Non-additive genetic variation in growth,carcass and fertility traits of beef cattle

Background

A better understanding of non-additive variance could lead to increased knowledge on the genetic control and physiology of quantitative traits, and to improved prediction of the genetic value and phenotype of individuals. Genome-wide panels of single nucleotide polymorphisms (SNPs) have been mainly used to map additive effects for quantitative traits, but they can also be used to investigate non-additive effects. We estimated dominance and epistatic effects of SNPs on various traits in beef cattle and the variance explained by dominance, and quantified the increase in accuracy of phenotype prediction by including dominance deviations in its estimation.

Methods

Genotype data (729 068 real or imputed SNPs) and phenotypes on up to 16 traits of 10 191 individuals from Bos taurus, Bos indicus and composite breeds were used. A genome-wide association study was performed by fitting the additive and dominance effects of single SNPs. The dominance variance was estimated by fitting a dominance relationship matrix constructed from the 729 068 SNPs. The accuracy of predicted phenotypic values was evaluated by best linear unbiased prediction using the additive and dominance relationship matrices. Epistatic interactions (additive × additive) were tested between each of the 28 SNPs that are known to have additive effects on multiple traits, and each of the other remaining 729 067 SNPs.

Results

The number of significant dominance effects was greater than expected by chance and most of them were in the direction that is presumed to increase fitness and in the opposite direction to inbreeding depression. Estimates of dominance variance explained by SNPs varied widely between traits, but had large standard errors. The median dominance variance across the 16 traits was equal to 5% of the phenotypic variance. Including a dominance deviation in the prediction did not significantly increase its accuracy for any of the phenotypes. The number of additive × additive epistatic effects that were statistically significant was greater than expected by chance.

Conclusions

Significant dominance and epistatic effects occur for growth, carcass and fertility traits in beef cattle but they are difficult to estimate precisely and including them in phenotype prediction does not increase its accuracy.     

Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.     

Genomic prediction of hybrid crops allows disentangling dominance and epistasis

We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted “residual genetic” line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme—e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.     

Epistasis in Measured Genotypes: Drosophila P-Element Insertions

Transposon tagging provides an opportunity to construct large numbers of strains of organisms that differ by single insertional mutations. By scoring the phenotypes of these ``measured genotypes,' powerful tests of effects of mutations on phenotypic expression have been performed. Here we extend this approach by constructing with simple crosses all possible two-locus genotypes for each of eight pairs of P-element insertions. Analysis of metabolic phenotypes (fat and glycogen contents, enzyme activities, total protein, and body weight) of the resulting nine genotypes provides direct estimates of additive, dominance, and epistatic effects of the mutations. Nested two-way analysis of variance identified significant epistatic effects in 27% of the tests (35/128 of the trait X P-element combinations). Posterior contrasts were performed to partition the epistatic variance into the four orthogonal components of COCKERHAM, and the data exhibit a tendency toward additive X dominance and dominance X dominance epistasis. Mutations in this study have epistatic effects on metabolic traits that are on the same order of magnitude as main (additive and dominance) effects. Measured genotypes have been used in other contexts to quantify epistatic effects on phenotypic expression, and these results are also briefly reviewed.     

Unraveling Additive from Nonadditive Effects Using Genomic Relationship Matrices

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.     

Something Old and Something New: Wedding Recombinant Inbred Lines with Traditional Line Cross Analysis Increases Power to Describe Gene Interactions

In this paper we present a novel approach to quantifying genetic architecture that combines recombinant inbred lines (RIL) with line cross analysis (LCA). LCA is a method of quantifying directional genetic effects (i.e. summed effects of all loci) that differentiate two parental lines. Directional genetic effects are thought to be critical components of genetic architecture for the long term response to selection and as a cause of inbreeding depression. LCA typically begins with two inbred parental lines that are crossed to produce several generations such as F1, F2, and backcrosses to each parent. When a RIL population (founded from the same P1 and P2 as was used to found the line cross population) is added to the LCA, the sampling variance of several nonadditive genetic effect estimates is greatly reduced. Specifically, estimates of directional dominance, additive x additive, and dominance x dominance epistatic effects are reduced by 92%, 94%, and 56% respectively. The RIL population can be simultaneously used for QTL identification, thus uncovering the effects of specific loci or genomic regions as elements of genetic architecture. LCA and QTL mapping with RIL provide two qualitatively different measures of genetic architecture with the potential to overcome weaknesses of each approach alone. This approach provides cross-validation of the estimates of additive and additive x additive effects, much smaller confidence intervals on dominance, additive x additive and dominance x dominance estimates, qualitatively different measures of genetic architecture, and the potential when used together to balance the weaknesses of LCA or RIL QTL analyses when used alone.     

