Models to estimate maternally controlled genetic variation in quantitative seed characters

Summary Estimating quantitative contributions to specific traits can be accomplished from a variety of genetic models (Mather 1949; Mather and Jinks 1971; Falconer 1981). Residual genetic effects, those beyond main and interaction effects of the embryo genotype, are often pooled under a single classification, termed maternal effects. Maternal contributions to seed-related traits can originate from various maternal sources (e.g., endosperm, testa and cytoplasm). Quantitative contributions of a maternal nature are not predictable from parental performance and effects are largely non-persistent over generations (Jinks et al. 1972). The methods used to determine maternal effects in quantitative traits often do not measure quantitative genetic parameters, while those that do are either complex or partially resolve potential contributions of individual sources of maternal effects. We present simple genetic models for estimating quantitative genetic parameters which take into account maternal effects expressed in the major seed tissues of higher plants.     

Validation of an approximate approach to compute genetic correlations between longevity and linear traits

The estimation of genetic correlations between a nonlinear trait such as longevity and linear traits is computationally difficult on large datasets. A two-step approach was proposed and was checked via simulation. First, univariate analyses were performed to get genetic variance estimates and to compute pseudo-records and their associated weights. These pseudo-records were virtual performances free of all environmental effects that can be used in a BLUP animal model, leading to the same breeding values as in the (possibly nonlinear) initial analyses. By combining these pseudo-records in a multiple trait model and fixing the genetic and residual variances to their values computed during the first step, we obtained correlation estimates by AI-REML and approximate MT-BLUP predicted breeding values that blend direct and indirect information on longevity. Mean genetic correlations and reliabilities obtained on simulated data confirmed the suitability of this approach in a wide range of situations. When nonzero residual correlations exist between traits, a sire model gave nearly unbiased estimates of genetic correlations, while the animal model estimates were biased upwards. Finally, when an incorrect genetic trend was simulated to lead to biased pseudo-records, a joint analysis including a time effect could adequately correct for this bias.     

A test of the hypothesis that correlational selection generates genetic correlations

Theory predicts that correlational selection on two traits will cause the major axis of the bivariate G matrix to orient itself in the same direction as the correlational selection gradient. Two testable predictions follow from this: for a given pair of traits, (1) the sign of correlational selection gradient should be the same as that of the genetic correlation, and (2) the correlational selection gradient should be positively correlated with the value of the genetic correlation. We test this hypothesis with a meta-analysis utilizing empirical estimates of correlational selection gradients and measures of the correlation between the two focal traits. Our results are consistent with both predictions and hence support the underlying hypothesis that correlational selection generates a genetic correlation between the two traits and hence orients the bivariate G matrix.     

Using the AMOVA framework to estimate a standardized genetic differentiation measure

Comparison of population structure between studies can be difficult, because the value of the often-used FST-statistic depends on the amount of genetic variation within populations. Recently, a standardized measure of genetic differentiation was developed based on GST, which addressed this problem, though no method was provided to estimate this standardized measure without bias. Here I present a method to estimate a standardized measure of population differentiation based on the analysis of molecular variance framework. One advantage of the method is that it can be readily expanded to include different hierarchical levels in the tested population structure.     

A genealogical estimate of genetic relationships

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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.     

Adaptation to the laboratory environment in Drosophila subobscura

Adaptation to a novel environment is expected to have a number of features. Among these is a temporal increase in fitness and some or all of its components. It is also expected that additive genetic variances for these fitness characters will fall. Finally, it is expected that at least some additive genetic correlations will decrease, from positive toward negative values. In a study of several life‐history variables in a Drosophila subobscura population sampled from the wild and then cultured in the laboratory, we did not find any such longitudinal trends over the first 29 generations. However, a temporal comparison (over 14 generations) of the later generations of this laboratory‐adapted population with a new population, derived from a more recent wild‐caught sample, indicated clearly that laboratory adaptation was nonetheless occurring. This study suggests the need for extensive replication and control in studies of the features of adaptation to a novel environment.     

