Epistatic Variance Research Articles

Investigating the impact of non-additive genetic effects in the estimation of variance components and genomic predictions for heat tolerance and performance traits in crossbred and purebred pig populations

BackgroundNon-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows.ResultsIncluding non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95–69.59% of the total genetic variance.ConclusionsIncluding non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

PSXII-3 Including Non-Additive Genetic Effects in Genomic Prediction and Estimation of Variance Components for Performance and Heat Stress Traits in Pigs

Abstract Non-additive genetic effects may have important roles in the phenotypic expression of performance and adaptation traits in livestock. Therefore, we aimed to evaluate the inclusion of non-additive genetic effects in genomic prediction models and variance component estimation of performance traits in a purebred pig population and heat tolerance indicators in a crossbred pig population. The first dataset consisted of 3,534 individuals with genotypes for 52,843 SNPs and five pre-adjusted phenotypes from a public database of a purebred pig line. Twelve models fitting or not dominance and/or epistasis effects (additive-by-additive, additive-by-dominance, and dominance-by-dominance) and inbreeding were used to estimate variance components. Prediction ability was assessed based on 10-fold cross-validation and the bias, dispersion, and accuracy estimates were computed based on the LR method. We also evaluated the impact of including non-additive genetic effects on the ranking of the animals’ breeding value and in the proportion of commonly selected individuals. The second dataset consisted of records from 1,645 lactating sows (Large White x Landrace cross) genotyped for 50,703 SNPs and traits related to heat stress response: skin temperature (ear, shoulder, rump, and tail), vaginal temperature measured every 10 minutes, and the average of the six records per hour corresponding to 08:00, 12:00, 16:00, and 20:00 hours during four days; respiration rate, panting score (PS; score scale from 0 to 3), and hair density (HD, score scale from 0 to 2). Four models including or not, inbreeding and the effect of dominance and additive-by-additive epistasis were used to estimate variance components. There was no effect on residual variance estimates due to the inclusion of non-additive genetic effects in the models for most traits. However, non-additive genetic effects reduced additive variance estimates, especially when additive-by-additive epistasis was fitted. Including non-additive genetic effects in the model did not improve the prediction accuracy of breeding values for purebreds, but there was a substantial change in the ranking of the animals and in the proportion of commonly selected individuals. For the crossbred traits, small non-additive genetic variance with large standard error estimates were obtained. Nevertheless, PS and HD presented notable additive-by-additive epistatic variance. PS presented h2aa estimates of 0.15 and additive-by-additive epistasis corresponding to 86.76% of the total genetic variance, while HD presented additive-by-additive epistatic heritability (h2aa) estimates from 0.46 to 0.49 and the proportion of total genetic variance explained by additive-by-additive epistasis ranged from 66.91% to 71.87%. In conclusion, including non-additive genetic effects did not improve the accuracy of prediction of breeding values for purebreds, but it changed the ranking of animals and selection decisions. Although PS and HD had large additive-by-additive epistasis effects, most of the traits related to heat stress in the crossbred population did not present relevant non-additive genetic effects.

Impact of inclusion non-additive effects on genome-wide association and variance’s components in Scottish black sheep

Context It’s well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection. Aim This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep. Methods This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used. Key results The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model. Conclusions The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16–24 weeks in Scottish Blackface lambs. Implications It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.

Gene-poor Y-chromosomes substantially impact male trait heritabilities and may help shape sexually dimorphic evolution.