Age trends in genetic parameters for growth and wood density in <Emphasis Type="Italic">Eucalyptus globulus</Emphasis>

Genetic parameters for stem diameter and wood density were compared at selection (4–5 years) and harvest (16–17 years) age in an open-pollinated progeny trial of Eucalyptus globulus in Tasmania (Australia). The study examined 514 families collected from 17 subraces of E. globulus. Wood density was assessed on a subsample of trees indirectly using pilodyn penetration at both ages and directly by core basic density at harvest age. Significant additive genetic variance and narrow-sense heritabilities ( h_\textop² h_{\text{op}}^2 ) were detected for all traits. Univariate and multivariate estimates of heritabilities were similar for each trait except harvest-age diameter. Comparable univariate estimates of selection- and harvest-age heritabilities for diameter masked changes in genetic architecture that occurred with stand development, whereby the loss of additive genetic variance through size-dependent mortality was countered by the accentuation of additive genetic differences among survivors with age. Regardless, the additive genetic (r _a) and subrace (r _s) correlations across ages were generally high for diameter (0.95 and 0.61, respectively) and pilodyn penetration (0.77 and 0.96), as were the correlations of harvest-age core basic density with selection- and harvest-age pilodyn (r _a −0.83, −0.88; r _s −0.96, −0.83). While r _s between diameter and pilodyn were close to zero at both ages, there was a significant change in r _a from adverse at selection age (0.25) to close to zero (−0.07) at harvest age. We argue that this change in the genetic correlation reflects a decoupling of the genetic association of growth and wood density with age. This result highlights the need to validate the use of selection-age genetic parameters for predicting harvest-age breeding values.     

Genetics of tassel branch number in maize and its implications for a selection program for small tassel size

Summary Tassel branch numbers of six crosses of maize (Zea mays L.) were analyzed to determine inheritance of this trait. Generation mean analyses were used to estimate genetic effects, and additive and nonadditive components of variance were calculated and evaluated for bias due to linkage. Both narrow-sense and broad-sense heritabilities were estimated. Additive genetic variance estimates were significant in five of the six crosses, whereas estimates of variance due to nonadditive components were significant in only three crosses. Additionally, estimates of additive variance components usually were larger than corresponding nonadditive components. There was no evidence for linkage bias in these estimates. Estimates of additive genetic effects were significant in four of six crosses, but significant dominance, additive × additive and additive × dominance effects also were detected. Additive, dominance, and epistatic gene action, therefore, all influenced the inheritance of tassel branch number, but additive gene action was most important. Both narrow-sense and broadsense heritability estimates were larger than those reported for other physiological traits of maize and corroborated conclusions concerning the importance of additive gene action inferred from analyses of genetic effects and variances. We concluded that selection for smalltasseled inbreds could be accomplished most easily through a mass-selection and/or pedigree-selection system. Production of a small-tasseled hybrid would require crossing of two small-tasseled inbreds. We proposed two genetic models to explain unexpected results obtained for two crosses. One model involved five interacting loci and the other employed two loci displaying only additive and additive × additive gene action.Journal Paper No. J-9231 of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa 50011. Project No. 2152     

Estimates of genetic variance in an F2 maize population

Maize (Zea mays L.) breeders have used several genetic-statistical models to study the inheritance of quantitative traits. These models provide information on the importance of additive, dominance, and epistatic genetic variance for a quantitative trait. Estimates of genetic variances are useful in understanding heterosis and determining the response to selection. The objectives of this study were to estimate additive and dominance genetic variances and the average level of dominance for an F2 population derived from the B73 x Mo17 hybrid and use weighted least squares to determine the importance of digenic epistatic variances relative to additive and dominance variances. Genetic variances were estimated using Design III and weighted least squares analyses. Both analyses determined that dominance variance was more important than additive variance for grain yield. For other traits, additive genetic variance was more important than dominance variance. The average level of dominance suggests either overdominant gene effects were present for grain yield or pseudo-overdominance because of linkage disequilibrium in the F2 population. Epistatic variances generally were not significantly different from zero and therefore were relatively less important than additive and dominance variances. For several traits estimates of additive by additive epistatic variance decreased estimates of additive genetic variance, but generally the decrease in additive genetic variance was not significant.     

Inheritance of zingiberene in Lycopersicon

Summary The simple mating designs provide unbiased estimates for genetic components of variance (additive genetic variance and dominance variance) under the assumption of no epistatic effect. There is empirical evidence, however, that suggests the existence of epistatic gene effects. The triallel and double cross mating designs permit the estimation of epistatic gene effects. A systematic and mathematical approach is suggested for the estimation of variance components based on the alternate model for triallel mating design.     