Effects of food abundance on genetic and maternal variation in the growth rate of juvenile red squirrels

Sources of variation in growth in body mass were assessed in natural and experimental conditions of high and low food abundance using reciprocal cross-fostering techniques and long-term data (1987-2002) for a population of North American red squirrels (Tamiasciurus hudsonicus). Growth rates were significantly higher in naturally good and food supplemented conditions, than in poor conditions. Mother-offspring resemblance was higher in poor conditions as a result of large increases in both the direct genetic variance and direct-maternal genetic covariance and a smaller increase in the coefficient of maternal variation. Furthermore, the genetic correlation across environments was significantly less than one indicating that sources of heritable variation differed between the two environments. These results are consistent with the hypothesis that selection has eroded heritable variation for growth more in good conditions and indicate the potential for independent adaptation of growth rates in good and poor conditions.     

Partitioning of genetic variation across the genome using multimarker methods in a wild bird population

The underlying basis of genetic variation in quantitative traits, in terms of the number of causal variants and the size of their effects, is largely unknown in natural populations. The expectation is that complex quantitative trait variation is attributable to many, possibly interacting, causal variants, whose effects may depend upon the sex, age and the environment in which they are expressed. A recently developed methodology in animal breeding derives a value of relatedness among individuals from high‐density genomic marker data, to estimate additive genetic variance within livestock populations. Here, we adapt and test the effectiveness of these methods to partition genetic variation for complex traits across genomic regions within ecological study populations where individuals have varying degrees of relatedness. We then apply this approach for the first time to a natural population and demonstrate that genetic variation in wing length in the great tit (Parus major) reflects contributions from multiple genomic regions. We show that a polygenic additive mode of gene action best describes the patterns observed, and we find no evidence of dosage compensation for the sex chromosome. Our results suggest that most of the genomic regions that influence wing length have the same effects in both sexes. We found a limited amount of genetic variance in males that is attributed to regions that have no effects in females, which could facilitate the sexual dimorphism observed for this trait. Although this exploratory work focuses on one complex trait, the methodology is generally applicable to any trait for any laboratory or wild population, paving the way for investigating sex‐, age‐ and environment‐specific genetic effects and thus the underlying genetic architecture of phenotype in biological study systems.     

Partition of Genetic Variance for Sub-Populations

The variance between and within backcrosses of two populations is partitioned on the base of (I) multinomially distributed numbers of effects of specific chromosomes taking (II) recombination into account and on the base of (III) normally distributed sums of gene effects. The method of estimation makes use of a specific structure of data with backcross parents taken from different generations of random mating reproduction of crosses between the two populations.     

Combining direct and indirect genetic methods to estimate dispersal for informing wildlife disease management decisions

Epidemiological models are useful tools for management to predict and control wildlife disease outbreaks. Dispersal behaviours of the vector are critical in determining patterns of disease spread, and key variables in epidemiological models, yet they are difficult to measure. Raccoon rabies is enzootic over the eastern seaboard of North America and management actions to control its spread are costly. Understanding dispersal behaviours of raccoons can contribute to refining management protocols to reduce economic impacts. Here, estimates of dispersal were obtained through parentage and spatial genetic analyses of raccoons in two areas at the front of the raccoon rabies epizootic in Ontario; Niagara (N = 296) and St Lawrence (N = 593). Parentage analysis indicated the dispersal distance distribution is highly positively skewed with 85% of raccoons, both male and female, moving < 3 km. The tail of this distribution indicated a small proportion (< 4%) moves more than 20 km. Analysis of spatial genetic structure provided a similar assessment as the spatial genetic correlation coefficient dropped sharply after 1 km. Directionality of dispersal would have important implications for control actions; however, evidence of directional bias was not found. Separating the data into age and sex classes the spatial genetic analyses detected female philopatry. Dispersal distances differed significantly between juveniles and adults, while juveniles in the Niagara region were significantly more related to each other than adults were to each other. Factors that may contribute to these differences include kin association, and spring dispersal. Changes to the timing and area covered by rabies control operations in Ontario are indicated based on these dispersal data.     

