作物品种间杂种优势遗传分析的新方法

本文提出了分析双列杂交试验资料的两个遗传模型。第一个模型包括加性、显性和母体效应;第二个模型只包括简单的加性和显性效应。还介绍了分析杂种优势、估算遗传方差分量以及预测遗传效应值的相应统计分析方法。用所介绍的遗传模型和分析方法以及常用的Griffing配合力分析方法,分析了棉花6个品种双列杂交的产量性状,并进一步比较了不同方法的分析结果。采用本文所介绍的遗传模型和分析方法,可以克服用Griffing的配合力模型及其方法分析杂种优势和配合力遗传表现所存在的局限性。     

Methods for the study of cytoplasmic effects on quantitative traits

Summary The methods used to study cytoplasmic effects in quantitative traits often do not measure quantitative genetic parameters, while those that do are either complicated or do not take into account situations where the expression of cytoplasmic effects does not persist, but decreases in advanced generations. We present two simple models that take cytoplasmic effects and the quantitative genetic parameters into account. One of the models (A) is for cases where cytoplasmic effects remain constant through successive generations, and the second model (B) is for traits where cytoplasm-genotype interactions are present. This model also takes into account the decreasing persistence of cytoplasmic effects with advancing generations, which is often reported in the literature.     

数量性状发育遗传模型及其分析方法的研究进展

发育遗传模型是同时反映性状遗传和发育本质、提供影响遗传变异及调整发育进程的有关因素的信息的模型。建立在群体遗传学基础上的直接效应模型适用于单一基因控制的简单性状。渐成模型将遗传变异分解成直接分量和渐成分量(母体效应和互作效应),能更好地反映有机体遗传和发育的生物学机制。生长轨迹模型有效地综合了复杂性状各分量的发育动态,可获得连续的、综合的、详细的、动态的发育信息。条件遗传分析方法不仅可以估算特定时间段的净效应,且可将净效应分解为不同遗传分量,了解各效应分量的相对贡献。 Abstract：Developmental genetic models and analysis methods for quantitative traits are presented.Developmental genetic models should reflect the genetic and developmental essence,and provide the information of the factors influencing the genetic variation and the developmental process.Direct effect models,which based on the population genetics,may be suitable to analyze simple traits with single gene.Epigenetic models can decompose the whole genetic variation into direct and epigenetic components (maternal effects and epigenetic interaction effects),so that biological mechanism can be better understood.Growth trace models effectively synthesize the developmental dynamics of components of complex traits.With them,continuous,compositive,detailed,and dynamic information of development is available.Conditional analysis method can not only estimate the net effects in a specific time interval,but also depose them into genetic components and help to appreciate the contributions of different effects.     

Maternal and genetic contributions to geographical variation in Rana temporaria larval life-history traits

The relative importance of genetic, environmental, and maternal effects as determinants of geographical variation in vertebrate life-histories has not often been explored. We examined the role of genetic and maternal effects as determinants of population divergence in survival and three important larval life-history traits (growth rate, age, and size at metamorphosis) using reciprocal crosses between two latitudinally separated populations of the common frog ( Rana temporaria Linnaeus). Genetic effects were important in all three traits as indicated by the significant effect of male origin, but there was also evidence for nonadditive genetic contributions on metamorphic size and growth rate. Likewise, maternal effect contributions to population divergence were large, partially environment dependent, and apparently acting primarily through egg size in two of three traits. These results suggest that both genetic and maternal effects are important determinants of geographical variation in amphibian life-histories, and that much of the differentiation resulting from maternal effects is mediated through variation in egg size. © 2002 The Linnean Society of London, Biological Journal of the Linnean Society , 2002, 76 , 61–70.     

