct patterns of genomic variation harbouring multiple trait-relevant genes. Low levels of within-line genetic diversity indicate that many of the beneficial alleles have arrived to fixation alongside with neutral alleles. This study represents the first step in deciphering the influence of selection and neutral evolutionary forces on the genomes of these extreme mouse lines and depicts the genetic complexity underlying polygenic traits.

Two mouse lines selected for large litter size display different lifetime fecundities.

We recently described two outbred mouse lines that were selected for large litter size at first delivery. However, lifetime fecundity appears to be economically more important for the husbandry of many polytocous species for which mouse lines might serve as bona fide animal models (e.g. for pigs). In the present study, we compared the lifetime fecundities of two highly fertile mouse lines (FL1 and FL2: >20 offspring/litter at first delivery) with those of an unselected control line (ctrl) and two lines that were selected for high body weight (DU6) and high protein mass (DU6P) without selection pressure on fertility. We tested the hypothesis that selection for large litter size at first parturition would also increase lifetime fecundity in mice, and we observed very large differences between lines. Whereas FL1 and ctrl delivered up to nine and ten litters, none of the DU6 and DU6P females gave birth to more than five litters. In line with this observation, FL1 delivered the most pups per lifetime (85.7/female). FL2 females produced the largest average litter sizes (20.4 pups/litter) in the first four litters; however, they displayed a reduced number of litters. With the exception of ctrl, litter sizes declined from litter to litter. Repeated delivery of litters with high offspring numbers did not affect the general health of FL females. The presented data demonstrate that two biodiverse, highly fertile mouse lines selected for large litter size at first delivery show different lifetime reproductive fitness levels. Thus, these mouse lines might serve as valuable mouse models for investigating lifetime productivity and longevity in farm animals.

Testicular transcriptional signatures associated with high fertility.

Factors of high fertility are poorly described. The majority of transgenic or knockout models with a reproductive phenotype are subfertile or infertile phenotypes. Few genotypes have been linked to improved reproductive performance (0.2%) or increased litter size (1%). In this study, we used a unique mouse model, fertility line FL1, selected for 'high fertility' for more than 170 generations. This strain has almost doubled the number of littermates as well as their total birth weight accompanied by an elevated ovulation rate and increased numbers of corpora lutea compared to a randomly mated and unselected control line (Ctrl). Here, we investigate whether the gonadal tissue of FL1 males are affected by 'co-evolution' after more than 40 years of female-focused selection. Using microarrays, we analysed the testicular transcriptome of the FL1 and Ctrl mice. These data were also compared with previously published female gonadal transcriptional alterations. We detected alterations in testicular gene expression, which are partly associated with female reproductive performance. Thus, female-focused selection for litter size has not only affected the female side, but also has been manifested in transcriptional alterations on the male gonadal organ. This suggests consequences for the entire mouse lines in the long run and emphasizes the perspective of inevitably considering both genders about mechanisms of high fertility.

Selection for female traits of high fertility affects male reproductive performance and alters the testicular transcriptional profile.

BACKGROUND: Many genes important for reproductive performance are shared by both sexes. However, fecundity indices are primarily based on female parameters such as litter size. We examined a fertility mouse line (FL2), which has a considerably increased number of offspring and a total litter weight of 180% compared to a randomly bred control line (Ctrl) after more than 170 generations of breeding. In the present study, we investigated whether there might be a parallel evolution in males after more than 40 years of breeding in this outbred mouse model. RESULTS: Males of the fertility mouse line FL2 showed reduced sperm motility performance in a 5 h thermal stress experiment and reduced birth rate in the outbred mouse line. Transcriptional analysis of the FL2 testis showed the differential expression of genes associated with steroid metabolic processes (Cyp1b1, Cyp19a1, Hsd3b6, and Cyp21a1) and female fecundity (Gdf9), accompanied by 150% elevated serum progesterone levels in the FL2 males. Cluster analysis revealed the downregulation of genes of the kallikrein-related peptidases (KLK) cluster located on chromosome 7 in addition to alterations in gene expression with serine peptidase activity, e.g., angiotensinogen (Agt), of the renin-angiotensin system essential for ovulation. Although a majority of functional annotations map to female reproduction and ovulation, these genes are differentially expressed in FL2 testis. CONCLUSIONS: These data indicate that selection for primary female traits of increased litter size not only affects sperm characteristics but also manifests as transcriptional alterations of the male side likely with direct long-term consequences for the reproductive performance of the mouse line.

Reproductive performance primarily depends on the female genotype in a two-factorial breeding experiment using high-fertility mouse lines.

