A reassessment of genetic limits to evolutionary change

An absence of genetic variance in traits under selection is perhaps the oldest explanation for a limit to evolutionary change, but has also been the most easily dismissed. We review a range of theoretical and empirical results covering single traits to more complex multivariate systems, and show that an absence of genetic variance may be more common than is currently appreciated. From a single-trait perspective, we highlight that it is becoming clear that some trait types do not display significant levels of genetic variation, and we raise the possibility that species with restricted ranges may differ qualitatively from more widespread species in levels of genetic variance in ecologically important traits. A common misconception in many life-history studies is that a lack of genetic variance in single traits, and genetic constraints as a consequence of bivariate genetic correlations, are different causes of selection limits. We detail how interpretations of bivariate patterns are unlikely to demonstrate genetic limits to selection in many cases. We advocate a multivariate definition of genetic constraints that emphasizes the presence (or otherwise) of genetic variance in the multivariate direction of selection. For multitrait systems, recent results using longer term studies of organisms, in which more is understood concerning what traits may be under selection, have indicated that selection may exhaust genetic variance, resulting in a limit to the selection response.

Studying phenotypic evolution using multivariate quantitative genetics

Quantitative genetics provides a powerful framework for studying phenotypic evolution and the evolution of adaptive genetic variation. Central to the approach is G, the matrix of additive genetic variances and covariances. G summarizes the genetic basis of the traits and can be used to predict the phenotypic response to multivariate selection or to drift. Recent analytical and computational advances have improved both the power and the accessibility of the necessary multivariate statistics. It is now possible to study the relationships between G and other evolutionary parameters, such as those describing the mutational input, the shape and orientation of the adaptive landscape, and the phenotypic divergence among populations. At the same time, we are moving towards a greater understanding of how the genetic variation summarized by G evolves. Computer simulations of the evolution of G, innovations in matrix comparison methods, and rapid development of powerful molecular genetic tools have all opened the way for dissecting the interaction between allelic variation and evolutionary process. Here I discuss some current uses of G, problems with the application of these approaches, and identify avenues for future research.

The stability of P in coral reef fishes

The constancy of phenotypic variation and covariation is an assumption that underlies most recent investigations of past selective regimes and attempts to predict future responses to selection. Few studies have tested this assumption of constancy despite good reasons to expect that the pattern of phenotypic variation and covariation may vary in space and time. We compared phenotypic variance-covariance matrices (P) estimated for Populations of six species of distantly related coral reef fishes sampled at two locations on Australia's Great Barrier Reef separated by more than 1000 km. The intraspecific similarity between these matrices was estimated using two methods: matrix correlation and common principal component analysis. Although there was no evidence of equality between pairs of P, both statistical approaches indicated a high degree of similarity in morphology between the two populations for each species. In general, the hierarchical decomposition of the variance-covariance structure of these populations indicated that all principal components of phenotypic variance-covariance were shared but that they differed in the degree of variation associated with each of these components. The consistency of this pattern is remarkable given the diversity of morphologies and life histories encompassed by these species. Although some phenotypic instability was indicated, these results were consistent with a generally conserved pattern of multivariate selection between populations.

The Poisson-exponential lifetime distribution

In this paper we proposed a new two-parameters lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk problem base. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulae for its reliability and failure rate functions, quantiles and moments, including the mean and variance. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived analytically in order to obtaining the asymptotic covariance matrix. The methodology is illustrated on a real data set. (C) 2010 Elsevier B.V. All rights reserved.

Genetic variability in S(1) families from different maize populations.

Genetic variability in S(1) families from different maize populations. The objectives of the present work were directed towards the study of genetic: variablilty In seven maize populations with a broad genetic base, as a guide for population improvement. The field evaluation was conducted in completely randomized blocks, at one location (Anhembi, Sao Paulo state) with different groups, of S(1) families Obtained from seven populations (GO-D: dent type, GO-F: flint type, GO-L: long car, GO-G: thick Car; and composites G3, G4 and GO-S). Estimates were obtained for genetic variance (progeny mean basis), phenotypic variance of families means, and coefficient of heritability (broad sense) for progeny means. Estimates of heritability were high for Car weight (0,89 to 0.94), car length (0.77 to 0.88) and car diameter (0.77 to 0.92); and lower for plant height (0.58 to 0.80) and Car height (0.54 to 0.84), thus showing the high Potential of the populations for recurrent selection based oil S, families. Ear yield in the base populations used as controls varied front 11,200 kg ha(-1) (GO-D) to 12,800 kg ha(-1) (G3). The means of S(1) families varied from 6,070 kg ha(-1) (GO-F) to 7,380 kg ha(-1) (G4); the Inbreeding depression in S(1) Families varied front 37.5% (G4) to 48.0% (G3) relative to the non-inbred population.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

The genetic and environmental relationship between the interpersonal sensitivity measure (IPSM) and the personality dimensions of Eysenck and Cloninger