Gene actions at loci underlying several quantitative traits in two elite rice hybrids

To understand the gene activities controlling nine important agronomic quantitative traits in rice, we applied a North Carolina design 3 (NC III design) analysis to recombinant inbred lines (RILs) in highly heterotic inter- (IJ) and intra-subspecific (II) hybrids by performing the following tasks: (1) investigating the relative contribution of additive, dominant, and epistatic effects for performance traits by generation means analysis and variance component estimates; (2) detecting the number, genomic positions, and genetic effects of QTL for phenotypic traits; and (3) characterizing their mode of gene action. Under an F∞-metric, generation means analysis and variance components estimates revealed that epistatic effects prevailed for the majority of traits in the two hybrids. QTL analysis identified 48 and 66 main-effect QTL (M-QTL) for nine traits in IJ and II hybrids, respectively. In IJ hybrids, 20 QTL (41.7%) showed an additive effect of gene actions, 20 (41.7%) showed partial-to-complete dominance, and 8 (16.7%) showed overdominance. In II hybrids, 34 QTL (51.5%) exhibited additive effects, 14 (21.2%) partial-to-complete dominance, and 18 (27.3%) overdominance. There were 153 digenic interactions (E-QTL) in the IJ hybrid and 252 in the II hybrid. These results suggest that additive effects, dominance, overdominance, and particularly epistasis attribute to the genetic basis of the expression of traits in the two hybrids. Additionally, we determined that the genetic causes of phenotypic traits and their heterosis are different. In the plants we studied, the phenotypic traits investigated and their heterosis were conditioned by different M-QTL and E-QTL, respectively, and were mainly due to non-allelic interactions (epistasis).     

A Genome-Wide Association Study Reveals Dominance Effects on Number of Teats in Pigs

Dominance has been suggested as one of the genetic mechanisms explaining heterosis. However, using traditional quantitative genetic methods it is difficult to obtain accurate estimates of dominance effects. With the availability of dense SNP (Single Nucleotide Polymorphism) panels, we now have new opportunities for the detection and use of dominance at individual loci. Thus, the aim of this study was to detect additive and dominance effects on number of teats (NT), specifically to investigate the importance of dominance in a Landrace-based population of pigs. In total, 1,550 animals, genotyped for 32,911 SNPs, were used in single SNP analysis. SNPs with a significant genetic effect were tested for their mode of gene action being additive, dominant or a combination. In total, 21 SNPs were associated with NT, located in three regions with additive (SSC6, 7 and 12) and one region with dominant effects (SSC4). Estimates of additive effects ranged from 0.24 to 0.29 teats. The dominance effect of the QTL located on SSC4 was negative (−0.26 teats). The additive variance of the four QTLs together explained 7.37% of the total phenotypic variance. The dominance variance of the four QTLs together explained 1.82% of the total phenotypic variance, which corresponds to one-fourth of the variance explained by additive effects. The results suggest that dominance effects play a relevant role in the genetic architecture of NT. The QTL region on SSC7 contains the most promising candidate gene: VRTN. This gene has been suggested to be related to the number of vertebrae, a trait correlated with NT.     

Dominance Genetic Variance for Traits Under Directional Selection in Drosophila serrata

In contrast to our growing understanding of patterns of additive genetic variance in single- and multi-trait combinations, the relative contribution of nonadditive genetic variance, particularly dominance variance, to multivariate phenotypes is largely unknown. While mechanisms for the evolution of dominance genetic variance have been, and to some degree remain, subject to debate, the pervasiveness of dominance is widely recognized and may play a key role in several evolutionary processes. Theoretical and empirical evidence suggests that the contribution of dominance variance to phenotypic variance may increase with the correlation between a trait and fitness; however, direct tests of this hypothesis are few. Using a multigenerational breeding design in an unmanipulated population of Drosophila serrata, we estimated additive and dominance genetic covariance matrices for multivariate wing-shape phenotypes, together with a comprehensive measure of fitness, to determine whether there is an association between directional selection and dominance variance. Fitness, a trait unequivocally under directional selection, had no detectable additive genetic variance, but significant dominance genetic variance contributing 32% of the phenotypic variance. For single and multivariate morphological traits, however, no relationship was observed between trait–fitness correlations and dominance variance. A similar proportion of additive and dominance variance was found to contribute to phenotypic variance for single traits, and double the amount of additive compared to dominance variance was found for the multivariate trait combination under directional selection. These data suggest that for many fitness components a positive association between directional selection and dominance genetic variance may not be expected.     

Host genetic architecture in single and multiple infections

Hosts are often target to multiple simultaneous infections by genetically diverse parasite strains. The interaction among these strains and the interaction of each strain with the host was shown to have profound effects on the evolution of parasite traits. Host factors like genetic architecture of resistance have so far been largely neglected. To see whether genetic architecture differs between different kinds of infections we used joint scaling analysis to compare the genetic components of resistance in the red flour beetle Tribolium castaneum exposed to single and multiple strains of the microsporidian Nosema whitei. Our results indicate that additive, dominance and epistatic components were more important in single infections whereas maternal components play a decisive role in multiple infections. In detail, parameter estimates of additive, dominance and epistatic components correlated positively between single and multiple infections, whereas maternal components correlated negatively. These findings may suggest that specificity of host–parasite interactions are mediated by genetic and especially epistatic components whereas maternal effects constitute a more general form of resistance.     

