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.     

Epistasis between beneficial mutations and the phenotype-to-fitness Map for a ssDNA virus

Epistatic interactions between genes and individual mutations are major determinants of the evolutionary properties of genetic systems and have therefore been well documented, but few quantitative data exist on epistatic interactions between beneficial mutations, presumably because such mutations are so much rarer than deleterious ones. We explored epistasis for beneficial mutations by constructing genotypes with pairs of mutations that had been previously identified as beneficial to the ssDNA bacteriophage ID11 and by measuring the effects of these mutations alone and in combination. We constructed 18 of the 36 possible double mutants for the nine available beneficial mutations. We found that epistatic interactions between beneficial mutations were all antagonistic-the effects of the double mutations were less than the sums of the effects of their component single mutations. We found a number of cases of decompensatory interactions, an extreme form of antagonistic epistasis in which the second mutation is actually deleterious in the presence of the first. In the vast majority of cases, recombination uniting two beneficial mutations into the same genome would not be favored by selection, as the recombinant could not outcompete its constituent single mutations. In an attempt to understand these results, we developed a simple model in which the phenotypic effects of mutations are completely additive and epistatic interactions arise as a result of the form of the phenotype-to-fitness mapping. We found that a model with an intermediate phenotypic optimum and additive phenotypic effects provided a good explanation for our data and the observed patterns of epistatic interactions.     

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.     

Additive and non-additive genetic parameters from clonally replicated and seedling progenies of<Emphasis Type="Italic"> Eucalyptus globulus</Emphasis>

The first estimates of the importance of epistatic effects within Eucalyptus globulus were obtained from analysis of clonally replicated full-sib progeny tests grown in Portugal. Parents comprised diverse selections from the Portuguese landrace. Variance components were estimated for 4-year-old diameter growth and pilodyn penetration, an indirect measure of wood density, both key traits in the pulpwood breeding objective. The experimental components of variance were used to estimate heritabilities and proportions of the phenotypic variance due to dominance and epistasis. The additive variance was the only significant genetic component affecting either diameter or pilodyn. Estimates of the additive, dominance and epistatic effects accounted for 8–10%, 0–4% and 0.4% of the phenotypic variance in diameter, and for 11–17%, 0% and 5% of the phenotypic variance in pilodyn, respectively. A comparison of residual coefficients of variation within seedling and cloned progenies indicated that C effects within clones were not a serious source of random variability. Despite the test sites encompassing a diverse range of locations, no important genotype by environment interaction was detected. The results suggested that an improvement strategy combining both recurrent selection for additive genetic merit and clonal testing may be adequate for optimizing genetic gains from this genetic base.Communicated by O. Savolainen     

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.     

Environment Determines Epistatic Patterns for a ssDNA Virus

Despite the accumulation of substantial quantities of information about epistatic interactions among both deleterious and beneficial mutations in a wide array of experimental systems, neither consistent patterns nor causal explanations for these interactions have yet emerged. Furthermore, the effects of mutations depend on the environment in which they are characterized, implying that the environment may also influence epistatic interactions. Recent work with beneficial mutations for the single-stranded DNA bacteriophage ID11 demonstrated that interactions between pairs of mutations could be understood by means of a simple model that assumes that mutations have additive phenotypic effects and that epistasis arises through a nonlinear phenotype–fitness map with a single intermediate optimum. To determine whether such a model could also explain changes in epistatic patterns associated with changes in environment, we measured epistatic interactions for these same mutations under conditions for which we expected to find the wild-type ID11 at different distances from its phenotypic optimum by assaying fitnesses at three different temperatures: 33°, 37°, and 41°. Epistasis was present and negative under all conditions, but became more pronounced as temperature increased. We found that the additive-phenotypes model explained these patterns as changes in the parameters of the phenotype–fitness map, but that a model that additionally allows the phenotypes to vary across temperatures performed significantly better. Our results show that ostensibly complex patterns of fitness effects and epistasis across environments can be explained by assuming a simple structure for the genotype–phenotype relationship.     