A shortened version of the Interpersonal Sensitivity Measure (IPSM) developed to predict depression prone personalities was administered in a self-report questionnaire to a community-based sample of 3269 Australian twin pairs aged 18-28 years, along with Eysenck's EPQ and Cloninger's TPQ. The IPSM included four sub-scales: Separation Anxiety (SEP); Interpersonal Sensitivity (INT); Fragile Inner-Self (FIS); and Timidity (TIM). Univariate analysis revealed that individual differences in the IPSM sub-scale scores were best explained by additive genetic and specific environmental effects. Confirming previous research findings, familial aggregation for the EPQ and TPQ personality dimensions was entirely due to additive genetic effects. In the multivariate case, a model comprising additive genetic and specific environmental effects best explained the covariation between the latent factors for male and female twin pairs alike. The EPQ and TPQ dimensions accounted for moderate to large proportions of the genetic variance (40-76%) in the IPSM sub-scales, while most of the non-shared environment variance was unique to the IPSM sub-scales. (C) 2001 Elsevier Science Ltd. All rights reserved.

Genetic covariation of neuroticism with monoamine oxidase activity and smoking

Variation in the personality trait of neuroticism is known to be affected by genetic influences, but despite a number of association studies, the genes involved have not yet been characterized. In a recent study of platelet monoamine oxidase in 1,551 twin subjects, we found a significant association between monoamine oxidase activity and scores on the Eysenck Personality Questionnaire neuroticism scale. Further analyses presented here indicate that both neuroticism and monoamine oxidase activity are associated with variation in smoking habits, and that adjusting for the effect of smoking strengthens the association between MAO and neuroticism. Analysis of the genetic and environmental sources of covariation between neuroticism, smoking, and monoamine oxidase activity show that approximately 8% of the genetic variance in neuroticism is due to the same additive genetic effects that contribute to variation in monoamine oxidase activity, suggesting that variation in neuroticism is associated in part with aspects of serotonin metabolism. (C) 2001 Wiley-Liss, Inc.

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. ne statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.

Genetic Covariation between Serum {gamma}-Glutamyltransferase Activity and Cardiovascular Risk Factors

Background: Several studies have shown that variation in serum gamma-glutamyltransferase (GGT) in the population is associated with risk of death or development of cardiovascular disease, type 2 diabetes, stroke, or hypertension. This association is only partly explained by associations between GGT and recognized risk factors. Our aim was to estimate the relative importance of genetic and environmental sources of variation in GGT as well as genetic and environmental sources of covariation between GGT and other liver enzymes and markers of cardiovascular risk in adult twin pairs. Methods: We recruited 1134 men and 2241 women through the Australian Twin Registry. Data were collected through mailed questionnaires, telephone interviews, and by analysis of blood samples. Sources of variation in GGT, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) and of covariation between GGT and cardiovascular risk factors were assessed by maximum-likelihood model-fitting. Results: Serum GGT, ALT, and AST were affected by additive genetic and nonshared environmental factors, with heritabilities estimated at 0.52, 0.48, and 0.32, respectively. One-half of the genetic variance in GGT was shared with ALT, AST, or both. There were highly significant correlations between GGT and body mass index; serum lipids, lipoproteins, glucose, and insulin; and blood pressure. These correlations were more attributable to genes that affect both GGT and known cardiovascular risk factors than to environmental factors. Conclusions: Variation in serum enzymes that reflect liver function showed significant genetic effects, and there was evidence that both genetic and environmental factors that affect these enzymes can also affect cardiovascular risk. (C) 2002 American Association for Clinical Chemistry.

Genetic and Environmental Influences on Migraine: A Twin Study Across Six Countries

Migraine is a common neurovascular brain disorder that is manifested in recurrent episodes of disabling headache. The aim of the present study was to compare the prevalence and heritability of migraine across six of the countries that participate in GenomEutwin project including a total number of 29,717 twin pairs. Migraine was assessed by questionnaires that differed between most countries. It was most prevalent in Danish and Dutch females (32% and 34%, respectively), whereas the lowest prevalence was found in the younger and older Finnish cohorts (13% and 10%, respectively). The estimated genetic variance (heritability) was significant and the same between sexes in all countries. Heritability ranged from 34% to 57%, with lowest estimates in Australia, and highest estimates in the older cohort of Finland, the Netherlands, and Denmark. There was some indication that part of the genetic variance was non-additive, but this was significant in Sweden only. In addition to genetic factors, environmental effects that are non-shared between members of a twin pair contributed to the liability of migraine. After migraine definitions are homogenized among the participating countries, the GenomEUtwin project will provide a powerful resource to identify the genes involved in migraine.