Quantitative genetic analysis of bi-iliac breadth

Bi-iliac breadth, the frontal maximum diameter between right and left iliac crests, was measured in 1,547 male and 2,085 female residents of a rural area in Japan. All subjects were over 14 years of age. The bi-iliac breadth showed an increase related to age but little sex difference. Modification of age and sex variations from the measured value was obtained by calculation of the score, represented by arithmetical means and standard deviations. Distribution of these scores appeared to be binomial, and since binomial distribution approaches normal distribution when n is large, it is presumed that this trait gains normal distribution. Thus variation of bi-iliac breadth in subjects 20-79 years of age enables us to analyze inheritance. No significant difference was found between husband and wife in the correlation coefficients or between father and daughter (0.13 +/- 0.08). Significant differences were found as follows: father-son (0.35 +/- 0.05) (P less than 0.001), mother-son (0.28 +/- 0.05) (P less than 0.001), and mother-daughter (0.28 +/- 0.06) (P less than 0.001). There was no indication of maternal or paternal effects, since no significant difference was found in father-child and mother-child correlation coefficients. It is concluded that bi-iliac breadth is a quantitative genetic trait under control of polygenes on autosomes. Regression coefficient of child on midparental value was 0.55 +/- 0.05, approximately twice the means of four pairs of correlation coefficients between parent-offspring. Narrowly, heritability was estimated as 0.54 approximately 0.55. Contribution of dominance to total variance was small (VD = 0.11), in contrast to the larger additive genetic variance (VA = 0.54).     

Resistance to oxidative stress shows low heritability and high common environmental variance in a wild bird

Oxidative stress was recently demonstrated to affect several fitness‐related traits and is now well recognized to shape animal life‐history evolution. However, very little is known about how much resistance to oxidative stress is determined by genetic and environmental effects and hence about its potential for evolution, especially in wild populations. In addition, our knowledge of phenotypic sexual dimorphism and cross‐sex genetic correlations in resistance to oxidative stress remains extremely limited despite important evolutionary implications. In free‐living great tits (Parus major), we quantified heritability, common environmental effect, sexual dimorphism and cross‐sex genetic correlation in offspring resistance to oxidative stress by performing a split‐nest cross‐fostering experiment where 155 broods were split, and all siblings (n = 791) translocated and raised in two other nests. Resistance to oxidative stress was measured as both oxidative damage to lipids and erythrocyte resistance to a controlled free‐radical attack. Both measurements of oxidative stress showed low additive genetic variances, high common environmental effects and phenotypic sexual dimorphism with males showing a higher resistance to oxidative stress. Cross‐sex genetic correlations were not different from unity, and we found no substantial heritability in resistance to oxidative stress at adult age measured on 39 individuals that recruited the subsequent year. Our study shows that individual ability to resist to oxidative stress is primarily influenced by the common environment and has a low heritability with a consequent low potential for evolution, at least at an early stage of life.     

Sex-specific genetic variances in life-history and morphological traits of the seed beetle Callosobruchus maculatus

Knowledge of heritability and genetic correlations are of central importance in the study of adaptive trait evolution and genetic constraints. We use a paternal half-sib-full-sib breeding design to investigate the genetic architecture of three life-history and morphological traits in the seed beetle, Callosobruchus maculatus. Heritability was significant for all traits under observation and genetic correlations between traits (r(A)) were low. Interestingly, we found substantial sex-specific genetic effects and low genetic correlations between sexes (r(MF)) in traits that are only moderately (weight at emergence) to slightly (longevity) sexually dimorphic. Furthermore, we found an increased sire ([Formula: see text]) compared to dam ([Formula: see text]) variance component within trait and sex. Our results highlight that the genetic architecture even of the same trait should not be assumed to be the same for males and females. Furthermore, it raises the issue of the presence of unnoticed environmental effects that may inflate estimates of heritability. Overall, our study stresses the fact that estimates of quantitative genetic parameters are not only population, time, environment, but also sex specific. Thus, extrapolation between sexes and studies should be treated with caution.     