Genetic analyses for certain plant and ear characters in pearl millet top-crosses

Summary Combining ability and the genetics of tiller number, days taken to flower and ear thickness were studied in top-cross progenies of pearl millet. General combining ability seemed to be more important for all the characters. The prevalence of epistatic variation, presumably of the type additive x additive, additive x dominance and dominance x dominance gene effects, with a non-significant contribution of additive and dominance components of genetic variance, was observed for tiller number. For days taken to flower, the importance of additive genetic variance was greater than that of the dominance component with directional dominance towards the recessive allele. However, for ear thickness, the existence of additive genetic variability together with the additive x additive type of genic interaction was suggested.An appreciable effect of epistasis on and ₁ components was observed for tiller number, whereas this effect was not so marked for other characters.The author is grateful to Dr. D. S. Athwal, formerly Professor and Head (now Assistant Director, The International Rice Research Institute, Los Banõs, Laguna, Philippines), Department of Plant Breeding, Punjab Agricultural University, Ludhiana, for providing the facilities.     

Local Adaptation and Genetics of Acid-Stress Tolerance in the Moor Frog, Rana arvalis

As potential to adapt to environmental stress can be essential for population persistence, knowledge on the genetic architecture of local adaptation is important for conservation genetics. We investigated the relative importance of additive genetic, dominance and maternal effects contributions to acid stress tolerance in two moor frog (Rana arvalis) populations originating from low and neutral pH habitats. Experiments with crosses obtained from artificial matings revealed that embryos from the acid origin population were more tolerant to low pH than embryos from the neutral origin population in embryonic survival rates, but not in terms of developmental stability, developmental and growth rates. Strong maternal effect and small additive genetic contributions to variation were detected in all traits in both populations. In general, dominance contributions to variance in different traits were of similar magnitude to the additive genetic effects, but dominance effects outweighed the additive genetic and maternal effects contributions to early growth in both populations. Furthermore, the expression of additive genetic variance was independent of pH treatment, suggesting little additive genetic variation in acid stress tolerance. The results suggest that although local genetic adaptation to acid stress has taken place, the current variation in acid stress tolerance in acidified populations may owe largely to non-genetic effects. However, low but significant heritabilities (h ² 0.07–0.22) in all traits – including viability itself – under a wide range of pH conditions suggests that environmental stress created by low pH is unlikely to lower moor frog populations' ability to respond to selection in the traits studied. Nevertheless, acid conditions could lower populations' ability to respond to selection in the long run through reduction in effective population size.     

Can dominance genetic variance be ignored in evolutionary quantitative genetic analyses of wild populations?

Accurately estimating genetic variance components is important for studying evolution in the wild. Empirical work on domesticated and wild outbred populations suggests that dominance genetic variance represents a substantial part of genetic variance, and theoretical work predicts that ignoring dominance can inflate estimates of additive genetic variance. Whether this issue is pervasive in natural systems is unknown, because we lack estimates of dominance variance in wild populations obtained in situ. Here, we estimate dominance and additive genetic variance, maternal variance, and other sources of nongenetic variance in eight traits measured in over 9000 wild nestlings linked through a genetically resolved pedigree. We find that dominance variance, when estimable, does not statistically differ from zero and represents a modest amount (2-36%) of genetic variance. Simulations show that (1) inferences of all variance components for an average trait are unbiased; (2) the power to detect dominance variance is low; (3) ignoring dominance can mildly inflate additive genetic variance and heritability estimates but such inflation becomes substantial when maternal effects are also ignored. These findings hence suggest that dominance is a small source of phenotypic variance in the wild and highlight the importance of proper model construction for accurately estimating evolutionary potential.     

二棱大麦熟期性状的遗传研究

以甘木二条等7个二棱大麦品种进行不完全双列杂交,对其亲本、F1和F2的抽穗期,灌浆期和成熟期三个性状以1992和1995年（播种年份）的两年资料,采用加性-显性-上位性（ADAA）模型进行遗传分析．遗传方差分量的比率估算表明,三个性状都存在上位性作用．除灌浆期外,其余二性状还受显性和加性效应的作用,并以加性为主．显性效应和加性效应与环境的互作均达显著水平,基因效应的预测值表明采用P3（黔浙1号）和P4（浙农大3号）较易获得早熟后代．     

On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope

Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or “breeding” values of individuals are generated by substitution effects, which involve both “biological” additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the “genotypic” value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts.