Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments

Background

When rainbow trout from a single breeding program are introduced into various production environments, genotype-by-environment (GxE) interaction may occur. Although growth and its uniformity are two of the most important traits for trout producers worldwide, GxE interaction on uniformity of growth has not been studied. Our objectives were to quantify the genetic variance in body weight (BW) and its uniformity and the genetic correlation (r_g) between these traits, and to investigate the degree of GxE interaction on uniformity of BW in breeding (BE) and production (PE) environments using double hierarchical generalized linear models. Log-transformed data were also used to investigate whether the genetic variance in uniformity of BW, GxE interaction on uniformity of BW, and r_g between BW and its uniformity were influenced by a scale effect.

Results

Although heritability estimates for uniformity of BW were low and of similar magnitude in BE (0.014) and PE (0.012), the corresponding coefficients of genetic variation reached 19 and 21%, which indicated a high potential for response to selection. The genetic re-ranking for uniformity of BW (r_g = 0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low r_g (-0.08) between uniformity in BE and PE, which indicated independent genetic rankings for uniformity in the two environments when the scale effect was accounted for. The r_g between BW and its uniformity were 0.30 for BE and 0.79 for PE but with log-transformed BW, these values switched to -0.83 and -0.62, respectively.

Conclusions

Genetic variance exists for uniformity of BW in both environments but its low heritability implies that a large number of relatives are needed to reach even moderate accuracy of selection. GxE interaction on uniformity is present for both environments and sib-testing in PE is recommended when the aim is to improve uniformity across environments. Positive and negative r_g between BW and its uniformity estimated with original and log-transformed BW data, respectively, indicate that increased BW is genetically associated with increased variance in BW but with a decrease in the coefficient of variation. Thus, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its r_g.     

Basic density of radiata pine in New Zealand: genetic and environmental factors

Wood basic density is among the selection criteria for many fast-grown tree species, including Pinus radiata D. Don in New Zealand. Basic density was assessed in 23,330 stem cores from 18 trials to study the heritability, the relevance of environmental effects and the magnitude of genotype-by-environment (GxE) interaction. Site differences in annual average temperature dominated variability in this dataset, with lower latitude and altitude (i.e. warmer) sites displaying higher average density. Between highest- and lowest-density sites there was an 18% difference (302.7 vs. 358.4 kg m^− 3) for the linear mean for cores of rings 1–5 and a 39% difference (329.7 vs. 459.1 kg m^− 3) for the linear mean of rings 6–10. The estimated heritabilities fluctuated between 0.28 and 0.94 (mean, 0.6); however, basic density displayed little within-site variability (phenotypic coefficient of variation, <8%). Bivariate analyses were used to estimate between-site genetic correlations as an indication of GxE interaction. Only 57 out of the 153 pairs of trials contained enough information to estimate the between-site genetic correlations and, out of those, 15 estimates were not statistically significant. Moderate to high (0.46–0.96) significant genetic correlation estimates indicated that there was little interaction for basic density, suggesting no need to modify the breeding strategy to account for differential performance in this trait. Poor connectedness between trials could be depressing estimates of genetic correlations. This situation should be considered when designing genetic testing schemes, particularly when purposely inducing imbalance as in rolling front strategies.     

Natural genetic variation in social environment choice: context-dependent gene-environment correlation in Drosophila melanogaster

Gene-environment correlation (rGE) occurs when an individual's genotype determines its choice of environment, generating a correlation between environment and genotype frequency. In particular, social rGE, caused by genetic variation in social environment choice, can critically determine both individual development and the course of social selection. Despite its foundational role in social evolution and developmental psychology theory, natural genetic variation in social environment choice has scarcely been examined empirically. Drosophila melanogaster provides an ideal system for investigating social rGE. Flies live socially in nature and have many opportunities to make social decisions; and natural, heterozygous genotypes may be replicated, enabling comparisons between genotypes across environments. Using this approach, I show that all aspects of social environment choice vary among natural genotypes, demonstrating pervasive social rGE. Surprisingly, genetic variation in group-size preference was density dependent, indicating that the behavioral and evolutionary consequences of rGE may depend on the context in which social decisions are made. These results provide the first detailed investigation of social rGE, and illustrate that that genetic variation may influence organismal performance by specifying the environment in which traits are expressed.     