Mouse models showing an improved fertility phenotype are barely described in the literature. In the present study, we further characterized two outbred mouse models that have been selected for the phenotype 'high fertility' for more than 177 generations (fertility lines (FL) 1 and 2). In order to delineate the impact of males and females on fertility parameters, we performed a two-factorial breeding experiment by mating males and females of the three different genotypes (FL1, FL2, unselected control (Ctrl)) in all 9 possible combinations. Reproductive performance, such as number of offspring per litter or total birth weight of the entire pup, mainly depends on the female genotype. Although the reproductive performance of FL1 and FL2 is very similar, their phenotypes differ. FL2 animals of both genders are larger compared to FL1 and control animals. Females of the control line delivered offspring earlier compared to FL1 and FL2 dams. Males of FL1 are the lightest and the only ones who gained weight during the two weeks mating period. To address whether this effect is correlated with differing serum androgen levels, we measured the concentrations of testosterone, dehydroepiandrosterone, 4-androstenedione, androstanediol and dihydrotestosterone in males of all three lines by GC-MS. We measured serum testosterone between 5.0 and 6.4 ng/mL, whereas the concentrations of the other androgens were at least one order of magnitude lower, with no significant differences between the lines. Our data indicate that reproductive outcome largely depends on the genotype of the female in a two-factorial breeding experiment and supports previous findings that the phenotype 'high fertility' is warranted by using different physiological strategies.

Adrenal cortex expression quantitative trait loci in a German Holstein × Charolais cross.

BACKGROUND: The importance of the adrenal gland in regard to lactation and reproduction in cattle has been recognized early. Caused by interest in animal welfare and the impact of stress on economically important traits in farm animals the adrenal gland and its function within the stress response is of increasing interest. However, the molecular mechanisms and pathways involved in stress-related effects on economically important traits in farm animals are not fully understood. Gene expression is an important mechanism underlying complex traits, and genetic variants affecting the transcript abundance are thought to influence the manifestation of an expressed phenotype. We therefore investigated the genetic background of adrenocortical gene expression by applying an adaptive linear rank test to identify genome-wide expression quantitative trait loci (eQTL) for adrenal cortex transcripts in cattle. RESULTS: A total of 10,986 adrenal cortex transcripts and 37,204 single nucleotide polymorphisms (SNPs) were analysed in 145 F2 cows of a Charolais × German Holstein cross. We identified 505 SNPs that were associated with the abundance of 129 transcripts, comprising 482 cis effects and 17 trans effects. These SNPs were located on all chromosomes but X, 16, 24 and 28. Associated genes are mainly involved in molecular and cellular functions comprising free radical scavenging, cellular compromise, cell morphology and lipid metabolism, including genes such as CYP27A1 and LHCGR that have been shown to affect economically important traits in cattle. CONCLUSIONS: In this study we showed that adrenocortical eQTL affect the expression of genes known to contribute to the phenotypic manifestation in cattle. Furthermore, some of the identified genes and related molecular pathways were previously shown to contribute to the phenotypic variation of behaviour, temperament and growth at the onset of puberty in the same population investigated here. We conclude that eQTL analysis appears to be a useful approach providing insight into the molecular and genetic background of complex traits in cattle and will help to understand molecular networks involved.

Mendelian sampling covariability of marker effects and genetic values.

BACKGROUND: Measures of the expected genetic variability among full-sibs are of practical relevance, such as in the context of mating decisions. An important application field in animal and plant breeding is the selection and allocation of mates when large or small amounts of genetic variability among offspring are desired, depending on user-specific goals. Estimates of the Mendelian sampling variance can be obtained by simulating gametes from parents with known diplotypes. Knowledge of recombination rates and additive marker effects is also required. In this study, we aimed at developing an exact method that can account for both additive and dominance effects. RESULTS: We derived parent-specific covariance matrices that exactly quantify the within-family (co-)variability of additive and dominance marker effects. These matrices incorporate prior knowledge of the parental diplotypes and recombination rates. When combined with additive marker effects, they allow the exact derivation of the Mendelian sampling (co-)variances of (estimated) breeding values for several traits, as well for the aggregate genotype. A comparative analysis demonstrated good average agreement between the exact values and the simulation results for a practical dataset (74,353 German Holstein cattle). CONCLUSIONS: The newly derived method is suitable for calculating the exact amount of intra-family variation of the estimated breeding values and genetic values (comprising additive and dominance effects).

Genome-Wide Association Study of a Varroa-Specific Defense Behavior in Honeybees (Apis mellifera).

