Competition can maintain genetic but not environmental variance in the presence of stabilizing selection

A population in which there is stabilizing selection acting on quantitative traits toward an intermediate optimum becomes monomorphic in the absence of mutation. Further, genotypes that show least environmental variation are also favored, such that selection is likely to reduce both genetic and environmental components of phenotypic variance. In contrast, intraspecific competition for resources is more severe between phenotypically similar individuals, such that those deviating from prevailing phenotypes have a selective advantage. It has been shown previously that polymorphism and phenotypic variance can be maintained if competition between individuals is "effectively" stronger than stabilizing selection. Environmental variance is generally observed in quantitative traits, so mechanisms to explain its maintenance are sought, but the impact of competition on its magnitude has not previously been studied. Here we assume that a quantitative trait is subject to selection for an optimal value and to selection due to competition. Further, we assume that both the mean and variance of the phenotypic value depend on genotype, such that both may be affected by selection. Theoretical analysis and numerical simulations reveal that environmental variance can be maintained only when the genetic variance (in mean phenotypic value) is constrained to a very low level. Environmental variance will be replaced entirely by genotypic variance if a range of genotypes that vary widely in mean phenotype are present or become so by mutation. The distribution of mean phenotypic values is discrete when competition is strong relative to stabilizing selection; but more genotypes segregate and the distribution can approach continuity as competition becomes extremely strong. If the magnitude of the environmental variance is not under genetic control, there is a complementary relationship between the levels of environmental and genetic variance such that the level of phenotypic variance is little affected.     

PHENOTYPIC PLASTICITY AND EPIGENETIC MARKING: AN ASSESSMENT OF EVIDENCE FOR GENETIC ACCOMMODATION

The relationship between genotype (which is inherited) and phenotype (the target of selection) is mediated by environmental inputs on gene expression, trait development, and phenotypic integration. Phenotypic plasticity or epigenetic modification might influence evolution in two general ways: (1) by stimulating evolutionary responses to environmental change via population persistence or by revealing cryptic genetic variation to selection, and (2) through the process of genetic accommodation, whereby natural selection acts to improve the form, regulation, and phenotypic integration of novel phenotypic variants. We provide an overview of models and mechanisms for how such evolutionary influences may be manifested both for plasticity and epigenetic marking. We point to promising avenues of research, identifying systems that can best be used to address the role of plasticity in evolution, as well as the need to apply our expanding knowledge of genetic and epigenetic mechanisms to our understanding of how genetic accommodation occurs in nature. Our review of a wide variety of studies finds widespread evidence for evolution by genetic accommodation.     

Incorporating genetic analyses into birth defects cluster investigations: strategies for identifying candidate genes

BACKGROUND: Incorporating genetic analyses into birth defect cluster investigations may increase understanding of both genetic and environmental risk factors for the defect. Current constraints of most birth defect cluster investigations make candidate gene selection the most feasible approach. Here, we describe strategies for choosing candidate genes for such investigations, which will also be applicable to more general gene-environment studies. METHODS: We reviewed publicly available web-based resources for selection of candidate genes and identification of risk factors, as well as publications on different strategies for candidate gene selection. RESULTS: Candidate gene selection requires consideration of available gene-disease databases, previous epidemiological studies, animal model research, linkage and expression studies, and other resources. We describe general considerations for utilizing available resources, as well as provide an example of a search for candidate genes related to gastroschisis. CONCLUSIONS: Available web resources could facilitate selection of candidate genes, but selection of optimal candidates will still require a strong understanding of genetics and the pathogenesis of the defect, as well as careful consideration of previous epidemiological studies.     

Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle

Background

Many studies have provided evidence of the existence of genetic heterogeneity of environmental variance, suggesting that it could be exploited to improve robustness and uniformity of livestock by selection. However, little is known about the perspectives of such a selection strategy in beef cattle.

Methods

A two-step approach was applied to study the genetic heterogeneity of residual variance of weight gain from birth to weaning and long-yearling weight in a Nellore beef cattle population. First, an animal model was fitted to the data and second, the influence of additive and environmental effects on the residual variance of these traits was investigated with different models, in which the log squared estimated residuals for each phenotypic record were analyzed using the restricted maximum likelihood method. Monte Carlo simulation was performed to assess the reliability of variance component estimates from the second step and the accuracy of estimated breeding values for residual variation.