Socioeconomic status (SES) and children's intelligence (IQ): in a UK-representative sample SES moderates the environmental, not genetic, effect on IQ

Background

The environment can moderate the effect of genes - a phenomenon called gene-environment (GxE) interaction. Several studies have found that socioeconomic status (SES) modifies the heritability of children''s intelligence. Among low-SES families, genetic factors have been reported to explain less of the variance in intelligence; the reverse is found for high-SES families. The evidence however is inconsistent. Other studies have reported an effect in the opposite direction (higher heritability in lower SES), or no moderation of the genetic effect on intelligence.

Methods

Using 8716 twin pairs from the Twins Early Development Study (TEDS), we attempted to replicate the reported moderating effect of SES on children''s intelligence at ages 2, 3, 4, 7, 9, 10, 12 and 14: i.e., lower heritability in lower-SES families. We used a twin model that allowed for a main effect of SES on intelligence, as well as a moderating effect of SES on the genetic and environmental components of intelligence.

Results

We found greater variance in intelligence in low-SES families, but minimal evidence of GxE interaction across the eight ages. A power calculation indicated that a sample size of about 5000 twin pairs is required to detect moderation of the genetic component of intelligence as small as 0.25, with about 80% power - a difference of 11% to 53% in heritability, in low- (−2 standard deviations, SD) and high-SES (+2 SD) families. With samples at each age of about this size, the present study found no moderation of the genetic effect on intelligence. However, we found the greater variance in low-SES families is due to moderation of the environmental effect – an environment-environment interaction.

Conclusions

In a UK-representative sample, the genetic effect on intelligence is similar in low- and high-SES families. Children''s shared experiences appear to explain the greater variation in intelligence in lower SES.     

Testing marker-based estimates of heritability in the wild

Marker-based estimates of heritability are an attractive alternative to pedigree-based methods for estimating quantitative genetic parameters in field studies where it is difficult or impossible to determine relationships and pedigrees. Here I test the ability of the marker-based method to estimate heritability of a suite of traits in a wild population of bighorn sheep (Ovis canadensis) using marker data from 32 microsatellite loci. I compared marker-based estimates with estimates obtained using a pedigree and the animal model. Marker-based estimates of heritability were imprecise and downwardly biased. The high degree of uncertainty in marker-based estimates suggests that the method may be sufficient to detect the presence of genetic variance for highly heritable traits, but not sufficiently reliable to estimate genetic parameters.     

Genetic and environmental variations in quantitative characters in fishes: a comparative analysis of monoclonal triploid and bisexual tetraploid spined loaches (<Emphasis Type="Italic">Cobitis</Emphasis>, Cobitidae)

The heritability estimates of 25 external morphometric characters and 23 craniometric indices are obtained by use of variances in monoclonal all-female triploids and bisexual tetraploids of spined loaches (genus Cobitis, Cobitidae) collected from the same breeding biotope. Most of studied traits demonstrate low heritability confirming previous conclusion on the similarity between external morphometric characters and craniological indices in relative effects of genetic and environmental components in their total phenotypic variation. Low heritability estimates in most of external morphological traits correspond to their low diagnostic value in Cobitis species. As a whole, in spite of certain deviations, studies on clonal forms do not refute the concept on higher heritability estimates in diagnostically significant traits in comparison with traits without diagnostic values in the same taxonomic group. Low heritability in most morphometric traits more probably is resulted from their low additive genetic variation caused by strong selection of evolutionary developed specific body shape in spined loaches, because strong selection should reduce the genetic variance in body proportions to minimal size. Sex differences observed in heritability estimates should be interpreted as a result of linkage of several additive genes controlling these traits to sex chromosomes. A few characters demonstrating high heritability estimates up to 0.492–0.580 are of great interest for taxonomic and phylogenetic studies in genus Cobitis and related taxa.     

Assumptions and Properties of Limiting Pathway Models for Analysis of Epistasis in Complex Traits

For most complex traits, results from genome-wide association studies show that the proportion of the phenotypic variance attributable to the additive effects of individual SNPs, that is, the heritability explained by the SNPs, is substantially less than the estimate of heritability obtained by standard methods using correlations between relatives. This difference has been called the “missing heritability”. One explanation is that heritability estimates from family (including twin) studies are biased upwards. Zuk et al. revisited overestimation of narrow sense heritability from twin studies as a result of confounding with non-additive genetic variance. They propose a limiting pathway (LP) model that generates significant epistatic variation and its simple parametrization provides a convenient way to explore implications of epistasis. They conclude that over-estimation of narrow sense heritability from family data (‘phantom heritability’) may explain an important proportion of missing heritability. We show that for highly heritable quantitative traits large phantom heritability estimates from twin studies are possible only if a large contribution of common environment is assumed. The LP model is underpinned by strong assumptions that are unlikely to hold, including that all contributing pathways have the same mean and variance and are uncorrelated. Here, we relax the assumptions that underlie the LP model to be more biologically plausible. Together with theoretical, empirical, and pragmatic arguments we conclude that in outbred populations the contribution of additive genetic variance is likely to be much more important than the contribution of non-additive variance.     