Honey bees are exposed to many damaging pathogens and parasites. The most devastating is Varroa destructor, which mainly affects the brood. A promising approach for preventing its spread is to breed Varroa-resistant honey bees. One trait that has been shown to provide significant resistance against the Varroa mite is hygienic behavior, which is a behavioral response of honeybee workers to brood diseases in general. Here, we report the use of an Affymetrix 44K SNP array to analyze SNPs associated with detection and uncapping of Varroa-parasitized brood by individual worker bees (Apis mellifera). For this study, 22 000 individually labeled bees were video-monitored and a sample of 122 cases and 122 controls was collected and analyzed to determine the dependence/independence of SNP genotypes from hygienic and nonhygienic behavior on a genome-wide scale. After false-discovery rate correction of the P values, 6 SNP markers had highly significant associations with the trait investigated (α < 0.01). Inspection of the genomic regions around these SNPs led to the discovery of putative candidate genes.

On the relevance of technical variation due to building pools in microarray experiments.

BACKGROUND: Pooled samples are frequently used in experiments measuring gene expression. In this method, RNA from different individuals sharing the same experimental conditions and explanatory variables is blended and their concentrations are jointly measured. As a matter of principle, individuals are represented in equal shares in each pool. However, some degree of disproportionality may arise from the limits of technical precision. As a consequence a special kind of technical error occurs, which can be modelled by a respective variance component. Previously published theory - allowing for variable pool sizes - has been applied to four microarray gene expression data sets from different species in order to assess the practical relevance of this type of technical error in terms of significance and size of this variance component. RESULTS: The number of transcripts with a significant variance component due to imperfect blending was found to be 4329 (23 %) in mouse data and 7093 (49 %) in honey bees, but only 6 in rats and none whatsoever in human data. These results correspond to a false discovery rate of 5 % in each data set. The number of transcripts found to be differentially expressed between treatments was always higher when the blending error variance was neglected. Simulations clearly indicated overly-optimistic (anti-conservative) test results in terms of false discovery rates whenever this source of variability was not represented in the model. CONCLUSIONS: Imperfect equality of shares when blending RNA from different individuals into joint pools of variable size is a source of technical variation with relevance for experimental design, practice at the laboratory bench and data analysis. Its potentially adverse effects, incorrect identification of differentially expressed transcripts and overly-optimistic significance tests, can be fully avoided, however, by the sound application of recently established theory and models for data analysis.

Flexible pooling in gene expression profiles: design and statistical modeling of experiments for unbiased contrasts.

Pooling is an important resource in microarray gene expression experiments. Due to restrictions imposed by the statistical analysis it is widespread practice to employ a fixed pool size over the whole experiment. But this limits the efficient use of experimental material. In this paper we propose a design with flexible pool sizes for mRNA pooling which includes varying numbers of experimental units per pool. Enforcing balance between the pool sizes of every treatment level, we show the unbiasedness of the generalized least squares estimator of a contrast testing for differences in gene expression between treatments. In order to model the variability of pooled observations we include random biological effects as well as a special kind of technical error (random effect for mixtures), induced by inaccuracies in blending aliquots of mRNA from different individuals into common pools. Results for one-color arrays are also extended to two-color arrays.

Genome-Wide Association Analysis of Semen Characteristics in Piétrain Boars.

Since artificial insemination is common practice in pig breeding, the quality and persistence of the semen are decisive for the usability of individual boars. In the current study, genome-wide association analyses were performed to investigate the genetic variability underlying phenotypic variations in semen characteristics. These traits comprise sperm morphology and sperm motility under different temporal and thermal storage conditions, in addition to standard semen quality parameters. Two consecutive samples of the fourth and fifth ejaculates from the same boar were comprehensively analyzed in a genotyped Piétrain boar population. A total of 13 genomic regions on different chromosomes were identified that contain single-nucleotide polymorphisms significantly associated with these traits. Subsequent analysis of the genomic regions revealed candidate genes described to be involved in spermatogenesis, such as FOXL3, GPER1, PDGFA, PRKAR1B, SNRK, SUN1, and TSPO, and sperm motility, including ARRDC4, CEP78, DNAAF5, and GPER1. Some of these genes were also associated with male fertility or infertility in mammals (e.g., CEP78, GPER1). The analyses based on these laboriously determined and valuable phenotypes contribute to a better understanding of the genetic background of male fertility traits in pigs and could prospectively contribute to the improvement of sperm quality through breeding approaches.

Accuracy of the unified approach in maternally influenced traits--illustrated by a simulation study in the honey bee (Apis mellifera).