Results

The results suggest that both genetic and environmental factors have an effect on the residual variance of weight gain from birth to weaning and long-yearling in Nellore beef cattle and that uniformity of these traits could be improved by selecting for lower residual variance, when considering a large amount of information to predict genetic merit for this criterion. Simulations suggested that using the two-step approach would lead to biased estimates of variance components, such that more adequate methods are needed to study the genetic heterogeneity of residual variance in beef cattle.     

Analysis and practical guideline of constraint-based boolean method in genetic network inference

Boolean-based method, despite of its simplicity, would be a more attractive approach for inferring a network from high-throughput expression data if its effectiveness has not been limited by high false positive prediction. In this study, we explored factors that could simply be adjusted to improve the accuracy of inferring networks. Our work focused on the analysis of the effects of discretisation methods, biological constraints, and stringency of boolean function assignment on the performance of boolean network, including accuracy, precision, specificity and sensitivity, using three sets of microarray time-series data. The study showed that biological constraints have pivotal influence on the network performance over the other factors. It can reduce the variation in network performance resulting from the arbitrary selection of discretisation methods and stringency settings. We also presented the master boolean network as an approach to establish the unique solution for boolean analysis. The information acquired from the analysis was summarised and deployed as a general guideline for an efficient use of boolean-based method in the network inference. In the end, we provided an example of the use of such a guideline in the study of Arabidopsis circadian clock genetic network from which much interesting biological information can be inferred.     

Improving accuracy of breeding values by incorporating genomic information in spatial-competition mixed models

Climate change and the increasing demand for sustainable energy resources require urgent strategies to increase the accuracy of selection in tree breeding (associated with higher gain). We investigated the combined pedigree and genomic-based relationship approach and its impact on the accuracy of predicted breeding values using data from 5-year-old Eucalyptus grandis progeny trial. The number of trees that can be genotyped in a tree breeding population is limited; therefore, the combined approach can be a feasible and efficient strategy to increase the genetic gain and provide more accurate predicted breeding values. We calculated the accuracy of predicted breeding values for two growth traits, diameter at breast height and total height, using two evaluation approaches: the combined approach and the classical pedigree-based approach. We also investigated the influence of two different trait heritabilities as well as the inclusion of competition genetic effects or environmental heterogeneity in an individual-tree mixed model on the estimated variance components and accuracy of breeding values. The genomic information of genotyped trees is automatically propagated to all trees with the combined approach, including the non-genotyped mothers. This increased the accuracy of overall breeding values, except for the non-genotyped trees from the competition model. The increase in the accuracy was higher for the total height, the trait with low heritability. The combined approach is a simple, fast, and accurate genomic selection method for genetic evaluation of growth traits in E. grandis and tree species in general. It is simple to implement in a traditional individual-tree mixed model and provides an easy extension to individual-tree mixed models with competition effects and/or environmental heterogeneity.     

Animal board invited review: Genomic-based improvement of cattle in response to climate change

Climate change brings challenges to cattle production, such as the need to adapt to new climates and pressure to reduce greenhouse emissions (GHG). In general, the improvement of traits in current breeding goals is favourably correlated with the reduction of GHG. Current breeding goals and tools for increasing cattle production efficiency have reduced GHG. The same amount of production can be achieved by a much smaller number of animals. Genomic selection (GS) may offer a cost-effective way of using an efficient breeding approach, even in low- and middle-income countries. As climate change increases the intensity of heatwaves, adaptation to heat stress leads to lower efficiency of production and, thus, is unfavourable to the goal of reducing GHG. Furthermore, there is evidence that heat stress during cow pregnancy can have many generation-long lowering effects on milk production. Both adaptation and reduction of GHG are among the difficult-to-measure traits for which GS is more efficient and suitable than the traditional non-genomic breeding evaluation approach. Nevertheless, the commonly used within-breed selection may be insufficient to meet the new challenges; thus, cross-breeding based on selecting highly efficient and highly adaptive breeds may be needed. Genomic introgression offers an efficient approach for cross-breeding that is expected to provide high genetic progress with a low rate of inbreeding. However, well-adapted breeds may have a small number of animals, which is a source of concern from a genetic biodiversity point of view. Furthermore, low animal numbers also limit the efficiency of genomic introgression. Sustainable cattle production in countries that have already intensified production is likely to emphasise better health, reproduction, feed efficiency, heat stress and other adaptation traits instead of higher production. This may require the application of innovative technologies for phenotyping and further use of new big data techniques to extract information for breeding.     