The role of maternal and paternal effects in the evolution of parental quality by sexual selection

Genetic models of maternal effects and models of mate choice have focused on the evolutionary effects of variation in parental quality. There have been, however, few attempts to combine these into a single model for the evolution of sexually selected traits. We present a quantitative genetic model that considers how male and female parental quality (together or separately) affect the expression of a sexually selected offspring trait. We allow female choice of males based on this parentally affected trait and examine the evolution of mate choice, parental quality and the indicator trait. Our model reveals a number of consequences of maternal and paternal effects. (1) The force of sexual selection owing to adaptive mate choice can displace parental quality from its natural selection optimum. (2) The force of sexual selection can displace female parental quality from its natural selection optimum even when nonadaptive mate choice occurs (e.g. runaway sexual selection), because females of higher parental quality produce more attractive sons and these sons counterbalance the loss in fitness owing to over-investment in each offspring. (3) Maternal and paternal effects can provide a source of genetic variation for offspring traits, allowing evolution by sexual selection even when those traits do not show direct genetic variation (i.e. are not heritable). (4) The correlation between paternal investment and the offspring trait influenced by the parental effects can result in adaptive mate choice and lead to the elaboration of both female preference and the male sexually selected trait. When parental effects exist, sexual selection can drive the evolution of parental quality when investment increases the attractiveness of offspring, leading to the elaboration of indicator traits and higher than expected levels of parental investment.     

Estimating quantitative genetic parameters in wild populations: a comparison of pedigree and genomic approaches

The estimation of quantitative genetic parameters in wild populations is generally limited by the accuracy and completeness of the available pedigree information. Using relatedness at genomewide markers can potentially remove this limitation and lead to less biased and more precise estimates. We estimated heritability, maternal genetic effects and genetic correlations for body size traits in an unmanaged long‐term study population of Soay sheep on St Kilda using three increasingly complete and accurate estimates of relatedness: (i) Pedigree 1, using observation‐derived maternal links and microsatellite‐derived paternal links; (ii) Pedigree 2, using SNP‐derived assignment of both maternity and paternity; and (iii) whole‐genome relatedness at 37 037 autosomal SNPs. In initial analyses, heritability estimates were strikingly similar for all three methods, while standard errors were systematically lower in analyses based on Pedigree 2 and genomic relatedness. Genetic correlations were generally strong, differed little between the three estimates of relatedness and the standard errors declined only very slightly with improved relatedness information. When partitioning maternal effects into separate genetic and environmental components, maternal genetic effects found in juvenile traits increased substantially across the three relatedness estimates. Heritability declined compared to parallel models where only a maternal environment effect was fitted, suggesting that maternal genetic effects are confounded with direct genetic effects and that more accurate estimates of relatedness were better able to separate maternal genetic effects from direct genetic effects. We found that the heritability captured by SNP markers asymptoted at about half the SNPs available, suggesting that denser marker panels are not necessarily required for precise and unbiased heritability estimates. Finally, we present guidelines for the use of genomic relatedness in future quantitative genetics studies in natural populations.     

How to separate genetic and environmental causes of similarity between relatives

Related individuals often have similar phenotypes, but this similarity may be due to the effects of shared environments as much as to the effects of shared genes. We consider here alternative approaches to separating the relative contributions of these two sources to phenotypic covariances, comparing experimental approaches such as cross-fostering, traditional statistical techniques and more complex statistical models, specifically the 'animal model'. Using both simulation studies and empirical data from wild populations, we demonstrate the ability of the animal model to reduce bias due to shared environment effects such as maternal or brood effects, especially where pedigrees contain multiple generations and immigration rates are low. However, where common environment effects are strong, a combination of both cross-fostering and an animal model provides the best way to avoid bias. We illustrate ways of partitioning phenotypic variance into components of additive genetic, maternal genetic, maternal environment, common environment, permanent environment and temporal effects, but also show how substantial confounding between these different effects may occur. Whilst the flexibility of the mixed model approach is extremely useful for incorporating the spatial, temporal and social heterogeneity typical of natural populations, the advantages will inevitably be restricted by the quality of pedigree information and care needs to be taken in specifying models that are appropriate to the data.     