BACKGROUND: The honey bee is an economically important species. With a rapid decline of the honey bee population, it is necessary to implement an improved genetic evaluation methodology. In this study, we investigated the applicability of the unified approach and its impact on the accuracy of estimation of breeding values for maternally influenced traits on a simulated dataset for the honey bee. Due to the limitation to the number of individuals that can be genotyped in a honey bee population, the unified approach can be an efficient strategy to increase the genetic gain and to provide a more accurate estimation of breeding values. We calculated the accuracy of estimated breeding values for two evaluation approaches, the unified approach and the traditional pedigree based approach. We analyzed the effects of different heritabilities as well as genetic correlation between direct and maternal effects on the accuracy of estimation of direct, maternal and overall breeding values (sum of maternal and direct breeding values). The genetic and reproductive biology of the honey bee was accounted for by taking into consideration characteristics such as colony structure, uncertain paternity, overlapping generations and polyandry. In addition, we used a modified numerator relationship matrix and a realistic genome for the honey bee. RESULTS: For all values of heritability and correlation, the accuracy of overall estimated breeding values increased significantly with the unified approach. The increase in accuracy was always higher for the case when there was no correlation as compared to the case where a negative correlation existed between maternal and direct effects. CONCLUSIONS: Our study shows that the unified approach is a useful methodology for genetic evaluation in honey bees, and can contribute immensely to the improvement of traits of apicultural interest such as resistance to Varroa or production and behavioural traits. In particular, the study is of great interest for cases where negative correlation between maternal and direct effects and uncertain paternity exist, thus, is of relevance for other species as well. The study also provides an important framework for simulating genomic and pedigree datasets that will prove to be helpful for future studies.

Optimising multistage dairy cattle breeding schemes including genomic selection using decorrelated or optimum selection indices.

BACKGROUND: The prediction of the outcomes from multistage breeding schemes is especially important for the introduction of genomic selection in dairy cattle. Decorrelated selection indices can be used for the optimisation of such breeding schemes. However, they decrease the accuracy of estimated breeding values and, therefore, the genetic gain to an unforeseeable extent and have not been applied to breeding schemes with different generation intervals and selection intensities in each selection path. METHODS: A grid search was applied in order to identify optimum breeding plans to maximise the genetic gain per year in a multistage, multipath dairy cattle breeding program. In this program, different values of the accuracy of estimated genomic breeding values and of their costs per individual were applied, whereby the total breeding costs were restricted. Both decorrelated indices and optimum selection indices were used together with fast multidimensional integration algorithms to produce results. RESULTS: In comparison to optimum indices, the genetic gain with decorrelated indices was up to 40% less and the proportion of individuals undergoing genomic selection was different. Additionally, the interaction between selection paths was counter-intuitive and difficult to interpret. Independent of using decorrelated or optimum selection indices, genomic selection replaced traditional progeny testing when maximising the genetic gain per year, as long as the accuracy of estimated genomic breeding values was ≥ 0.45. Overall breeding costs were mainly generated in the path "dam-sire". Selecting males was still the main source of genetic gain per year. CONCLUSION: Decorrelated selection indices should not be used because of misleading results and the availability of accurate and fast algorithms for exact multidimensional integration. Genomic selection is the method of choice when maximising the genetic gain per year but genotyping females may not allow for a reduction in overall breeding costs. Furthermore, the economic justification of genotyping females remains questionable.

Simulating a base population in honey bee for molecular genetic studies.

BACKGROUND: Over the past years, reports have indicated that honey bee populations are declining and that infestation by an ecto-parasitic mite (Varroa destructor) is one of the main causes. Selective breeding of resistant bees can help to prevent losses due to the parasite, but it requires that a robust breeding program and genetic evaluation are implemented. Genomic selection has emerged as an important tool in animal breeding programs and simulation studies have shown that it yields more accurate breeding value estimates, higher genetic gain and low rates of inbreeding. Since genomic selection relies on marker data, simulations conducted on a genomic dataset are a pre-requisite before selection can be implemented. Although genomic datasets have been simulated in other species undergoing genetic evaluation, simulation of a genomic dataset specific to the honey bee is required since this species has a distinct genetic and reproductive biology. Our software program was aimed at constructing a base population by simulating a random mating honey bee population. A forward-time population simulation approach was applied since it allows modeling of genetic characteristics and reproductive behavior specific to the honey bee. RESULTS: Our software program yielded a genomic dataset for a base population in linkage disequilibrium. In addition, information was obtained on (1) the position of markers on each chromosome, (2) allele frequency, (3) χ(2) statistics for Hardy-Weinberg equilibrium, (4) a sorted list of markers with a minor allele frequency less than or equal to the input value, (5) average r(2) values of linkage disequilibrium between all simulated marker loci pair for all generations and (6) average r2 value of linkage disequilibrium in the last generation for selected markers with the highest minor allele frequency. CONCLUSION: We developed a software program that takes into account the genetic and reproductive biology specific to the honey bee and that can be used to constitute a genomic dataset compatible with the simulation studies necessary to optimize breeding programs. The source code together with an instruction file is freely accessible at http://msproteomics.org/Research/Misc/honeybeepopulationsimulator.html.