Effect of migration and environmental heterogeneity on the maintenance of quantitative genetic variation: a simulation study

The paradox of high genetic variation observed in traits under stabilizing selection is a long‐standing problem in evolutionary theory, as mutation rates appear too low to explain observed levels of standing genetic variation under classic models of mutation–selection balance. Spatially or temporally heterogeneous environments can maintain more standing genetic variation within populations than homogeneous environments, but it is unclear whether such conditions can resolve the above discrepancy between theory and observation. Here, we use individual‐based simulations to explore the effect of various types of environmental heterogeneity on the maintenance of genetic variation (V_A) for a quantitative trait under stabilizing selection. We find that V_A is maximized at intermediate migration rates in spatially heterogeneous environments and that the observed patterns are robust to changes in population size. Spatial environmental heterogeneity increased variation by as much as 10‐fold over mutation–selection balance alone, whereas pure temporal environmental heterogeneity increased variance by only 45% at max. Our results show that some combinations of spatial heterogeneity and migration can maintain considerably more variation than mutation–selection balance, potentially reconciling the discrepancy between theoretical predictions and empirical observations. However, given the narrow regions of parameter space required for this effect, this is unlikely to provide a general explanation for the maintenance of variation. Nonetheless, our results suggest that habitat fragmentation may affect the maintenance of V_A and thereby reduce the adaptive capacity of populations.     

Rule-Based Models of the Interplay between Genetic and Environmental Factors in Childhood Allergy

Both genetic and environmental factors are important for the development of allergic diseases. However, a detailed understanding of how such factors act together is lacking. To elucidate the interplay between genetic and environmental factors in allergic diseases, we used a novel bioinformatics approach that combines feature selection and machine learning. In two materials, PARSIFAL (a European cross-sectional study of 3113 children) and BAMSE (a Swedish birth-cohort including 2033 children), genetic variants as well as environmental and lifestyle factors were evaluated for their contribution to allergic phenotypes. Monte Carlo feature selection and rule based models were used to identify and rank rules describing how combinations of genetic and environmental factors affect the risk of allergic diseases. Novel interactions between genes were suggested and replicated, such as between ORMDL3 and RORA, where certain genotype combinations gave odds ratios for current asthma of 2.1 (95% CI 1.2-3.6) and 3.2 (95% CI 2.0-5.0) in the BAMSE and PARSIFAL children, respectively. Several combinations of environmental factors appeared to be important for the development of allergic disease in children. For example, use of baby formula and antibiotics early in life was associated with an odds ratio of 7.4 (95% CI 4.5-12.0) of developing asthma. Furthermore, genetic variants together with environmental factors seemed to play a role for allergic diseases, such as the use of antibiotics early in life and COL29A1 variants for asthma, and farm living and NPSR1 variants for allergic eczema. Overall, combinations of environmental and life style factors appeared more frequently in the models than combinations solely involving genes. In conclusion, a new bioinformatics approach is described for analyzing complex data, including extensive genetic and environmental information. Interactions identified with this approach could provide useful hints for further in-depth studies of etiological mechanisms and may also strengthen the basis for risk assessment and prevention.     

Accuracy of marker-assisted selection with auxiliary traits

Genetic information on molecular markers is increasingly being used in plant and animal improvement programmes particularly as indirect means to improve a metric trait by selection either on an individual basis or on the basis of an index incorporating such information. This paper examines the utility of an index of selection that not only combines phenotypic and molecular information on the trait under improvement but also combines similar information on one or more auxiliary traits. The accuracy of such a selection procedure has been theoretically studied for sufficiently large populations so that the effects of detected quantitative trait loci can be perfectly estimated. The theory is illustrated numerically by considering one auxiliary trait. It is shown that the use of an auxiliary trait improves the selection accuracy; and, hence, the relative efficiency of index selection compared to individual selection which is based on the same intensity of selection. This is particularly so for higher magnitudes of residual genetic correlation and environmental correlation having opposite signs, lower values of the proportion of genetic variation in the main trait associated with the markers, negligible proportion of genetic variation in the auxiliary trait associated with the markers, and lower values of the heritability of the main trait but higher values of the heritability of the auxiliary trait.     