Maternal genetic effects on adaptive divergence between anadromous and resident brook charr during early life history

The importance of directional selection relative to neutral evolution may be determined by comparing quantitative genetic variation in phenotype (Q(ST)) to variation at neutral molecular markers (F(ST)). Quantitative divergence between salmonid life history types is often considerable, but ontogenetic changes in the significance of major sources of genetic variance during post-hatch development suggest that selective differentiation varies by developmental stage. In this study, we tested the hypothesis that maternal genetic differentiation between anadromous and resident brook charr (Salvelinus fontinalis Mitchill) populations for early quantitative traits (embryonic size/growth, survival, egg number and developmental time) would be greater than neutral genetic differentiation, but that the maternal genetic basis for differentiation would be higher for pre-resorption traits than post-resorption traits. Quantitative genetic divergence between anadromous (seawater migratory) and resident Laval River (Québec) brook charr based on maternal genetic variance was high (Q(ST) > 0.4) for embryonic length, yolk sac volume, embryonic growth rate and time to first response to feeding relative to neutral genetic differentiation [F(ST) = 0.153 (0.071-0.214)], with anadromous females having positive genetic coefficients for all of the above characters. However, Q(ST) was essentially zero for all traits post-resorption of the yolk sac. Our results indicate that the observed divergence between resident and anadromous brook charr has been driven by directional selection, and may therefore be adaptive. Moreover, they provide among the first evidence that the relative importance of selective differentiation may be highly context-specific, and varies by genetic contributions to phenotype by parental sex at specific points in offspring ontogeny. This in turn suggests that interpretations of Q(ST)-F(ST) comparisons may be improved by considering the structure of quantitative genetic architecture by age category and the sex of the parent used in estimation.     

Shifting patterns in genetic control at the embryo-alevin boundary in brook charr

Maternal inputs to offspring early in development are initially high but the process of development suggests that ontogenetic shifts in the importance of maternal genetic variation relative to other sources should occur. We investigated additive genetic variance and covariance for direct (animal), sire, and maternal effects on embryonic length (EL), yolk sac volume (YSV), and alevin (after yolk sac resorption) length (AL) for 460 embryonic and 460 alevin brook charr (Salvelinus fontinalis) in 23 half-sib families (12 sires, 23 dams). There were no additive genetic effects of sires or individual animals on their own phenotype using sire-dam and maternal-animal models for YSV or EL (h(a)2 < 0.05). However, at the alevin stage we detected low but significant heritability for AL (h(a)2 = 0.14 +/- 0.11). Conversely, maternal genetic effects were high for both embryonic traits (h(EL)2 = 0.61 +/- 0.05; h(YSU)2 = 0.57 +/- 0.06) but faded rapidly for postresorption length (h(AL)2 = 0.18 +/- 0.04). Maternal effects in the sire-dam model corresponded highly with those in the animal-dam model. We did not detect significant genetic covariance between progeny and dams for preresorption traits or between sires and dams for any trait. However, following resorption of the yolk sac, the genetic value of dams for AL was negatively correlated with that of individual progeny (r(m,a) = -0.38 +/- 0.13), suggesting trade-offs and/or stabilizing selection between maternal and animal genetic trait value. This finding was supported by models of dam fecundity on offspring length and dam weight in phenotypic space. Heritability estimates using simple regression of embryo phenotype on adult parental phenotype produced upwardly biased estimates of genetic variance (h2 > 1.0). We propose that development through the embryo-alevin boundary may be a major point in salmonids for ontogenetic changes in the genetic architecture of embryo size from maternal genetic effects to those of the individual organism, and that maternal-offspring conflicts in resource allocation related to size may be partially indicated by negative genetic covariance.     

母体遗传效应对青海细毛羊生产性能遗传参数估计的影响

为了研究母体遗传效应对青海细毛羊生长性状、产毛性状的影响,文章采用平均信息最大约束似然法应用不同混合动物模型估计青海细毛羊生产性状的遗传参数,并采用似然比检验对不同模型进行比较分析。各模型中均包括固定效应、个体直接加性遗传效应、残差效应;随机效应为:个体永久环境效应、母体遗传效应、母体永久环境效应。不同模型对随机效应作了不同考虑:模型1不考虑个体永久环境效应、母体遗传效应、母体永久环境效应;模型2考虑母体永久环境效应;模型3考虑母体遗传效应;模型4考虑母体遗传效应和母体永久环境效应;模型5考虑个体永久环境效应和母体遗传效应;模型6考虑个体永久环境效应、母体遗传效应、母体永久环境效应。各模型估计的初生重遗传力为:0.1896~0.3781;断奶重遗传力为:0.2537~0.2890;周岁重遗传力范围:0.2244~0.3225;成年羊体重遗传力范围:0.2205~0.3983;产毛量遗传力为:0.1218~0.1490;羊毛细度遗传力为:0.0983~0.4802;羊毛长度遗传力为:0.1170~0.1311。与模型1相比,模型3对于初生重、断奶重差异显著(P<0.01),对于周岁重、成年羊体重各模型与模型1的似然比检验差异不显著(P>0.05);与模型6相比,模型4、5对于羊毛细度差异显著(P<0.01),模型4对羊毛长度差异显著(P<0.05),对于产毛量各模型与模型6似然比检验差异不显著(P>0.05)。生长性状中初生重、断奶重受母体遗传效应影响显著,周岁重、成年羊体重受母体遗传效应影响不显著;产毛性状中羊毛细度、长度受母体遗传效应影响显著,产毛量受母体遗传效应影响较弱。     