Ruminal background of predisposed milk urea (MU) concentration in Holsteins.

Efforts to reduce nitrogen (N) emissions are currently based on the optimization of dietary- N supply at average herd N requirements. The implementation of the considerable individual differences and predispositions in N- use efficiency and N- excretion in breeding programs is hampered by the difficulty of data collection. Cow individual milk urea (MU) concentration has been proposed as an easy-to-measure surrogate trait, but recent studies questioned its predictive power. Therefore, a deeper understanding of the biological mechanisms underlying predisposed higher (HMUg) or lower (LMUg) MU concentration in dairy cows is needed. Considering the complex N- metabolism in ruminants, the distinction between HMUg and LMUg could be based on differences in (i) the rumen microbial community, (ii) the host-specific transcription processes in the rumen villi, and (iii) the host-microbe interaction in the rumen. Therefore, rumen fluid and rumen epithelial samples from 10 HMUg and 10 LMUg cows were analyzed by 16S sequencing and HiSeq sequencing. In addition, the effect of dietary-N reduction on ruminal shifts was investigated in a second step. In total, 10 differentially abundant genera (DAG) were identified between HMUg and LMUg cows, elucidating greater abundances of ureolytic Succinivibrionaceae_UCG-002 and Ruminococcaceae_unclassified in LMUg animals and enhanced occurrences of Butyvibrio in HMUg cows. Differential expression analysis revealed genes of the bovine Major Histocompatibility Complex (BOLA genes) as well as MX1, ISG15, and PRSS2 displaying candidates of MU predisposition that further attributed to enhanced immune system activities in LMUg cows. A number of significant correlations between microbial genera and host transcript abundances were uncovered, including strikingly positive correlations of BOLA-DRA transcripts with Roseburia and Lachnospiraceae family abundances that might constitute particularly prominent microbial-host interplays of MU predisposition. The reduction of feed-N was followed by 18 DAG in HMUg and 19 DAG in LMUg, depicting pronounced interest on Shuttleworthia, which displayed controversial adaption in HMUg and LMUg cows. Lowering feed-N further elicited massive downregulation of immune response and energy metabolism pathways in LMUg. Considering breeding selection strategies, this study attributed information content to MU about predisposed ruminal N-utilization in Holstein-Friesians.

Including non-additive genetic effects in Bayesian methods for the prediction of genetic values based on genome-wide markers.

BACKGROUND: Molecular marker information is a common source to draw inferences about the relationship between genetic and phenotypic variation. Genetic effects are often modelled as additively acting marker allele effects. The true mode of biological action can, of course, be different from this plain assumption. One possibility to better understand the genetic architecture of complex traits is to include intra-locus (dominance) and inter-locus (epistasis) interaction of alleles as well as the additive genetic effects when fitting a model to a trait. Several Bayesian MCMC approaches exist for the genome-wide estimation of genetic effects with high accuracy of genetic value prediction. Including pairwise interaction for thousands of loci would probably go beyond the scope of such a sampling algorithm because then millions of effects are to be estimated simultaneously leading to months of computation time. Alternative solving strategies are required when epistasis is studied. METHODS: We extended a fast Bayesian method (fBayesB), which was previously proposed for a purely additive model, to include non-additive effects. The fBayesB approach was used to estimate genetic effects on the basis of simulated datasets. Different scenarios were simulated to study the loss of accuracy of prediction, if epistatic effects were not simulated but modelled and vice versa. RESULTS: If 23 QTL were simulated to cause additive and dominance effects, both fBayesB and a conventional MCMC sampler BayesB yielded similar results in terms of accuracy of genetic value prediction and bias of variance component estimation based on a model including additive and dominance effects. Applying fBayesB to data with epistasis, accuracy could be improved by 5% when all pairwise interactions were modelled as well. The accuracy decreased more than 20% if genetic variation was spread over 230 QTL. In this scenario, accuracy based on modelling only additive and dominance effects was generally superior to that of the complex model including epistatic effects. CONCLUSIONS: This simulation study showed that the fBayesB approach is convenient for genetic value prediction. Jointly estimating additive and non-additive effects (especially dominance) has reasonable impact on the accuracy of prediction and the proportion of genetic variation assigned to the additive genetic source.

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.