How natural selection facilitates sexually dimorphic evolution despite a shared genome is unclear. The patrilineal inheritance of Y-chromosomes makes them an appealing solution. However, they have largely been dismissed due to their gene-poor, heterochromatic nature and because the additive genetic variation necessary for adaptive evolution is theoretically difficult to maintain. Further, previous empirical work has revealed mostly Y-linked sign epistatic variance segregating within populations, which can often impede adaptive evolution. To assess the evolutionary impact of Y-linked variation, we established replicate populations in Drosophila simulans containing multiple Y-chromosomes (YN populations) or a single Y-chromosome variant (Y1 populations) drawn from a single population. We estimated male and female heritabilities for several traits known to be influenced by Y-chromosomes, including the number of sternopleural bristles, abdominal bristles, sex comb teeth, and tibia length. A decrease in YN heritabilities compared with Y1 would be consistent with Y-chromosome variation being sign epistatic. A decrease in Y1 heritabilities would be consistent with Y-chromosome variation being additive, though additive-by-additive epistatic variation cannot be entirely dismissed. Female heritability estimates served as controls and were not expected to differ. We found male Y1 populations exhibited lower heritabilities for all traits except tibia length; consistent with Y-linked additivity (on average YN trait heritabilities were 25% greater than Y1). Female estimates showed no difference. These data suggest Y-chromosomes should play an important role in male trait evolution and may even influence sexually dimorphic evolution by shaping traits shared by both sexes.

Genomic dissection of repeatability considering additive and nonadditive genetic effects for semen production traits in beef and dairy bulls.

The low heritability and moderate repeatability of semen production traits in beef and dairy bulls suggest that nonadditive genetic effects, such as dominance and epistatic effects, play an important role in semen production and should therefore be considered in genetic improvement programs. In this study, the repeatability of semen production traits in Japanese Black bulls (JB) as beef bulls and Holstein bulls (HOL) as dairy bulls was evaluated by considering additive and nonadditive genetic effects using the Illumina BovineSNP50 BeadChip. We also evaluated the advantage of using more complete models that include nonadditive genetic effects by comparing the rank of genotyped animals and the phenotype prediction ability of each model. In total, 65,463 records for 615 genotyped JB and 48,653 records for 845 genotyped HOL were used to estimate additive and nonadditive (dominance and epistatic) variance components for semen volume (VOL), sperm concentration (CON), sperm motility (MOT), MOT after freeze-thawing (aMOT), and sperm number (NUM). In the model including both additive and nonadditive genetic effects, the broad-sense heritability (0.17 to 0.43) was more than twice as high as the narrow-sense heritability (0.04 to 0.11) for all traits and breeds, and the differences between the broad-sense heritability and repeatability were very small for VOL, NUM, and CON in both breeds. A large proportion of permanent environmental variance was explained by epistatic variance. The epistatic variance as a proportion of total phenotypic variance was 0.07 to 0.33 for all traits and breeds. In addition, heterozygosity showed significant positive relationships with NUM, MOT, and aMOT in JB and NUM in HOL, when the heterozygosity rate was included as a covariate. In a comparison of models, the inclusion of nonadditive genetic effects resulted in a re-ranking of the top genotyped bulls for the additive effects. Adjusting for nonadditive genetic effects could be expected to produce a more accurate breeding value, even if the models have similar fitting. However, including nonadditive genetic effects did not improve the ability of any model to predict phenotypic values for any trait or breed compared with the predictive ability of a model that includes only additive effects. Consequently, although nonadditive genetic effects, especially epistatic effects, play an important role in semen production traits, they do not improve prediction accuracy in beef and dairy bulls.

Inheritance studies through generation mean analysis for quantitative traits in soybean (Glycine max (L.) Merrill.)

Most of the economically important traits in soybean are quantitatively inherited. The generation mean analysis involving a five-parameter model was carried out in four crosses, viz., Pratap Soya- 2 × LP 5-2, Co 3 × LP 5-2, Co 3 × LP 5-1 and Co 3 × LP 13-1 to investigate additive, dominance and epistatic variance. Therefore, F1, F2 and F3 generations of the above four crosses were evaluated along with their respective parents to estimate the gene action for eleven quantitative traits through generation mean analysis, which provides information about all the gene interactions. The crosses Co 3 × LP 5-1 and Co 3 × LP 13-1 for plant height, Pratap Soya-2 × LP 5-2 for protein content and Co 3 × LP 5-2 for both oil content and seed yield per plant exhibited the adequacy of the additive dominance model. The remaining crosses exhibited epistatic interactions with all other traits. Hence simple recurrent selection can be followed to increase the frequency of desirable genes in the population and the resulted improved population can be used to develop superior lines with desirable genes by pedigree breeding. The crosses Co 3 × LP 5-1 and Pratap Soya - 2 × LP 5-2 were best for further selection programmes with regard to seed yield and quality improvement.