Genome partitioning of genetic variation for height from 11,214 sibling pairs

Height has been used for more than a century as a model by which to understand quantitative genetic variation in humans. We report that the entire genome appears to contribute to its additive genetic variance. We used genotypes and phenotypes of 11,214 sibling pairs from three countries to partition additive genetic variance across the genome. Using genome scans to estimate the proportion of the genomes of each chromosome from siblings that were identical by descent, we estimated the heritability of height contributed by each of the 22 autosomes and the X chromosome. We show that additive genetic variance is spread across multiple chromosomes and that at least six chromosomes (i.e., 3, 4, 8, 15, 17, and 18) are responsible for the observed variation. Indeed, the data are not inconsistent with a uniform spread of trait loci throughout the genome. Our estimate of the variance explained by a chromosome is correlated with the number of times suggestive or significant linkage with height has been reported for that chromosome. Variance due to dominance was not significant but was difficult to assess because of the high sampling correlation between additive and dominance components. Results were consistent with the absence of any large between-chromosome epistatic effects. Notwithstanding the proposed architecture of complex traits that involves widespread gene-gene and gene-environment interactions, our results suggest that variation in height in humans can be explained by many loci distributed over all autosomes, with an additive mode of gene action.     

Beyond mean allelic effects: A locus at the major color gene MC1R associates also with differing levels of phenotypic and genetic (co)variance for coloration in barn owls

The mean phenotypic effects of a discovered variant help to predict major aspects of the evolution and inheritance of a phenotype. However, differences in the phenotypic variance associated to distinct genotypes are often overlooked despite being suggestive of processes that largely influence phenotypic evolution, such as interactions between the genotypes with the environment or the genetic background. We present empirical evidence for a mutation at the melanocortin‐1‐receptor gene, a major vertebrate coloration gene, affecting phenotypic variance in the barn owl, Tyto alba. The white MC1R allele, which associates with whiter plumage coloration, also associates with a pronounced phenotypic and additive genetic variance for distinct color traits. Contrarily, the rufous allele, associated with a rufous coloration, relates to a lower phenotypic and additive genetic variance, suggesting that this allele may be epistatic over other color loci. Variance differences between genotypes entailed differences in the strength of phenotypic and genetic associations between color traits, suggesting that differences in variance also alter the level of integration between traits. This study highlights that addressing variance differences of genotypes in wild populations provides interesting new insights into the evolutionary mechanisms and the genetic architecture underlying the phenotype.     

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.     

Improvement of Prediction Ability for Genomic Selection of Dairy Cattle by Including Dominance Effects

Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. The role of dominance in the Holstein and Jersey breeds was investigated for eight traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; somatic cell score; fat percent and protein percent. Additive and dominance variance components were estimated and then used to estimate additive and dominance effects of single nucleotide polymorphisms (SNPs). The predictive abilities of three models with both additive and dominance effects and a model with additive effects only were assessed using ten-fold cross-validation. One procedure estimated dominance values, and another estimated dominance deviations; calculation of the dominance relationship matrix was different for the two methods. The third approach enlarged the dataset by including cows with genotype probabilities derived using genotyped ancestors. For yield traits, dominance variance accounted for 5 and 7% of total variance for Holsteins and Jerseys, respectively; using dominance deviations resulted in smaller dominance and larger additive variance estimates. For non-yield traits, dominance variances were very small for both breeds. For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included. No corresponding gains in prediction ability were found for non-yield traits. Including cows with derived genotype probabilities from genotyped ancestors did not improve prediction accuracy. The largest additive effects were located on chromosome 14 near DGAT1 for yield traits for both breeds; those SNPs also showed the largest dominance effects for fat yield (both breeds) as well as for Holstein milk yield.     