Efficiency of indirect selection at selection equilibrium

Summary Efficiency of indirect selection compared with that of direct selection to increase the mean value of some trait has been usually studied by considering a single generation of indirect and direct responses to selection only. However, under continued selection, genetic variances and covariances, and therefore expected genetic responses, change each generation due to linkage disequilibrium. With directional and truncation selection, genetic parameters asymptote to limiting values after several generations. The efficiency of indirect selection is examined in this limiting situation. The ratio of correlated response to direct response for the trait to improve in the limit is compared with the ratio after the first generation of selection. For all initial parameter values for which indirect selection is more efficient than direct selection, relative efficiency of indirect selection is smaller in the limit than in the first generation. For some parameter values, indirect selection is more efficient than direct selection in the first generation, but less efficient in the limit. Expressions for minimum values of the initial genetic correlation and heritability of the alternative trait required for indirect selection to be preferred in the limit are derived. These values are higher when limiting responses are used instead of single generation responses. The loss in relative efficiency of indirect selection from changes in genetic parameters due to selection should be taken into account when applications of indirect selection are considered.     

Genetic Variation in Toxicant-Stressed Populations: An Evaluation of the “Genetic Erosion” Hypothesis

The question whether environmental pollution affects genetic diversity in natural populations remains unanswered to date despite the fact that genetic variation is one of the three pillars of biodiversity recognized in the Rio convention of 1993. The loss of genetic diversity in populations subjected to anthropogenic stress can be designated as “genetic erosion” and may be considered as a factor of concern in risk assessment of toxic chemicals. Theoretically there are four different ways in which toxicants can affect genetic variation: (i) by increasing mutation rates, (ii) by directional selection on tolerant genotypes, (iii) by causing bottleneck events, and (iv) by altering migration. This paper reviews studies that have documented genetic change in animal populations exposed to environmental pollution. In these studies, genetic variation is measured in a variety of ways: heritability of quantitative characters, heterozygosity of allozyme loci, haplotype diversity in mitochondrial DNA, and variability in RAPD fingerprints. Studies on cadmium tolerance of Collembola living in metal-contaminated soil suggest that strong directional selection pressure may decrease genetic variability of traits immediately linked to tolerance. Allozyme studies in fish have documented a similar decrease of genetic variation in populations living in strongly acidified waters. A correlation between RAPD-PCR-based genetic similarity and site contamination has been documented in crayfish. Overall, there is significant support for the genetic erosion hypothesis, but the issue cannot be considered settled. In most studies insufficient attention is given to factors such as population size, bottlenecks and mutation, which may influence genetic variability in addition to the toxicant selection regime. At the moment, there does not seem to be a sound scientific basis for incorporating genetic diversity measurements into risk assessment, despite the variety of easily applicable molecular techniques available. It is often not known what kind of variation is measured by these techniques (neutral or selectable) and how the markers are inherited. Given the importance of the issue, as stressed by the Rio Convention, a concentrated research effort is necessary to better define the question and find a general approach to evaluate its importance in ecological risk assessment.     