Significance of linkage disequilibrium and epistasis on genetic variances in noninbred and inbred populations

BackgroundThe influence of linkage disequilibrium (LD), epistasis, and inbreeding on genotypic variance continues to be an important area of investigation in genetics and evolution. Although the current knowledge about biological pathways and gene networks indicates that epistasis is important in determining quantitative traits, the empirical evidence for a range of species and traits is that the genotypic variance is most additive. This has been confirmed by some recent theoretical studies. However, because these investigations assumed linkage equilibrium, considered only additive effects, or used simplified assumptions for two- and higher-order epistatic effects, the objective of this investigation was to provide additional information about the impact of LD and epistasis on genetic variances in noninbred and inbred populations, using a simulated dataset.ResultsIn general, the most important component of the genotypic variance was additive variance. Because of positive LD values, after 10 generations of random crosses there was generally a decrease in all genetic variances and covariances, especially the nonepistatic variances. Thus, the epistatic variance/genotypic variance ratio is inversely proportional to the LD level. Increasing inbreeding increased the magnitude of the additive, additive x additive, additive x dominance, and dominance x additive variances, and decreased the dominance and dominance x dominance variances. Except for duplicate epistasis with 100% interacting genes, the epistatic variance/genotypic variance ratio was proportional to the inbreeding level. In general, the additive x additive variance was the most important component of the epistatic variance. Concerning the genetic covariances, in general, they showed lower magnitudes relative to the genetic variances and positive and negative signs. The epistatic variance/genotypic variance ratio was maximized under duplicate and dominant epistasis and minimized assuming recessive and complementary epistasis. Increasing the percentage of epistatic genes from 30 to 100% increased the epistatic variance/genotypic variance ratio by a rate of 1.3 to 12.6, especially in inbred populations. The epistatic variance/genotypic variance ratio was maximized in the noninbred and inbred populations with intermediate LD and an average allelic frequency of the dominant genes of 0.3 and in the noninbred and inbred populations with low LD and an average allelic frequency of 0.5.ConclusionsAdditive variance is in general the most important component of genotypic variance. LD and inbreeding have a significant effect on the magnitude of the genetic variances and covariances. In general, the additive x additive variance is the most important component of epistatic variance. The maximization of the epistatic variance/genotypic variance ratio depends on the LD level, degree of inbreeding, epistasis type, percentage of interacting genes, and average allelic frequency.

Dominance Can Increase Genetic Variance After a Population Bottleneck: A Synthesis of the Theoretical and Empirical Evidence.

Drastic reductions in population size, or population bottlenecks, can lead to a reduction in additive genetic variance and adaptive potential. Genetic variance for some quantitative genetic traits, however, can increase after a population reduction. Empirical evaluations of quantitative traits following experimental bottlenecks indicate that non-additive genetic effects, including both allelic dominance at a given locus and epistatic interactions among loci, may impact the additive variance contributed by alleles that ultimately influences phenotypic expression and fitness. The dramatic effects of bottlenecks on overall genetic diversity have been well studied, but relatively little is known about how dominance and demographic events like bottlenecks can impact additive genetic variance. Herein, we critically examine how the degree of dominance among alleles affects additive genetic variance after a bottleneck. We first review and synthesize studies that document the impact of empirical bottlenecks on dominance variance. We then extend earlier work by elaborating on 2 theoretical models that illustrate the relationship between dominance and the potential increase in additive genetic variance immediately following a bottleneck. Furthermore, we investigate the parameters that influence the maximum level of genetic variation (associated with adaptive potential) after a bottleneck, including the number of founding individuals. Finally, we validated our methods using forward-time population genetic simulations of loci with varying dominance and selection levels. The fate of non-additive genetic variation following bottlenecks could have important implications for conservation and management efforts in a wide variety of taxa, and our work should help contextualize future studies (e.g., epistatic variance) in population genomics.