Quantitative conservation genetics of wild and managed bees

Quantitative genetic traits provide insights into the evolutionary potential of populations, as heritability estimates measure the population’s ability to respond to global changes. Although wild and managed bees are increasingly threatened by the degradation of natural habitats and climate change, risking plant biodiversity and agriculture production, no study has yet performed a systematic review of heritability estimates across the group. Here we help fill this knowledge gap, gathering all available heritability estimates for ants, bees, and wasps, evaluating which factors affect these estimates and assessing the reported genetic correlations between traits. Using a model selection approach to analyze a dataset of more than 800 heritability estimates, we found that heritability is influenced by trait type, with morphological traits exhibiting the highest heritability estimates, and defense and metabolism-related traits showing the lowest estimates. Study system, sociality degree, experimental design, estimation type (narrow or broad-sense heritability), and sample size were not found to affect heritability estimates. Results remained unaltered when correcting for phylogenetic inertia, and when analyzing social bees separately. Genetic correlations between honeybee traits revealed both positive coefficients, usually for traits in the same category, and negative coefficients, suggesting trade-offs among other traits. We discuss these findings and highlight the importance of maintaining genetic variance in fitness-related traits. Our study shows the importance of considering heritability estimates and genetic correlations when designing breeding and conservation programs. We hope this meta-analysis helps identify sustainable breeding approaches and conservation strategies that help safeguard the evolutionary potential of wild and managed bees.     

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.     

Quantitative genetic tools for insecticide resistance risk assessment: estimating the heritability of resistance

Quantitative genetic studies of resistance can provide estimates of genetic parameters not available with other types of genetic analyses. Three methods are discussed for estimating the amount of additive genetic variation in resistance to individual insecticides and subsequent estimation of the heritability (h2) of resistance. Sibling analysis and offspring-parent regression permit direct estimates of h2 by comparing the resistance phenotypes of individuals of known relatedness. Threshold trait analyses, performed on data from selection experiments, provide estimates of realized heritability. Procedures are outlined for predicting changes in resistance to insecticides based on h2 estimates. Quantitative genetic theory is examined as it relates to resistance and resistance as a quantitative trait; quantitative genetic methods also are unique in providing estimates of genetic correlations between traits. Comments are included on estimates of genetic correlation between resistance and phenotypic traits (e.g., development time) and how they may be used to predict changes in the genetic aspects of phenology that result from insecticide applications (i.e., to predict how the reproductive capacity of future generations will differ from that of the treated generation).     

Plasticity in Vegetative Growth over Contrasted Growing Sites of an F1 Olive Tree Progeny during Its Juvenile Phase

Climatic changes impact fruit tree growth and severely limit their production. Investigating the tree ability to cope with environmental variations is thus necessary to adapt breeding and management strategies in order to ensure sustainable production. In this study, we assessed the genetic parameters and genotype by environment interaction (GxE) during the early tree growth. One hundred and twenty olive seedlings derived from the cross ‘Olivière’ x ‘Arbequina’ were examined across two sites with contrasted environments, accounting for ontogenetic trends over three years. Models including the year of growth, branching order, environment, genotype effects, and their interactions were built with variance function and covariance structure of residuals when necessary. After selection of a model, broad sense heritabilities were estimated. Despite strong environmental effect on most traits, no GxE was found. Moreover, the internal structure of traits co-variation was similar in both sites. Ontogenetic growth variation, related to (i) the overall tree form and (ii) the growth and branching habit at growth unit scale, was not altered by the environment. Finally, a moderate to strong genetic control was identified for traits at the whole tree scale and at internode scale. Among all studied traits, the maximal internode length exhibited the highest heritability (H² = 0.74). Considering the determinant role of this trait in tree architecture and its stability across environments, this study consolidates its relevance for breeding.     