Design and analysis of experiments on random drift and inbreeding depression

While the genetic consequences of inbreeding and small population size are of fundamental importance in many areas of biology, empirical research on these phenomena has proceeded in the absence of a well-developed statistical methodology. The usual approach is to compare observed means and variances with the expectations of Wright's neutral, additive genetic model for quantitative characters. If the observations deviate from the expectations more than can be accounted for by sampling variance of the parameter estimates, the null hypothesis is routinely rejected in favor of alternatives invoking evolutionary forces such as selection or nonadditive gene action. This is a biased procedure because it treats sequential samples from the same populations as independent, and because it ignores the fact that the expectations of the neutral additive genetic model will rarely be realized when only a finite number of lines are studied. Even when genes are perfectly additive and neutral, the variation among the properties of founder populations, the random development of linkage disequilibrium within lines, and the variance in inbreeding between lines reduce the likelihood that Wright's expectations will be realized in any particular set of lines. Under most experimental designs, these sources of variation are much too large to be ignored. Formulas are presented for the variance-covariance structure of the realized within- and between-line variance under the neutral additive genetic model. These results are then used to develop statistical tests for detecting the operation of selection and/or inbreeding depression in small populations. A number of recommendations are made for the optimal design of experiments on drift and inbreeding, and a method is suggested for the correction of data for general environmental effects. In general, it appears that we can best understand the response of populations to inbreeding and finite population size by studying a very large number (>100) of self-fertilizing or full-sib mated lines in parallel with one or more stable control populations.     

Genetic evaluation of hip score in UK Labrador Retrievers

Hip dysplasia is an important and complex genetic disease in dogs with both genetic and environmental influences. Since the osteoarthritis that develops is irreversible the only way to improve welfare, through reducing the prevalence, is through genetic selection. This study aimed to evaluate the progress of selection against hip dysplasia, to quantify potential improvements in the response to selection via use of genetic information and increases in selection intensity, and to prepare for public provision of estimated breeding values (EBV) for hip dysplasia in the UK. Data consisted of 25,243 single records of hip scores of Labrador Retrievers between one and four years old, from radiographs evaluated between 2000 and 2007 as part of the British Veterinary Association (BVA) hip score scheme. A natural logarithm transformation was applied to improve normality and linear mixed models were evaluated using ASREML. Genetic correlations between left and right scores, and total hip scores at one, two and three years of age were found to be close to one, endorsing analysis of total hip score in dogs aged one to three as an appropriate approach. A heritability of 0.35±0.016 and small but significant litter effect (0.07±0.009) were estimated. The observed trends in both mean hip score and mean EBV over year of birth indicate that a small genetic improvement has been taking place, approximately equivalent to avoiding those dogs with the worst 15% of scores. Deterministic analysis supported by simulations showed that a 19% greater response could be achieved using EBV compared to phenotype through increases in accuracy alone. This study establishes that consistent but slow genetic improvement in the hip score of UK Labrador Retrievers has been achieved over the previous decade, and demonstrates that progress may be easily enhanced through the use of EBVs and more intense selection.     

The Genetic Architecture of Flowering Time and Photoperiod Sensitivity in Maize as Revealed by QTL Review and Meta Analysis

The control of flowering is not only important for reproduction,but also plays a key role in the processes of domestication and adaptation.To reveal the genetic architecture for flowering time and photoperiod sensitivity,a comprehensive evaluation of the relevant literature was performed and followed by meta analysis.A total of 25 synthetic consensus quantitative trait loci(QTL)and four hot-spot genomic regions were identified for photoperiod sensitivity including 11 genes related to photoperiod response or flower morphogenesis and development.Besides,a comparative analysis of the QTL for flowering time and photoperiod sensitivity highlighted the regions containing shared and unique QTL for the two traits.Candidate genes associated with maize flowering were identified through integrated analysis of the homologous genes for flowering time in plants and the consensus QTL regions for photoperiod sensitivity in maize(Zea mays L.).Our results suggest that the combination of literature review,meta-analysis and homologous blast is an efficient approach to identify new candidate genes and create a global view of the genetic architecture for maize photoperiodic flowering.Sequences of candidate genes can be used to develop molecular markers for various models of marker-assisted selection,such as marker-assisted recurrent selection and genomic selection that can contribute significantly to crop environmental adaptation.     