Re-evaluation of NCGR Davis Ficus carica and palmata SSR profiles.

To date all public records of F. carica SSR profiles are from NCGR Davis. Prior studies of this data have not been received well because several of the stated relationships do not match what is observed in the field. Upon examination of the prior authors methods it is found that the 1979 Nei similarity measures are not valid distance metrics for the profiles thus invalidating their analysis of genetic distance. Further, the data are tensor in nature and it is shown here that "flattening the data" for use in a vector method will change the problem under study. Consequently the present analysis focuses on geometric, statistical, and biostatistical tensor-based methods–finding that only the latter produces results matching what is manually observed among the profiles. Combining this with historical breeding records and morphologic observations reveals that a modest portion of the profiled accessions are mislabeled–and also reveals the existence of previously undocumented close relations. Another area of concern in the prior studies is the statistical partitioning of the complete graph of distances to define clades. In the present analysis it is shown that genetic clades cannot be defined in this profile collection due to lack of cohesion in nearest neighbor components. It is also shown that it is presently intractable to significantly rectify gaps in the sample population by profile enrichment because the number of individuals in an entire population within the estimated profile distribution exceeds 1014. The profiles themselves are found to have very few occurrences of common values between the 15 loci and thus according to Fisher’s theory of epistatic variance no correlation to phenotype attributes is expected–a result verified by the original investigators. Therefore further discovery of appropriate markers is needed to fully capture geno- and pheno-type characteristics in F. carica and F. palmata SSR profiles.

Study of combining ability analysis on half diallel crosses in water stress of wheat (triticum aestivum L.)

Combining ability analysis was performed in a 10 x 10 half diallel cross of ten bread wheat genotypes for yield and its contributing traits. The experiment was conducted in 2013–2014 and 2014–2015 at Crop Research Centre, Sardar Vallabhbhai Patel University of Agriculture & Technology, Modipuram, Meerut, UP. Combining ability revealed that both general and specific combining ability variances were responsible for all the traits. Thus, additive and non-additive gene action was important in controlling the inheritance of all the traits. The variance due to gca was equal to that part of genetic variance which is due to additive and additive x additive effects, while variances due to sca was ascribed to non-additive components of variance that include dominance and epistatic variance. On the basis of overall gca effects, the best general combiners were WH-147, RAJ-3765, WH-730, NP-846, HUW-468, UP-2382 for maximum number of characters under normal and water stress condition. On the basis specific combining ability effects, five crosses viz., HUW-234 x RAJ-3765, HUW-234 x HUW468, WH-730 x C-306, C-306 x HD-2967 and DBW-17 x UP-2382 in normal condition, and WH-147 x WH-730, WH-147 x HD-2967, RAJ-3765 x HUW-468, RAJ-3765 x WH-730 and WH-730 x UP-2382 in water stress condition were found as good specific combiner for grain yield per plant.

Effect of additive, dominant and epistatic variances on breeding and deployment strategy in Norway spruce

Abstract Genetic variances are important parameters and have a great impact on the determination of optimal breeding strategies of tree species. A large clonal testing program was conducted to estimate additive, dominant and epistatic variances for the development of breeding and deployment strategies in Norway spruce (Picea abies (L.) Karst.). The analysis results of genetic variation for growth and wood properties in two clonal trials in central Sweden indicated that the important sources of total genetic variation were both additive and non-additive genetic variances. Additive genetic variation accounted for the majority of total genetic variation for diameter at breast height (DBH) and wood quality traits, whereas non-additive genetic variation was significant only for tree height at an early age. Predicted genetic gain was the highest for clonal deployment based on best tested (replicated) clones (4.7–65.3 per cent), followed by clonal deployment of the best individual trees from a full-sib family trial (3.5–57.7 per cent), and the deployment of seedlings generated by open-pollination (1.9–48.3 per cent).

Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris.

Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine (Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive × additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.

Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive (hSNP2), dominance (δSNP2) and additive-by-additive (ηSNP2) genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide a new theory to predict standard errors estimated using either least-squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits h¯SNP2=0.208). In contrast, the average estimate of δ¯SNP2 across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance η¯SNP2 across the traits was 0.055, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.

The Augmented Classical Twin Design: Incorporating Genome-Wide Identity by Descent Sharing Into Twin Studies in Order to Model Violations of the Equal Environments Assumption.

The Classical Twin Method (CTM) compares the similarity of monozygotic (MZ) twins with that of dizygotic (DZ) twins to make inferences about the relative importance of genes and environment in the etiology of individual differences. The design has been applied to thousands of traits across the biomedical, behavioral and social sciences and is arguably the most widely used natural experiment known to science. The fundamental assumption of the CTM is that trait relevant environmental covariation within MZ pairs is the same as that found within DZ pairs, so that zygosity differences in within-pair variance must be due to genetic factors uncontaminated by the environment. This equal environments assumption (EEA) has been, and still is hotly contested, and has been mentioned as a possible contributing factor to the missing heritability conundrum. In this manuscript, we introduce a new model for testing the EEA, which we call the Augmented Classical Twin Design which uses identity by descent (IBD) sharing between DZ twin pairs to estimate separate environmental variance components for MZ and DZ twin pairs, and provides a test of whether these are equal. We show through simulation that given large samples of DZ twin pairs, the model provides unbiased estimates of variance components and valid tests of the EEA under strong assumptions (e.g. no epistatic variance, IBD sharing in DZ twins estimated accurately etc.) which may not hold in reality. Sample sizes in excess of 50,000 DZ twin pairs with genome-wide genetic data are likely to be required in order to detect substantial violations of the EEA with moderate power. Consequently, we recommend that the Augmented Classical Twin Design only be applied to datasets with very large numbers of DZ twin pairs (> 50,000 DZ twin pairs), and given the strong assumptions relating to the absence of epistatic variance, appropriate caution be exercised regarding interpretation of the results.

The March of the Beetles: Epistatic Components Dominate Divergence in Dispersal Tendency in Tribolium castaneum.

The genetic underpinnings of traits are rarely simple. Most traits of interest are instead the product of multiple genes acting in concert to determine the phenotype. This is particularly true for behavioral traits, like dispersal. Our investigation focuses on the genetic architecture of dispersal tendency in the red flour beetle, Tribolium castaneum. We used artificial selection to generate lines with either high or low dispersal tendency. Our populations responded quickly in the first generations of selection and almost all replicates had higher dispersal tendency in males than in females. These selection lines were used to create a total of 6 additional lines: F1 and reciprocal F1, as well as 4 types of backcrosses. We estimated the composite genetic effects that contribute to divergence in dispersal tendency among lines using line cross-analysis. We found variation in the dispersal tendency of our lines was best explained by autosomal additive and 3 epistatic components. Our results indicate that dispersal tendency is heritable, but much of the divergence in our selection lines was due to epistatic effects. These results are consistent with other life-history traits that are predicted to maintain more epistatic variance than additive variance and highlight the potential for epistatic variation to act as an adaptive reserve that may become visible to selection when a population is subdivided.

Genome-Wide Epistatic Interaction Networks Affecting Feed Efficiency in Duroc and Landrace Pigs