Genetic variation in foliar carbon isotope composition in relation to tree growth and foliar nitrogen concentration in clones of the F-1 hybrid between slash pine and Caribbean pine

The objectives of this study were: (1) to quantify the genetic variation in foliar carbon isotope composition (delta(13)C) of 122 clones of ca. 4-year-old F-1 hybrids between slash pine (Pinus elliottii Engelm var. elliottii) and Caribbean pine (Pinus caribaea var. hondurensis Barr.,et Golf.) grown at two field experimental sites with different water and nitrogen availability in southeast Queensland, Australia, in relation to tree growth and foliar nitrogen concentration (N-mass); and (2) to assess the potential of using delta(13)C measurements, in the foliage materials collected from the clone hedges at nursery and the 4-year-old tree canopies in the field, as an indirect index of tree water use efficiency for selecting elite F-1 hybrid pine clones with improved tree growth. There were significant differences in foliar delta(13)C between the nursery hedges and the 4-year-old tree canopies in the field, between the summer and winter seasons, between the two experimental sites, and between the upper outer and lower outer canopy positions sampled. This indicates that delta(13)C measurements in the foliage materials are significantly influenced by the sampling techniques and environmental conditions. Significant differences in foliar delta(13)C, at the upper outer canopy in both field experiments in summer and winter, were detected between the clones, and between the female parents of the clones. Clone means of tree height at age ca. 3 years were positively related to those of the upper outer canopy delta(13)C at both experimental sites in winter, but only for the wetter site in summer. There were positive, linear relationships between clone means of canopy delta(13)C and those of canopy N-mass, indicating that canopy photosynthetic capacity might be an important factor regulating the clonal variation in canopy delta(13)C. Significant correlations were found between clone means of canopy delta(13)C at both experimental sites in summer and winter, and between those at the upper outer and lower outer canopy positions. Mean clone delta(13)C for the nursery hedges was only positively related to mean clone stem diameter at 1.3 m height at age 3 years on the wetter site. The clone by site interaction for foliar delta(13)C at the upper outer canopy was significant only in summer. Overall, the relatively high genetic variance components for foliar delta(13)C and significant, positive correlations between clone means of foliar delta(13)C and tree growth have highlighted the potential of using foliar delta(13)C measurements for assisting in selection of the elite F-1 hybrid pine clones with improved tree growth. (C) 2002 Elsevier Science B.V. All rights reserved.

Orthogonal models, structure crossing, nesting and inference

We intend to study the algebraic structure of the simple orthogonal models to use them, through binary operations as building blocks in the construction of more complex orthogonal models. We start by presenting some matrix results considering Commutative Jordan Algebras of symmetric matrices, CJAs. Next, we use these results to study the algebraic structure of orthogonal models, obtained by crossing and nesting simpler ones. Then, we study the normal models with OBS, which can also be orthogonal models. We intend to study normal models with OBS (Orthogonal Block Structure), NOBS (Normal Orthogonal Block Structure), obtaining condition for having complete and suffcient statistics, having UMVUE, is unbiased estimators with minimal covariance matrices whatever the variance components. Lastly, see ([Pereira et al. (2014)]), we study the algebraic structure of orthogonal models, mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known orthogonal pairwise orthogonal projection matrices, OPOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expressions for the LSE of these models.

The nuclear matrix: a critical appraisal.

It is becoming increasingly clear that the cell nucleus is a highly structurized organelle. Because of its tight compartmentalization, it is generally believed that a framework must exist, responsible for maintaining such a spatial organization. Over the last twenty years many investigations have been devoted to identifying the nuclear framework. Structures isolated by different techniques have been obtained in vitro and are variously referred to as nuclear matrix, nucleoskeleton or nuclear scaffold. Many different functions, such as DNA replication and repair, mRNA transcription, processing and transport have been described to occur in close association with these structures. However, there is still much debate as to whether or not any of these preparations corresponds to a nuclear framework that exists in vivo. In this article we summarize the most commonly-used methods for obtaining preparations of nuclear frameworks and we also stress the possible artifacts that can be created in vitro during the isolation procedures. Emphasis is placed also on the protein composition of the frameworks as well as on some possible signalling functions that have been recently described to occur in tight association with the nuclear matrix.

Genetic and environmental components of phenotypic variation in immune response and body size of a colonial bird, Delichon urbica (the house martin).

Directional selection for parasite resistance is often intense in highly social host species. Using a partial cross-fostering experiment we studied environmental and genetic variation in immune response and morphology in a highly colonial bird species, the house martin (Delichon urbica). We manipulated intensity of infestation of house martin nests by the haematophagous parasitic house martin bug Oeciacus hirundinis either by spraying nests with a weak pesticide or by inoculating them with 50 bugs. Parasitism significantly affected tarsus length, T cell response, immunoglobulin and leucocyte concentrations. We found evidence of strong environmental effects on nestling body mass, body condition, wing length and tarsus length, and evidence of significant additive genetic variance for wing length and haematocrit. We found significant environmental variance, but no significant additive genetic variance in immune response parameters such as T cell response to the antigenic phytohemagglutinin, immunoglobulins, and relative and absolute numbers of leucocytes. Environmental variances were generally greater than additive genetic variances, and the low heritabilities of phenotypic traits were mainly a consequence of large environmental variances and small additive genetic variances. Hence, highly social bird species such as the house martin, which are subject to intense selection by parasites, have a limited scope for immediate microevolutionary response to selection because of low heritabilities, but also a limited scope for long-term response to selection because evolvability as indicated by small additive genetic coefficients of variation is weak.