Addressing the Life Cycle of Sewers in Contrasting Cities through an Eco‐Efficiency Approach

Evaluating the sustainability of the urban water cycle is not straightforward, although a variety of methods have been proposed. Given the lack of integrated data about sewers, we applied the eco‐efficiency approach to two case studies located in Spain with contrasting climate, population, and urban and sewer configurations. Our goal was to determine critical variables and life cycle stages and provide results for decision making. We used life cycle assessment and life cycle costing to evaluate their environmental and economic impacts. Results showed that both cities have a similar profile, albeit their contrasting features, that is, operation and maintenance, was the main environmental issue (50% to 70% of the impacts) and pipe installation registered the greatest economic capital expenditure (70% to 75%) due to labor. The location of the wastewater treatment plant (WWTP) is an essential factor in our analysis mainly due to the topography effects (e.g., the annual pump energy was 13 times greater in Calafell). Using the eco‐efficiency portfolio, we observed that sewers might be less eco‐efficient than WWTPs and that we need to envision their design in the context of an integrated WWTP‐sewer management to improve sewer performance. In terms of methodological approach, the bidimensional nature of eco‐efficiency enables the benchmarking of product systems and might be more easily interpreted by the general public. However, there are still some constraints that should be addressed to improve communication, such as the selection of indicators discussed in the article.     

Genetic Variation in a Heterogeneous Environment. II. Temporal Heterogeneity and Directional Selection

The maintenance of genetic variation is investigated in a finite population where selection at an autosomal locus with two alleles varies temporally between two environments and the heterozygote has an intermediate fitness value. When there is additive gene action and equal selection in both environments, the autocorrelation between subsequent environments must be negative for more maintenance of genetic variation than for neutrality. The maximum maintenance occurs when there is equal selection in the two environments and the autocorrelation approaches ^-1.0 (for a stochastic model), or when there is short repeating cycle such as one related to seasons. Also comparison of the effects of stochastic variation in selection in finite and infinite populations is made by using Monte Carlo simulation. One situation was found where temporal environmental variation maintains genetic variation very effectively even in a small population and that is when there is evolution of dominance, i.e., the heterozygote is closer in fitness to the favored homozygote than the other homozygote. An important conclusion is that in a finite population genetic tracing of environmental change, particularly when there is a positive autocorrelation between environments or a long environmental cycle, leads to an increased loss of genetic variation making such a response undesirable in the long term, a result different from that in infinite populations.     

Probability of success of breeding strategies for improving pro-vitamin A content in maize

Biofortification for pro-vitamin A content (pVAC) of modern maize inbreds and hybrids is a feasible way to deal with vitamin A deficiency in rural areas in developing countries. The objective of this study was to evaluate the probability of success of breeding strategies when transferring the high pVAC present in donors to elite modern-adapted lines. For this purpose, a genetic model was built based on previous genetic studies, and different selection schemes including phenotypic selection (PS) and marker-assisted selection (MAS) were simulated and compared. MAS for simultaneously selecting all pVAC genes and a combined scheme for selecting two major pVAC genes by MAS followed by ultra performance liquid chromatography screening for the remaining genetic variation on pVAC were identified as being most effective and cost-efficient. The two schemes have 83.7 and 84.8% probabilities of achieving a predefined breeding target on pVAC and adaptation in one breeding cycle under the current breeding scale. When the breeding scale is increased by making 50% more crosses, the probability values could reach 94.8 and 95.1% for the two schemes. Under fixed resources, larger early generation populations with fewer crosses had similar breeding efficiency to smaller early generation populations with more crosses. Breeding on a larger scale was more efficient both genetically and economically. The approach presented in this study could be used as a general way in quantifying probability of success and comparing different breeding schemes in other breeding programs.     

The opportunity for canalization and the evolution of genetic networks

There has been a recent revival of interest in how genetic interactions evolve, spurred on by an increase in our knowledge of genetic interactions at the molecular level. Empirical work on genetic networks has revealed a surprising amount of robustness to perturbations, suggesting that robustness is an evolved feature of genetic networks. Here, we derive a general model for the evolution of canalization that can incorporate any form of perturbation. We establish an upper bound to the strength of selection on canalization that is approximately equal to the fitness load in the system. This method makes it possible to compare different forms of perturbation, including genetic, developmental, and environmental effects. In general, load that arises from mutational processes is low because the mutation rate is itself low. Mutation load can create selection for canalization in a small network that can be achieved through dominance evolution or gene duplication, and in each case selection for canalization is weak at best. In larger genetic networks, selection on genetic canalization can be reasonably strong because larger networks have higher mutational load. Because load induced through migration, segregation, developmental noise, and environmental variance is not mutation limited, each can cause strong selection for canalization.