Epialleles via DNA methylation: consequences for plant evolution

In plants, naturally occurring methylation of genes can affect the level of gene expression. Variation among individuals in the degree of methylation of a gene, termed epialleles, produces novel phenotypes that are heritable across generations. To date, ecologically important genes with methylated epialleles have been found to affect floral shape, vegetative and seed pigmentation, pathogen resistance and development in plants. Currently, the extent to which epiallelic variation is an important common contributor to phenotypic variation in natural plant populations and its fitness consequences are not known. Because epiallele phenotypes can have identical underlying DNA sequences, response to selection on these phenotypes is likely to differ from expectations based on traditional models of microevolution. Research is needed to understand the role of epialleles in natural plant populations. Recent advances in molecular genetic techniques could enable population biologists to screen for epiallelic variants within plant populations and disentangle epigenetic from more standard genetic sources of phenotypic variance, such as additive genetic variance, dominance variance, epistasis and maternal genetic effects.     

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.     

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．     

A comparison of the genetic basis of wing size divergence in three parallel body size clines of Drosophila melanogaster

Body size clines in Drosophila melanogaster have been documented in both Australia and South America, and may exist in Southern Africa. We crossed flies from the northern and southern ends of each of these clines to produce F(1), F(2), and first backcross generations. Our analysis of generation means for wing area and wing length produced estimates of the additive, dominance, epistatic, and maternal effects underlying divergence within each cline. For both females and males of all three clines, the generation means were adequately described by these parameters, indicating that linkage and higher order interactions did not contribute significantly to wing size divergence. Marked differences were apparent between the clines in the occurrence and magnitude of the significant genetic parameters. No cline was adequately described by a simple additive-dominance model, and significant epistatic and maternal effects occurred in most, but not all, of the clines. Generation variances were also analyzed. Only one cline was described sufficiently by a simple additive variance model, indicating significant epistatic, maternal, or linkage effects in the remaining two clines. The diversity in genetic architecture of the clines suggests that natural selection has produced similar phenotypic divergence by different combinations of gene action and interaction.     

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).     

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.     

Epistasis and Its Contribution to Genetic Variance Components

We present a new parameterization of physiological epistasis that allows the measurement of epistasis separate from its effects on the interaction (epistatic) genetic variance component. Epistasis is the deviation of two-locus genotypic values from the sum of the contributing single-locus genotypic values. This parameterization leads to statistical tests for epistasis given estimates of two-locus genotypic values such as can be obtained from quantitative trait locus studies. The contributions of epistasis to the additive, dominance and interaction genetic variances are specified. Epistasis can make substantial contributions to each of these variance components. This parameterization of epistasis allows general consideration of the role of epistasis in evolution by defining its contribution to the additive genetic variance.     

Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium

Background

Cockerham genetic models are commonly used in quantitative trait loci (QTL) analysis with a special feature of partitioning genotypic variances into various genetic variance components, while the F_∞ genetic models are widely used in genetic association studies. Over years, there have been some confusion about the relationship between these two type of models. A link between the additive, dominance and epistatic effects in an F_∞ model and the additive, dominance and epistatic variance components in a Cockerham model has not been well established, especially when there are multiple QTL in presence of epistasis and linkage disequilibrium (LD).

Results

In this paper, we further explore the differences and links between the F_∞ and Cockerham models. First, we show that the Cockerham type models are allelic based models with a special modification to correct a confounding problem. Several important moment functions, which are useful for partition of variance components in Cockerham models, are also derived. Next, we discuss properties of the F_∞ models in partition of genotypic variances. Its difference from that of the Cockerham models is addressed. Finally, for a two-locus biallelic QTL model with epistasis and LD between the loci, we present detailed formulas for calculation of the genetic variance components in terms of the additive, dominant and epistatic effects in an F_∞ model. A new way of linking the Cockerham and F_∞ model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F_∞ models are applied.

