Home About us Contact
|
Home About us Contact | ||
|
|
||
Quantitative Genetics (quantitative + genetics)
Selected AbstractsA CENTENNIAL CELEBRATION FOR QUANTITATIVE GENETICSEVOLUTION, Issue 5 2007Derek A. Roff Quantitative genetics is at or is fast approaching its centennial. In this perspective I consider five current issues pertinent to the application of quantitative genetics to evolutionary theory. First, I discuss the utility of a quantitative genetic perspective in describing genetic variation at two very different levels of resolution, (1) in natural, free-ranging populations and (2) to describe variation at the level of DNA transcription. Whereas quantitative genetics can serve as a very useful descriptor of genetic variation, its greater usefulness is in predicting evolutionary change, particularly when used in the first instance (wild populations). Second, I review the contributions of Quantitative trait loci (QLT) analysis in determining the number of loci and distribution of their genetic effects, the possible importance of identifying specific genes, and the ability of the multivariate breeder's equation to predict the results of bivariate selection experiments. QLT analyses appear to indicate that genetic effects are skewed, that at least 20 loci are generally involved, with an unknown number of alleles, and that a few loci have major effects. However, epistatic effects are common, which means that such loci might not have population-wide major effects: this question waits upon (QTL) analyses conducted on more than a few inbred lines. Third, I examine the importance of research into the action of specific genes on traits. Although great progress has been made in identifying specific genes contributing to trait variation, the high level of gene interactions underlying quantitative traits makes it unlikely that in the near future we will have mechanistic models for such traits, or that these would have greater predictive power than quantitative genetic models. In the fourth section I present evidence that the results of bivariate selection experiments when selection is antagonistic to the genetic covariance are frequently not well predicted by the multivariate breeder's equation. Bivariate experiments that combine both selection and functional analyses are urgently needed. Finally, I discuss the importance of gaining more insight, both theoretical and empirical, on the evolution of the G and P matrices. [source] QUANTITATIVE GENETICS OF PLASTRON SHAPE IN SLIDER TURTLES (TRACHEMYS SCRIPTA)EVOLUTION, Issue 3 2006Erin M. Myers Abstract Shape variation is widespread in nature and embodies both a response to and a source for evolution and natural selection. To detect patterns of shape evolution, one must assess the quantitative genetic underpinnings of shape variation as well as the selective environment that the organisms have experienced. Here we used geometric morphometrics to assess variation in plastron shell shape in 1314 neonatal slider turtles (Trachemys scripta) from 162 clutches of laboratory-incubated eggs from two nesting areas. Multivariate analysis of variance indicated that nesting area has a limited role in describing plastron shape variation among clutches, whereas differences between individual clutches were highly significant, suggesting a prominent clutch effect. The covariation between plastron shape and several possible maternal effect variables (yolk hormone levels and egg dimensions) was assessed for a subset of clutches and found to be negligible. We subsequently employed several recently proposed methods for estimating heritability from shape variables, and generalized a univariate approach to accommodate unequal sample sizes. Univariate estimates of shape heritability based on Procrustes distances yielded large values for both nesting populations (h2, 0.86), and multivariate estimates of maximal additive heritability were also large for both nesting populations (h2max, 0.57). We also estimated the dominant trend in heritable shape change for each nesting population and found that the direction of shape evolution was not the same for the two sites. Therefore, although the magnitude of shape evolution was similar between nesting populations, the manner in which plastron shape is evolving is not. We conclude that the univariate approach for assessing quantitative genetic parameters from geometric morphometric data has limited utility, because it is unable to accurately describe how shape is evolving. [source] MULTIVARIATE QUANTITATIVE GENETICS AND THE LEK PARADOX: GENETIC VARIANCE IN MALE SEXUALLY SELECTED TRAITS OF DROSOPHILA SERRATA UNDER FIELD CONDITIONSEVOLUTION, Issue 12 2004Emma Hine Abstract Single male sexually selected traits have been found to exhibit substantial genetic variance, even though natural and sexual selection are predicted to deplete genetic variance in these traits. We tested whether genetic variance in multiple male display traits of Drosophila serrata was maintained