母体遗传效应对青海细毛羊生产性能遗传参数估计的影响

为了研究母体遗传效应对青海细毛羊生长性状、产毛性状的影响, 文章采用平均信息最大约束似然法应用不同混合动物模型估计青海细毛羊生产性状的遗传参数, 并采用似然比检验对不同模型进行比较分析。各模型中均包括固定效应、个体直接加性遗传效应、残差效应; 随机效应为：个体永久环境效应、母体遗传效应、母体永久环境效应。不同模型对随机效应作了不同考虑：模型1不考虑个体永久环境效应、母体遗传效应、母体永久环境效应; 模型2考虑母体永久环境效应; 模型3考虑母体遗传效应; 模型4考虑母体遗传效应和母体永久环境效应; 模型5考虑个体永久环境效应和母体遗传效应; 模型6考虑个体永久环境效应、母体遗传效应、母体永久环境效应。各模型估计的初生重遗传力为：0.1896~0.3781; 断奶重遗传力为：0.2537~0.2890; 周岁重遗传力范围：0.2244~0.3225; 成年羊体重遗传力范围：0.2205~0.3983; 产毛量遗传力为：0.1218~0.1490; 羊毛细度遗传力为：0.0983~0.4802; 羊毛长度遗传力为：0.1170~0.1311。与模型1相比, 模型3对于初生重、断奶重差异显著(P<0.01), 对于周岁重、成年羊体重各模型与模型1的似然比检验差异不显著(P>0.05); 与模型6相比, 模型4、5对于羊毛细度差异显著(P<0.01), 模型4对羊毛长度差异显著(P<0.05), 对于产毛量各模型与模型6似然比检验差异不显著(P>0.05)。生长性状中初生重、断奶重受母体遗传效应影响显著, 周岁重、成年羊体重受母体遗传效应影响不显著; 产毛性状中羊毛细度、长度受母体遗传效应影响显著, 产毛量受母体遗传效应影响较弱。     

Shared spatial effects on quantitative genetic parameters: accounting for spatial autocorrelation and home range overlap reduces estimates of heritability in wild red deer

Social structure, limited dispersal, and spatial heterogeneity in resources are ubiquitous in wild vertebrate populations. As a result, relatives share environments as well as genes, and environmental and genetic sources of similarity between individuals are potentially confounded. Quantitative genetic studies in the wild therefore typically account for easily captured shared environmental effects (e.g., parent, nest, or region). Fine-scale spatial effects are likely to be just as important in wild vertebrates, but have been largely ignored. We used data from wild red deer to build "animal models" to estimate additive genetic variance and heritability in four female traits (spring and rut home range size, offspring birth weight, and lifetime breeding success). We then, separately, incorporated spatial autocorrelation and a matrix of home range overlap into these models to estimate the effect of location or shared habitat on phenotypic variation. These terms explained a substantial amount of variation in all traits and their inclusion resulted in reductions in heritability estimates, up to an order of magnitude up for home range size. Our results highlight the potential of multiple covariance matrices to dissect environmental, social, and genetic contributions to phenotypic variation, and the importance of considering fine-scale spatial processes in quantitative genetic studies.     