Interactions among genomic loci have often been overlooked in genome-wide association studies, revealing the combinatorial effects of variants on phenotype or disease manifestation. Unexplained genetic variance, interactions among causal genes of small effects, and biological pathways could be identified using a network biology approach. The main objective of this study was to determine the genome-wide epistatic variants affecting feed efficiency traits [feed conversion ratio (FCR) and residual feed intake (RFI)] based on weighted interaction SNP hub (WISH-R) method. Herein, we detected highly interconnected epistatic SNP modules, pathways, and potential biomarkers for the FCR and RFI in Duroc and Landrace purebreds considering the whole population, and separately for low and high feed efficient groups. Highly interacting SNP modules in Duroc (1,247 SNPs) and Landrace (1,215 SNPs) across the population and for low feed efficient (Duroc—80 SNPs, Landrace—146 SNPs) and high feed efficient group (Duroc—198 SNPs, Landrace—232 SNPs) for FCR and RFI were identified. Gene and pathway analyses identified ABL1, MAP3K4, MAP3K5, SEMA6A, KITLG, and KAT2B from chromosomes 1, 2, 5, and 13 underlying ErbB, Ras, Rap1, thyroid hormone, axon guidance pathways in Duroc. GABBR2, GNA12, and PRKCG genes from chromosomes 1, 3, and 6 pointed towards thyroid hormone, cGMP-PKG and cAMP pathways in Landrace. From Duroc low feed efficient group, the TPK1 gene was found involved with thiamine metabolism, whereas PARD6G, DLG2, CRB1 were involved with the hippo signaling pathway in high feed efficient group. PLOD1 and SETD7 genes were involved with lysine degradation in low feed efficient group in Landrace, while high feed efficient group pointed to genes underpinning valine, leucine, isoleucine degradation, and fatty acid elongation. Some SNPs and genes identified are known for their association with feed efficiency, others are novel and potentially provide new avenues for further research. Further validation of epistatic SNPs and genes identified here in a larger cohort would help to establish a framework for modelling epistatic variance in future methods of genomic prediction, increasing the accuracy of estimated genetic merit for FE and helping the pig breeding industry.

Genomic dissection of maternal, additive and non-additive genetic effects for growth and carcass traits in Nile tilapia

BackgroundThe availability of both pedigree and genomic sources of information for animal breeding and genetics has created new challenges in understanding how they can be best used and interpreted. This study estimated genetic variance components based on genomic information and compared these to the variance components estimated from pedigree alone in a population generated to estimate non-additive genetic variance. Furthermore, the study examined the impact of the assumptions of Hardy–Weinberg equilibrium (HWE) on estimates of genetic variance components. For the first time, the magnitude of inbreeding depression for important commercial traits in Nile tilapia was estimated by using genomic data.ResultsThe study estimated the non-additive genetic variance in a Nile tilapia population of full-sib families and, when present, it was almost entirely represented by additive-by-additive epistatic variance, although in pedigree studies this non-additive variance is commonly assumed to arise from dominance. For body depth (BD) and body weight at harvest (BWH), the proportion of additive-by-additive epistatic to phenotypic variance was estimated to be 0.15 and 0.17 using genomic data (P < 0.05). In addition, with genomic data, the maternal variance (P < 0.05) for BD, BWH, body length (BL) and fillet weight (FW) explained approximately 10% of the phenotypic variances, which was comparable to pedigree-based estimates. The study also showed the detrimental effects of inbreeding on commercial traits of tilapia, which was estimated to reduce trait values by 1.1, 0.9, 0.4 and 0.3% per 1% increase in the individual homozygosity for FW, BWH, BD and BL, respectively. The presence of inbreeding depression but lack of dominance variance was consistent with an infinitesimal dominance model for the traits.ConclusionsThe benefit of including non-additive genetic effects for genetic evaluations in tilapia breeding schemes is not evident from these findings, but the observed inbreeding depression points to a role for reciprocal recurrent selection. Commercially, this conclusion will depend on the scheme’s operational costs and resources. The creation of maternal lines in Tilapia breeding schemes may be a possibility if the variation associated with maternal effects is heritable.