under field conditions. A breeding design involving 300 field-reared males and their laboratory-reared offspring allowed the estimation of the genetic variance-covariance matrix for six male cuticular hydrocarbons (CHCs) under field conditions. Despite individual CHCs displaying substantial genetic variance under field conditions, the vast majority of genetic variance in CHCs was not closely associated with the direction of sexual selection measured on field phenotypes. Relative concentrations of three CHCs correlated positively with body size in the field, but not under laboratory conditions, suggesting condition-dependent expression of CHCs under field conditions. Therefore condition dependence may not maintain genetic variance in preferred combinations of male CHCs under field conditions, suggesting that the large mutational target supplied by the evolution of condition dependence may not provide a solution to the lek paradox in this species. Sustained sexual selection may be adequate to deplete genetic variance in the direction of selection, perhaps as a consequence of the low rate of favorable mutations expected in multiple trait systems. [source] QUANTITATIVE GENETICS OF SEXUAL PLASTICITY: THE ENVIRONMENTAL THRESHOLD MODEL AND GENOTYPE-BY-ENVIRONMENT INTERACTION FOR PHALLUS DEVELOPMENT IN THE SNAIL BULINUS TRUNCATUSEVOLUTION, Issue 5 2000Marie-France Ostrowski Abstract Sexual polymorphisms are model systems for analyzing the evolution of reproductive strategies. However, their plasticity and other binary traits have rarely been studied, with respect to environmental variables. A possible reason is that, although threshold models offer an adequate quantitative genetics framework for binary traits in a single environment, analyzing their plasticity requires more refined empirical and theoretical approaches. The statistical framework proposed here, based on the environmental threshold model (ETM), should partially fill this gap. This methodology is applied to an empirical dataset on a plastic sexual polymorphism, aphally, in the snail Bulinus truncatus. Aphally is characterized by the co-occurrence of regular hermaphrodites (euphallics) together with hermaphrodites deprived of the male copulatory organ (aphallics). Reaction norms were determined for 40 inbred lines, distributed at three temperatures, in a first experiment. A second experiment allowed us to rule out maternal effects. We confirmed the existence of high broad-sense heritabilities as well as a positive effect of high temperatures on aphally. However a significant genotype-by-environment interaction was detected for the first time, suggesting that sexual plasticity itself can respond to selection. A nested series of four ETM-like models was developed for estimating genetical effects on both mean aphally rate and plasticity. These models were tested using a maximum-likelihood procedure and fitted to aphally data. Although no perfect fit of models to data was observed, the refined versions of ETM models conveniently reduce the analysis of complex reaction norms of binary traits into standard quantitative genetics parameters, such as genetic values and environmental variances. [source] PRELIMINARY ANALYSIS OF QUANTITATIVE GENETICS AND PHENOTYPIC PLASTICITY IN AULACOSEIRA SUBARCTICA (BACILLAR-IOPHYTA)JOURNAL OF PHYCOLOGY, Issue 2000S. M. Edgar Several clones of Aulacoseira subarctica were isolated from Yellowstone, Lewis, and East Rosebud Lakes (Montana, Wyoming). Two to four clones from each lake were grown in batch cultures under three light intensities, 2, 11.4 and 115 ,E m,2 s,1. Clones were conditioned to their light environment for a three-week period. Inoculants from the conditioned clones taken during log phase of growth, were grown until in log phase, then samples were collected. Five randomly chosen valves for 2 replicates of each clone were examined using a scanning electron microscope and captured on film at a magnification of 20,000x. Each image was digitized and quantitative morphometric characters were measured. A preliminary quantitative genetic analysis was performed on selected characters within each light environment. Plasticity of characters within clones across the three light regimes were also examined. The amount of variability found within characters in A. subarctica will be discussed in terms of environmental, genetic, and microenvironmental sources. [source] A CENTENNIAL CELEBRATION FOR QUANTITATIVE GENETICSEVOLUTION, Issue 5 2007Derek A. Roff Quantitative genetics is at or is fast approaching its centennial. In this perspective I consider five current issues pertinent to the application of quantitative genetics to evolutionary theory. First, I discuss the utility of a quantitative genetic perspective in describing genetic variation at two very different levels of resolution, (1) in natural, free-ranging populations and (2) to describe variation at the level of DNA transcription. Whereas quantitative genetics can serve as a very useful descriptor of genetic variation, its greater usefulness is in predicting evolutionary change, particularly when used in the first instance (wild populations). Second, I review the contributions of Quantitative trait loci (QLT) analysis in determining the number of loci and distribution of their genetic effects, the possible importance of identifying specific genes, and the ability of the multivariate breeder's equation to predict the results of bivariate selection experiments. QLT analyses appear to indicate that genetic effects are skewed, that at least 20 loci are generally involved, with an unknown number of alleles, and that a few loci have major effects. However, epistatic effects are common, which means that such loci might not have population-wide major effects: this question waits upon (QTL) analyses conducted on more than a few inbred lines. Third, I examine the importance of research into the action of specific genes on traits. Although great progress has been made in identifying specific genes contributing to trait variation, the high level of gene interactions underlying quantitative traits makes it unlikely that in the near future we will have mechanistic models for such traits, or that these would have greater predictive power than quantitative genetic models. In the fourth section I present evidence that the results of bivariate selection experiments when selection is antagonistic to the genetic covariance are frequently not well predicted by the multivariate breeder's equation. Bivariate experiments that combine both selection and functional analyses are urgently needed. Finally, I discuss the importance of gaining more insight, both theoretical and empirical, on the evolution of the G and P matrices. [source] FROM MICRO- TO MACROEVOLUTION THROUGH QUANTITATIVE GENETIC VARIATION: POSITIVE EVIDENCE FROM FIELD CRICKETSEVOLUTION, Issue 10 2004Mattieu Bégin Abstract . -Quantitative genetics has been introduced to evolutionary biologists with the suggestion that microevolution could be directly linked to macroevolutionary patterns using, among other parameters, the additive genetic variance/ covariance matrix (G) which is a statistical representation of genetic constraints to evolution. However, little is known concerning the rate and pattern of evolution of G in nature, and it is uncertain whether the constraining effect of G is important over evolutionary time scales. To address these issues, seven species of field crickets from the genera Gryllus and Teleogryllus were reared in the laboratory, and quantitative genetic parameters for morphological traits were estimated from each of them using a nested full-sibling family design. We used three statistical approaches (T method, Flury hierarchy, and Mantel test) to compare G matrices or genetic correlation matrices in a phylogenetic framework. Results showed that G matrices were generally similar across species, with occasional differences between some species. We suggest that G has evolved at a low rate, a conclusion strengthened by the consideration that part of the observed across-species variation in G can be explained by the effect of a genotype by environment interaction. The observed pattern of G matrix variation between species could not be predicted by either morphological trait values or phylogeny. The constraint hypothesis was tested by comparing the multivariate orientation of the reconstructed ancestral G matrix to the orientation of the across-species divergence matrix (D matrix, based on mean trait values). The D matrix mainly revealed divergence in size and, to a much smaller extent, in a shape component related to the ovipositor length. This pattern of species divergence was found to be predictable from the ancestral G matrix in agreement with the expectation of the constraint hypothesis. Overall, these results suggest that the G matrix seems to have an influence on species divergence, and that macroevolution can be predicted, at least qualitatively, from quantitative genetic theory. Alternative explanations are discussed. [source] EASY AND FLEXIBLE BAYESIAN INFERENCE OF QUANTITATIVE GENETIC PARAMETERSEVOLUTION, Issue 6 2009Patrik Waldmann There has been a tremendous advancement of Bayesian methodology in quantitative genetics and evolutionary biology. Still, there are relatively few publications that apply this methodology, probably because the availability of multipurpose and user-friendly software is somewhat limited. It is here described how only a few rows of code of the well-developed and very flexible Bayesian software WinBUGS (Lunn et al. 2000) can be used for inference of the additive polygenic variance and heritabilty in pedigrees of general design. The presented code is illustrated by application to an earlier published dataset of Scots pine. [source] A CENTENNIAL CELEBRATION FOR QUANTITATIVE GENETICSEVOLUTION, Issue 5 2007Derek A. Roff Quantitative genetics is at or is fast approaching its centennial. In this perspective I consider five