Maximum likelihood estimation of human craniometric heritabilities

This study presents univariate narrow-sense heritability estimates for 33 common craniometric dimensions, calculated using the maximum likelihood variance components method on a skeletal sample of 298 pedigreed individuals from Hallstatt, Austria. Quantitative genetic studies that use skeletal cranial measurements as a basis for inferring microevolutionary processes in human populations usually employ heritability estimates to represent the genetic variance of the population. The heritabilities used are often problematic: most come from studies of living humans, and/or they were calculated using statistical techniques or assumptions violated by human groups. Most bilateral breadth measures in the current study show low heritability estimates, while cranial length and height measures have heritability values ranging between 0.102-0.729. There appear to be differences between the heritabilities calculated from crania and those from anthropometric studies of living humans, suggesting that the use of the latter in quantitative genetic models of skeletal data may be inappropriate. The univariate skeletal heritability estimates seem to group into distinct regions of the cranium, based on their relative values. The most salient group of measurements is for the midfacial/orbital region, with a number of measures showing heritabilities less than 0.30. Several possible reasons behind this pattern are examined. Given the fact that heritabilities calculated on one population should not be applied to others, suggestions are made for the use of the data presented.     

Genetic parameters for litter size in sheep: natural versus hormone-induced oestrus

The litter size in Suffolk and Texel-sheep was analysed using REML and Bayesian methods. Litters born after hormonal induced oestrus and after natural oestrus were treated as different traits in order to estimate the genetic correlation between the traits. Explanatory variables were the age of the ewe at lambing, period of lambing, a year*flock-effect, a permanent environmental effect associated with the ewe, and the additive genetic effect. The heritability estimates for litter size ranged from 0.06 to 0.13 using REML in bi-variate linear models. Transformation of the estimates to the underlying scale resulted in heritability estimates from 0.12 to 0.17. Posterior means of the heritability of litter size in the Bayesian approach with bi-variate threshold models varied from 0.05 to 0.18. REML estimates of the genetic correlations between the two types of litter size ranged from 0.57 to 0.64 in the Suffolk and from 0.75 to 0.81 in the Texel. The posterior means of the genetic correlation (Bayesian analysis) were 0.40 and 0.44 for the Suffolk and 0.56 and 0.75 for the Texel in the sire and animal model respectively. A bivariate threshold model seems appropriate for the genetic evaluation of prolificacy in the breeds concerned.     

Estimation of components of genetic variance and heritability for flowering time and yield in gerbera using Derivative-Free Restricted Maximum Likelihood (DFRML)

Summary Additive genetic components of variance and narrow-sense heritabilities were estimated for flowering time (FT) and cut-flower yield (Y) for six generations of the Davis Population of gerbera using Derivative-Free Restricted Maximum Likelihood (DFRML). Additive genetic variance accounted for 54% of the total variability for FT and 30% of the total variability for Y. The heritability of FT (0.54) agreed with previous ANOVA-based estimates. However, the heritability of Y (0.30) was substantially lower than estimates using ANOVA. The advantages of DFRML and its applications in the estimation of components of genetic variance and heritabilities of plant populations are discussed.     

The behaviors of some heritability estimators in the complete absence of genetic factors.

Heritability is an important concept in quantitative genetics and is widely used in human genetics. A high or even a moderate value of heritability estimate is usually taken as evidence for a genetic component for a quantitative trait. In this paper, the behaviors of some correlation-based heritability estimators are reexamined under the assumption of complete absence of any genetic factors. It turns out that when monozygotic (MZ) twins (or full sibs) are environmentally more similar than dizygotic twins (or half sibs), or when there is placement bias in MZ twins reared apart, those correlation-based heritability estimates can lead to nonnegligible or even high heritability values, even when genetic factors are completely absent. These alarming results suggest that extreme care should be exercised when using these heritability estimators.     

The influence of nonrandom extra‐pair paternity on heritability estimates derived from wild pedigrees

Quantitative genetic analysis is often fundamental for understanding evolutionary processes in wild populations. Avian populations provide a model system due to the relative ease of inferring relatedness among individuals through observation. However, extra‐pair paternity (EPP) creates erroneous links within the social pedigree. Previous work has suggested this causes minor underestimation of heritability if paternal misassignment is random and hence not influenced by the trait being studied. Nevertheless, much literature suggests numerous traits are associated with EPP and the accuracy of heritability estimates for such traits remains unexplored. We show analytically how nonrandom pedigree errors can influence heritability estimates. Then, combining empirical data from a large great tit (Parus major) pedigree with simulations, we assess how heritability estimates derived from social pedigrees change depending on the mode of the relationship between EPP and the focal trait. We show that the magnitude of the underestimation is typically small (<15%). Hence, our analyses suggest that quantitative genetic inference from pedigrees derived from observations of social relationships is relatively robust; our approach also provides a widely applicable method for assessing the consequences of nonrandom EPP.     

Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits

While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under-surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait Analysis (GCTA) was used to examine GxE variance contribution of 15 macronutrients and lifestyle to the total phenotypic variance of diabetes-related traits at the genome-wide level in a European American population. GCTA identified two key environmental factors making significant contributions to the GxE variance for diabetes-related traits: carbohydrate for fasting insulin (25.1% of total variance, P-nominal = 0.032) and homeostasis model assessment of insulin resistance (HOMA-IR) (24.2% of total variance, P-nominal = 0.035), n-6 polyunsaturated fatty acid (PUFA) for HOMA-β-cell-function (39.0% of total variance, P-nominal = 0.005). To demonstrate and support the results from GCTA, a GxE GWAS was conducted with each of the significant dietary factors and a control E factor (dietary protein), which contributed a non-significant GxE variance. We observed that GxE GWAS for the environmental factor contributing a significant GxE variance yielded more significant SNPs than the control factor. For each trait, we selected all significant SNPs produced from GxE GWAS, and conducted anew the GCTA to estimate the variance they contributed. We noted the variance contributed by these SNPs is higher than that of the control. In conclusion, we utilized a novel method that demonstrates the importance of genome-wide GxE interactions in explaining the variance of diabetes-related traits.     

Marker-based quantitative genetics in the wild?: the heritability and genetic correlation of chemical defenses in eucalyptus

Marker-based methods for estimating heritability and genetic correlation in the wild have attracted interest because traditional methods may be impractical or introduce bias via G x E effects, mating system variation, and sampling effects. However, they have not been widely used, especially in plants. A regression-based approach, which uses a continuous measure of genetic relatedness, promises to be particularly appropriate for use in plants with mixed-mating systems and overlapping generations. Using this method, we found significant narrow-sense heritability of foliar defense chemicals in a natural population of Eucalyptus melliodora. We also demonstrated a genetic basis for the phenotypic correlation underlying an ecological example of conditioned flavor aversion involving different biosynthetic pathways. Our results revealed that heritability estimates depend on the spatial scale of the analysis in a way that offers insight into the distribution of genetic and environmental variance. This study is the first to successfully use a marker-based method to measure quantitative genetic parameters in a tree. We suggest that this method will prove to be a useful tool in other studies and offer some recommendations for future applications of the method.     

Heritability variations of body linearity and obesity indicators during growth

Longitudinal as well as cross-sectional studies have shown variations with age in heritability estimates for body dimensions from infancy to adulthood, even though the patterns of variation are not completely clear. Further study on this subject is of great interest and may help obesity interventions for preventing or treating obesity in children. Therefore, the aim of the present study is to analyse the changes in the genetic and environmental architecture of 8 body linearity and obesity-related phenotypes during the growth process in a cross-sectional sample of 1018 nuclear families from the province of Biscay (Basque Country, Spain). The contribution of additive genetic effects to the variation of the analysed traits was estimated by a variance component analysis using the SOLAR program. Moderate to high heritability estimates were obtained for all 8 anthropometric phenotypes (38.23–65.98%). The heritability values show an increasing trend with age and in the course of the entire ontogenetic development two age periods were remarkable. At 7⁺–8⁺ years of age a strong increase in heritability estimates was found for all the anthropometric phenotypes, except for the sum of skinfolds (SF6), reflecting the biological significance of genes during mid-childhood. During puberty, most of the obesity related phenotypes showed their highest heritability values while linear measurements and weight presented a decrease in the genetic contributions. In conclusion, this study confirms that additive genetic influences have a considerable effect on body linearity and obesity-related traits throughout the growth period and that mid-childhood and puberty are very sensitive periods in human life cycle.     

The contribution of additive genetic variation to personality variation: heritability of personality

Individual animals frequently exhibit repeatable differences from other members of their population, differences now commonly referred to as ‘animal personality’. Personality differences can arise, for example, from differences in permanent environmental effects―including parental and epigenetic contributors―and the effect of additive genetic variation. Although several studies have evaluated the heritability of behaviour, less is known about general patterns of heritability and additive genetic variation in animal personality. As overall variation in behaviour includes both the among-individual differences that reflect different personalities and temporary environmental effects, it is possible for personality to be largely genetically influenced even when heritability of behaviour per se is quite low. The relative contribution of additive genetic variation to personality variation can be estimated whenever both repeatability and heritability are estimated for the same data. Using published estimates to address this issue, we found that approximately 52% of animal personality variation was attributable to additive genetic variation. Thus, while the heritability of behaviour is often moderate or low, the heritability of personality is much higher. Our results therefore (i) demonstrate that genetic differences are likely to be a major contributor to variation in animal personality and (ii) support the phenotypic gambit: that evolutionary inferences drawn from repeatability estimates may often be justified.