Analysis of non-additive genetic effects in Norway spruce

Genetic variance includes both additive and non-additive components, and the latter can be partitioned into dominance and epistatic variance. Additive genetic variance has been the main source of genetic improvement in most tree breeding programs and the genetic gain achieved has mostly been deployed through open-pollinated seed orchards. However, non-additive effects could be exploited in alternative deployment programs and provide additional genetic gain. Thus, knowledge of non-additive effects is essential for constructing robust genetic models and evaluating vegetative deployment systems, particularly for clonal forestry. In this study, we used data drawn from seven Norway spruce full-sib and three half-sib clonal trials to test and compare different models partitioning genetic effects into additive, dominance, and epistatic components for height and diameter. Simpler models provided more robust estimates of additive and non-additive effects, whereas more complex models provided more detail and insight in performance at single sites and G × E patterns, but with less reliable estimates. In this study, we cannot clearly distinguish the size and importance of dominance and epistatic effects. However, the total non-additive effects seem to be substantial, being around 80–90% of the additive, and were of the magnitude required to consider clonal forestry as an alternative to current deployment systems. Additive correlations largely followed the expected patterns based on seed zones and late spring frost incidence. Clonal correlations were generally lower than the additive implying that G × E interactions were stronger for non-additive effects.

A Low Resolution Epistasis Mapping Approach To Identify Chromosome Arm Interactions in Allohexaploid Wheat.

Epistasis is an important contributor to genetic variance. In inbred populations, pairwise epistasis is present as additive by additive interactions. Testing for epistasis presents a multiple testing problem as the pairwise search space for modest numbers of markers is large. Single markers do not necessarily track functional units of interacting chromatin as well as haplotype based methods do. To harness the power of multiple markers while minimizing the number of tests conducted, we present a low resolution test for epistatic interactions across whole chromosome arms. Epistasis covariance matrices were constructed from the additive covariances of individual chromosome arms. These covariances were subsequently used to estimate an epistatic variance parameter while correcting for background additive and epistatic effects. We find significant epistasis for 2% of the interactions tested for four agronomic traits in a winter wheat breeding population. Interactions across homeologous chromosome arms were identified, but were less abundant than other chromosome arm pair interactions. The homeologous chromosome arm pair 4BL/4DL showed a strong negative relationship between additive and interaction effects that may be indicative of functional redundancy. Several chromosome arms appeared to act as hubs in an interaction network, suggesting that they may contain important regulatory factors. The differential patterns of epistasis across different traits demonstrate that detection of epistatic interactions is robust when correcting for background additive and epistatic effects in the population. The low resolution epistasis mapping method presented here identifies important epistatic interactions with a limited number of statistical tests at the cost of low precision.

Estimation of additive and non-additive genetic variances of body weight in crossbreed populations of the Japanese quail

A major part of the growth performance in birds is the result of the combined effects of genes, including the general and specific combining ability that requires the design of an optimal mating system. The aim of this study was to fit the best model for each body weight trait from hatch to 45 days and the estimation of the variance components and the genetic parameters for the body weight traits of a crossbred population. This crossbred population was created by 4 strains of Japanese quail, including the Italian Speckled (A), Tuxedo (B), Pharaoh (C), Texas A&M (D), and the body weights of the different combinations were analyzed by 24 models including the direct genetic effect, the non-additive genetic effects including dominance and epistatic effects, the maternal permanent environmental and maternal genetic effects. The selection of the best fit model of each trait was performed based on the deviance information criteria. The variance components were estimated using a single-trait animal model analyzed with Gibbs sampling. At the early stage of bird growth, maternal genetic and maternal permanent environmental effects had a considerable contribution to the best model, but the contribution of these effects reduced with an increase in the bird's age and the additive variance contribution increased. Adding non-additive genetic effects (dominance and epistasis) to the models significantly reduced the variance of the error and the additive genetic variance estimated with high accuracy. The estimated heritability of body weight traits for the body weights of hatch, 5, 10, 15, 20, 25, 30, 35, 40, and 45 d were 0.316, 0.170, 0.251, 0.153, 0.132, 0.164, 0.290, 0.425, 0.476, and 0.362, respectively. The ratio of maternal genetic and maternal permanent environmental was considerable especially on early age body weight but the ratio of dominance and epistatic variances on each of the body weight traits was less than 4.5% of the total variance, but led to a more accurate estimates of the direct additive genetic.