current issues pertinent to the application of quantitative genetics to evolutionary theory. First, I discuss the utility of a quantitative genetic perspective in describing genetic variation at two very different levels of resolution, (1) in natural, free-ranging populations and (2) to describe variation at the level of DNA transcription. Whereas quantitative genetics can serve as a very useful descriptor of genetic variation, its greater usefulness is in predicting evolutionary change, particularly when used in the first instance (wild populations). Second, I review the contributions of Quantitative trait loci (QLT) analysis in determining the number of loci and distribution of their genetic effects, the possible importance of identifying specific genes, and the ability of the multivariate breeder's equation to predict the results of bivariate selection experiments. QLT analyses appear to indicate that genetic effects are skewed, that at least 20 loci are generally involved, with an unknown number of alleles, and that a few loci have major effects. However, epistatic effects are common, which means that such loci might not have population-wide major effects: this question waits upon (QTL) analyses conducted on more than a few inbred lines. Third, I examine the importance of research into the action of specific genes on traits. Although great progress has been made in identifying specific genes contributing to trait variation, the high level of gene interactions underlying quantitative traits makes it unlikely that in the near future we will have mechanistic models for such traits, or that these would have greater predictive power than quantitative genetic models. In the fourth section I present evidence that the results of bivariate selection experiments when selection is antagonistic to the genetic covariance are frequently not well predicted by the multivariate breeder's equation. Bivariate experiments that combine both selection and functional analyses are urgently needed. Finally, I discuss the importance of gaining more insight, both theoretical and empirical, on the evolution of the G and P matrices. [source] THE MUTATION MATRIX AND THE EVOLUTION OF EVOLVABILITYEVOLUTION, Issue 4 2007Adam G. Jones Evolvability is a key characteristic of any evolving system, and the concept of evolvability serves as a unifying theme in a wide range of disciplines related to evolutionary theory. The field of quantitative genetics provides a framework for the exploration of evolvability with the promise to produce insights of global importance. With respect to the quantitative genetics of biological systems, the parameters most relevant to evolvability are the G -matrix, which describes the standing additive genetic variances and covariances for a suite of traits, and the M -matrix, which describes the effects of new mutations on genetic variances and covariances. A population's immediate response to selection is governed by the G -matrix. However, evolvability is also concerned with the ability of mutational processes to produce adaptive variants, and consequently the M -matrix is a crucial quantitative genetic parameter. Here, we explore the evolution of evolvability by using analytical theory and simulation-based models to examine the evolution of the mutational correlation, r,, the key parameter determining the nature of genetic constraints imposed by M. The model uses a diploid, sexually reproducing population of finite size experiencing stabilizing selection on a two-trait phenotype. We assume that the mutational correlation is a third quantitative trait determined by multiple additive loci. An individual's value of the mutational correlation trait determines the correlation between pleiotropic effects of new alleles when they arise in that individual. Our results show that the mutational correlation, despite the fact that it is not involved directly in the specification of an individual's fitness, does evolve in response to selection on the bivariate phenotype. The mutational variance exhibits a weak tendency to evolve to produce alignment of the M -matrix with the adaptive landscape, but is prone to erratic fluctuations as a consequence of genetic drift. The interpretation of this result is that the evolvability of the population is capable of a response to selection, and whether this response results in an increase or decrease in evolvability depends on the way in which the bivariate phenotypic optimum is expected to move. Interestingly, both analytical and simulation results show that the mutational correlation experiences disruptive selection, with local fitness maxima at ,1 and +1. Genetic drift counteracts the tendency for the mutational correlation to persist at these extreme values, however. Our results also show that an evolving M -matrix tends to increase stability of the G -matrix under most circumstances. Previous studies of G -matrix stability, which assume nonevolving M -matrices, consequently may overestimate the