Conclusion

The Cockerham type models are allele-based models with a focus on partition of genotypic variances into various genetic variance components, which are contributed by allelic effects and their interactions. By contrast, the F_∞ regression models are genotype-based models focusing on modeling and testing of within-locus genotypic effects and locus-by-locus genotypic interactions. When there is no need to distinguish the paternal and maternal allelic effects, these two types of models are transferable. Transformation between an F_∞ model's parameters and its corresponding Cockerham model's parameters can be established through a relationship between their coding variables of genotypes. Genetic variance components in terms of the additive, dominance and epistatic genetic effects in an F_∞ model can then be calculated by translating formulas derived for the Cockerham models.

     

Simulation of quantitative characters from qualitatively acting genes

Summary The phenotypes associated with the nine genotypes in a quantitative genetic system consisting of two loci, each having two alleles can be described in terms of nine parameters, giving a system of nine linear equations. Populations with desired magnitudes and known nature of intra- and interlocus interactions are obtained by the use of this linear combination model. The total sums of squares for genotypes in these populations are partitioned into orthogonal components denoting additive and dominance effects of the two loci and the four types of nonallelic interactions between them. In most cases, the relative magnitudes of dominance and epistatic variances are found to be considerably smaller than the actual proportions of these genetic effects. Duplicate interaction produces larger epistatic variance than complementary type of gene interaction. At the higher levels of epistasis, dominant epistasis yields much larger epistatic variance than recessive epistasis. No epistatic variance is produced in the absence of epistatic effects. But, appreciable contributions of additive and dominance gene actions to the total genotypic variability are obtained even in the complete absence of these effects, if additive × dominance and dominance × dominance epistatic effects, respectively, are present. It is concluded that in elucidating the nature of gene action in simplified genetic systems, the estimates of first degree parameters obtained from the linear combination model are more useful than the orthogonal components of genotypic sum of squares.

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Zusammenfassung Die in einem quantitativ-genetischen System mit je 2 Allelen an 2 Loci möglichen 9 Phänotypen, die mit den entsprechenden Genotypen assoziiert sind, können durch einen Satz von 9 linearen Gleichungen beschrieben werden. Mit Hilfe dieses Modells der linearen Kombination wurden Populationen mit willkürlich gewählter Dimension und Art der Interaktion innerhalb der und zwischen den Loci konstruiert. Die Gesamtsummen der Abweichungsquadrate für die Genotypen derartiger Populationen werden in orthogonale Komponenten zerlegt, die den additiven und den Dominanz-Effekten bzw. den vier Arten der nichtallelen Interaktion der beiden Loci zugeschrieben werden können. In der Mehrzahl der Fälle sind die relativen Größenordnungen der Dominanz- und Epistasie-Varianzen wesentlich kleiner als die tatsächlichen Anteile dieser Effekte. Eine gegenseitige Vertretbarkeit nichtalleler Gene (duplicate gene action, 15:1-Spaltung) führt zu einer größeren Epistasievarianz als komplementäre Genwirkung (9:7-Spaltung). Bei stark ausgeprägter Epistasie führt die sog. dominante Epistasie (12:3:1-Spaltung) zu einer wesentlich größeren Epistasievarianz als die rezessive Epistasie (9:3:4-Spaltung). In Abwesenheit epistatischer Effekte wird keine Epistasievarianz beobachtet. Jedoch werden bemerkenswerte Beiträge additiver und dominanter Genwirkungen zur genotypischen Gesamtvariabilität auch bei völliger Abwesenheit derartiger Wirkungen beobachtet, wenn Interaktionen des Typs additiv × dominant bzw. dominant × dominant vorliegen. Hieraus wird geschlossen, daß die Aufklärung der Art der Genwirkung in einfachen genetischen Systemen gezeigt hat, daß die Schätzwerte der Parameter 1. Grades, die aus dem zitierten Modell mit linearer Kombination erhalten werden können, brauchbarer sind als die orthogonalen Kombinationen der genotypischen Summe der Abweichungsquadrate.

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The investigation was supported by the grant number A6221 of the National Research Council of Canada.