The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

Appropriate selection of parents for the development of mapping populations is pivotal to maximizing the power of quantitative trait loci detection. Trait genotypic variation within a family is indicative of the family's informativeness for genetic studies. Accurate prediction of the most useful parental combinations within a species would help guide quantitative genetics studies. We tested the reliability of genotypic and phenotypic distance estimators between pairs of maize inbred lines to predict genotypic variation for quantitative traits within families derived from biparental crosses. We developed 25 families composed of ~200 random recombinant inbred lines each from crosses between a common reference parent inbred, B73, and 25 diverse maize inbreds. Parents and families were evaluated for 19 quantitative traits across up to 11 environments. Genetic distances (GDs) among parents were estimated with 44 simple sequence repeat and 2303 single-nucleotide polymorphism markers. GDs among parents had no predictive value for progeny variation, which is most likely due to the choice of neutral markers. In contrast, we observed for about half of the traits measured a positive correlation between phenotypic parental distances and within-family genetic variance estimates. Consequently, the choice of promising segregating populations can be based on selecting phenotypically diverse parents. These results are congruent with models of genetic architecture that posit numerous genes affecting quantitative traits, each segregating for allelic series, with dispersal of allelic effects across diverse genetic material. This architecture, common to many quantitative traits in maize, limits the predictive value of parental genotypic or phenotypic values on progeny variance.     

PATERNAL CARE: DIRECT AND INDIRECT GENETIC EFFECTS OF FATHERS ON OFFSPRING PERFORMANCE

Knowledge of how genetic effects arising from parental care influence the evolution of offspring traits comes almost exclusively from studies of maternal care. However, males provide care in some taxa, and often this care differs from females in quality or quantity. If variation in paternal care is genetically based then, like maternal care and maternal effects, paternal effects may have important consequences for the evolution of offspring traits via indirect genetic effects (IGEs). IGEs and direct–indirect genetic covariances associated with parental care can contribute substantially to total heritability and influence predictions about how traits respond to selection. It is unknown, however, if the magnitude and sign of parental effects arising from fathers are the same as those arising from mothers. We used a reciprocal cross‐fostering experiment to quantify environmental and genetic effects of paternal care on offspring performance in the burying beetle, Nicrophorus vespilloides. We found that IGEs were substantial and direct–indirect genetic covariances were negative. Combined, these patterns led to low total heritabilities for offspring performance traits. Thus, under paternal care, offspring performance traits are unlikely to evolve in response to selection, and variation in these traits will be maintained in the population despite potentially strong selection on these traits. These patterns are similar to those generated by maternal care, indicating that the genetic effects of care on offspring performance are independent of the caregiver's sex.     

Fixed‐effect variance and the estimation of repeatabilities and heritabilities: issues and solutions

Linear mixed‐effects models are frequently used for estimating quantitative genetic parameters, including the heritability, as well as the repeatability, of traits. Heritability acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change, whereas repeatability informs us about the individual consistency of phenotypic traits. As quantities of biological interest, it is important that the denominator, the phenotypic variance in both cases, reflects the amount of phenotypic variance in the relevant ecological setting. The current practice of quantifying heritabilities and repeatabilities from mixed‐effects models frequently deprives their denominator of variance explained by fixed effects (often leading to upward bias of heritabilities and repeatabilities), and it has been suggested to omit fixed effects when estimating heritabilities in particular. We advocate an alternative option of fitting models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of the phenotypic variance. The approach is easily implemented and allows optimizing the estimation of phenotypic variance, for example by the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We address the estimation and interpretation of heritabilities in situations in which potential covariates are themselves heritable traits of the organism. Furthermore, we discuss complications that arise in generalized and nonlinear mixed models with fixed effects. In these cases, the variance parameters on the data scale depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.     

QUANTITATIVE GENETIC VARIANCE MAINTAINED BY FLUCTUATING SELECTION WITH OVERLAPPING GENERATIONS: VARIANCE COMPONENTS AND COVARIANCES