Back to the future: genetic correlations, adaptation and speciation

Genetic correlations can affect the course of phenotypic evolution. Although genetic correlations among traits are a common feature of quantitative genetic analyses, they have played a very minor role in recent linkage-map based analyses of the genetic architecture of quantitative traits. Here, we use our work on host-associated races in pea aphids to illustrate how quantitative trait locus (QTL) mapping can be used to test specific hypotheses about how genetic correlations may facilitate ecological specialization and speciation.     

A comparison of four methods for detecting weak genetic structure from marker data

Genetic structure is ubiquitous in wild populations and is the result of the processes of natural selection, genetic drift, mutation, and gene flow. Genetic drift and divergent selection promotes the generation of genetic structure, while gene flow homogenizes the subpopulations. The ability to detect genetic structure from marker data diminishes rapidly with a decreasing level of differentiation among subpopulations. Weak genetic structure may be unimportant over evolutionary time scales but could have important implications in ecology and conservation biology. In this paper we examine methods for detecting and quantifying weak genetic structures using simulated data. We simulated populations consisting of two putative subpopulations evolving for up to 50 generations with varying degrees of gene flow (migration), and varying amounts of information (allelic diversity). There are a number of techniques available to detect and quantify genetic structure but here we concentrate on four methods: F(ST), population assignment, relatedness, and sibship assignment. Under the simple mating system simulated here, the four methods produce qualitatively similar results. However, the assignment method performed relatively poorly when genetic structure was weak and we therefore caution against using this method when the analytical aim is to detect fine-scale patterns. Further work should examine situations with different mating systems, for example where a few individuals dominate reproductive output of the population. This study will help workers to design their experiments (e.g., sample sizes of markers and individuals), and to decide which methods are likely to be most appropriate for their particular data.     

Comparison of isogenic lines provides evidence that phenotypic plasticity is under genetic control in rainbow trout Oncorhynchus mykiss

Comparison of nine isogenic lines of rainbow trout Oncorhynchus mykiss kept in the same environment showed significant genetic determinism of phenotypic plasticity assessed through body mass measurements. Ranking of lines differed between two tested environments.     

On the Probabilities of Obtaining Negative Estimates of Genetic Variance Components

This paper derives the probabilities of obtaining negative estimates of additive and dominance genetic variances when one uses the traditional weighted least square method for estimating genetic variances as given in MATHER and JINKS (1971). The model considered involves P₁, P₂, F₂, B₁ (Backcross to P₁) and B₂ (Backcross to P₂). The results are derived under the ordinary assumptions as made in the genetic literatures. It is shown that unless the genetic effects are very large and environmental effects small, the probabilities of obtaining negative estimates of additive and dominance variances are in general quite large.     

Estimates of heritability and genetic correlations for milk coagulation properties and individual laboratory cheese yield in Sarda ewes

Objective of this study was to estimate genetic parameters of milk coagulation properties (MCPs) and individual laboratory cheese yield (ILCY) in a sample of 1018 Sarda breed ewes farmed in 47 flocks. Rennet coagulation time (RCT), curd-firming time (k₂₀) and curd firmness (a₃₀) were measured using Formagraph instrument, whereas ILCY were determined by a micromanufacturing protocol. About 10% of the milk samples did not coagulate within 30 min and 13% had zero value for k₂₀. The average ILCY was 36%. (Co)variance components of considered traits were estimated by fitting both single- and multiple-trait animal models. Flock-test date explained from 13% to 28% of the phenotypic variance for MCPs and 26% for ILCY, respectively. The largest value of heritability was estimated for RCT (0.23±0.10), whereas it was about 0.15 for the other traits. Negative genetic correlations between RCT and a₃₀ (−0.80±0.12), a₃₀ and k₂₀ (−0.91±0.09), and a₃₀ and ILCY (−0.67±0.08) were observed. Interesting genetic correlations between MCPs and milk composition (r_G>0.40) were estimated for pH, NaCl and casein. Results of the present study suggest to use only one out of three MCPs to measure milk renneting ability, due to high genetic correlations among them. Moreover, negative correlations between ILCY and MCPs suggest that great care should be taken when using these methods to estimate cheese yield from small milk samples.