The quantitative genetic variance-covariance that can be maintained in a random environment is studied, assuming overlapping generations and Gaussian stabilizing selection with a fluctuating optimum. The phenotype of an individual is assumed to be determined by additive contributions from each locus on paternal and maternal gametes (i.e., no epistasis and no dominance). Recurrent mutation is ignored, but linkage between loci is arbitrary. The genotype distribution in the evolutionarily stable population is generically discrete: only a finite number of polymorphic alleles with distinctly different effects are maintained, even though we allow a continuum of alleles with arbitrary phenotypic contributions to invade. Fluctuating selection maintains nonzero genetic variance in the evolutionarily stable population if the environmental heterogeneity is larger than a certain threshold. Explicit asymptotic expressions for the standing variance-covariance components are derived for the population near the threshold, or for large generational overlap, as a function of environmental variability and genetic parameters (i.e., number of loci, recombination rate, etc.), using the fact that the genotype distribution is discrete. Above the threshold, the population maintains considerable genetic variance in the form of positive linkage disequilibrium and positive gamete covariance (Hardy-Weinberg disequilibrium) as well as allelic variance. The relative proportion of these disequilibrium variances in the total genetic variance increases with the environmental variability.     

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.     

Runaway sexual selection without genetic correlations: social environments and flexible mate choice initiate and enhance the fisher process

Female mating preferences are often flexible, reflecting the social environment in which they are expressed. Associated indirect genetic effects (IGEs) can affect the rate and direction of evolutionary change, but sexual selection models do not capture these dynamics. We incorporate IGEs into quantitative genetic models to explore how variation in social environments and mate choice flexibility influence Fisherian sexual selection. The importance of IGEs is that runaway sexual selection can occur in the absence of a genetic correlation between male traits and female preferences. Social influences can facilitate the initiation of the runaway process and increase the rate of trait elaboration. Incorporating costs to choice do not alter the main findings. Our model provides testable predictions: (1) genetic covariances between male traits and female preferences may not exist, (2) social flexibility in female choice will be common in populations experiencing strong sexual selection, (3) variation in social environments should be associated with rapid sexual trait divergence, and (4) secondary sexual traits will be more elaborate than previously predicted. Allowing feedback from the social environment resolves discrepancies between theoretical predictions and empirical data, such as why indirect selection on female preferences, theoretically weak, might be sufficient for preferences to become elaborated.     

Good genes, genetic compatibility and the evolution of polyandry: use of the diallel cross to address competing hypotheses

Genetic benefits can enhance the fitness of polyandrous females through the high intrinsic genetic quality of females' mates or through the interaction between female and male genes. I used a full diallel cross, a quantitative genetics design that involves all possible crosses among a set of genetically homogeneous lines, to determine the mechanism through which polyandrous female decorated crickets (Gryllodes sigillatus) obtain genetic benefits. I measured several traits related to fitness and partitioned the phenotypic variance into components representing the contribution of additive genetic variance ('good genes'), nonadditive genetic variance (genetic compatibility), as well as maternal and paternal effects. The results reveal a significant variance attributable to both nonadditive and additive sources in the measured traits, and their influence depended on which trait was considered. The lack of congruence in sources of phenotypic variance among these fitness-related traits suggests that the evolution and maintenance of polyandry are unlikely to have resulted from one selective influence, but rather are the result of the collective effects of a number of factors.     

Are we underestimating the genetic variances of dimorphic traits?

Populations often contain discrete classes or morphs (e.g., sexual dimorphisms, wing dimorphisms, trophic dimorphisms) characterized by distinct patterns of trait expression. In quantitative genetic analyses, the different morphs can be considered as different environments within which traits are expressed. Genetic variances and covariances can then be estimated independently for each morph or in a combined analysis. In the latter case, morphs can be considered as separate environments in a bivariate analysis or entered as fixed effects in a univariate analysis. Although a common approach, we demonstrate that the latter produces downwardly biased estimates of additive genetic variance and heritability unless the quantitative genetic architecture of the traits concerned is perfectly correlated between the morphs. This result is derived for four widely used quantitative genetic variance partitioning methods. Given that theory predicts the evolution of genotype‐by‐environment (morph) interactions as a consequence of selection favoring different trait combinations in each morph, we argue that perfect correlations between the genetic architectures of the different morphs are unlikely. A sampling of the recent literature indicates that the majority of researchers studying traits expressed in different morphs recognize this and do estimate morph‐specific quantitative genetic architecture. However, ca. 16% of the studies in our sample utilized only univariate, fixed‐effects models. We caution against this approach and recommend that it be used only if supported by evidence that the genetic architectures of the different morphs do not differ.