level of instability of G relative to what might be expected in natural systems. Overall, our results indicate that evolvability can evolve in natural systems in a way that tends to result in alignment of the G -matrix, the M -matrix, and the adaptive landscape, and that such evolution tends to stabilize the G -matrix over evolutionary time. [source] AN EXACT FORM OF THE BREEDER'S EQUATION FOR THE EVOLUTION OF A QUANTITATIVE TRAIT UNDER NATURAL SELECTIONEVOLUTION, Issue 11 2005John S. Heywood Abstract Starting with the Price equation, I show that the total evolutionary change in mean phenotype that occurs in the presence of fitness variation can be partitioned exactly into five components representing logically distinct processes. One component is the linear response to selection, as represented by the breeder's equation of quantitative genetics, but with heritability defined as the linear regression coefficient of mean offspring phenotype on parent phenotype. The other components are identified as constitutive transmission bias, two types of induced transmission bias, and a spurious response to selection caused by a covariance between parental fitness and offspring phenotype that cannot be predicted from parental phenotypes. The partitioning can be accomplished in two ways, one with heritability measured before (in the absence of) selection, and the other with heritability measured after (in the presence of) selection. Measuring heritability after selection, though unconventional, yields a representation for the linear response to selection that is most consistent with Darwinian evolution by natural selection because the response to selection is determined by the reproductive features of the selected group, not of the parent population as a whole. The analysis of an explicitly Mendelian model shows that the relative contributions of the five terms to the total evolutionary change depends on the level of organization (gene, individual, or mated pair) at which the parent population is divided into phenotypes, with each frame of reference providing unique insight. It is shown that all five components of phenotypic evolution will generally have nonzero values as a result of various combinations of the normal features of Mendelian populations, including biparental sex, allelic dominance, inbreeding, epistasis, linkage disequilibrium, and environmental covariances between traits. Additive genetic variance can be a poor predictor of the adaptive response to selection in these models. The narrow-sense heritability s,2A/s,2P should be viewed as an approximation to the offspring-parent linear regression rather than the other way around. [source] COMPARING STRENGTHS OF DIRECTIONAL SELECTION: HOW STRONG IS STRONG?EVOLUTION, Issue 10 2004Joe Hereford Abstract The fundamental equation in evolutionary quantitative genetics, the Lande equation, describes the response to directional selection as a product of the additive genetic variance and the selection gradient of trait value on relative fitness. Comparisons of both genetic variances and selection gradients across traits or populations require standardization, as both are scale dependent. The Lande equation can be standardized in two ways. Standardizing by the variance of the selected trait yields the response in units of standard deviation as the product of the heritability and the variance-standardized selection gradient. This standardization conflates selection and variation because the phenotypic variance is a function of the genetic variance. Alternatively, one can standardize the Lande equation using the trait mean, yielding the proportional response to selection as the product of the squared coefficient of additive genetic variance and the mean-standardized selection gradient. Mean-standardized selection gradients are particularly useful for summarizing the strength of selection because the mean-standardized gradient for fitness itself is one, a convenient benchmark for strong selection. We review published estimates of directional selection in natural populations using mean-standardized selection gradients. Only 38 published studies provided all the necessary information for calculation of mean-standardized gradients. The median absolute value of multivariate mean-standardized gradients shows that selection is on average 54% as strong as selection on fitness. Correcting for the upward bias introduced by taking absolute values lowers the median to 31%, still very strong selection. Such large estimates clearly cannot be representative of selection on all traits. Some possible sources of overestimation of the strength of selection include confounding environmental and genotypic effects on fitness, the use of fitness components as proxies for fitness, and biases in publication or choice of traits to study. [source] EVOLUTION AND STABILITY OF THE G-MATRIX ON A LANDSCAPE WITH A MOVING OPTIMUMEVOLUTION, Issue 8 2004Adam G. Jones Abstract In quantitative genetics, the genetic architecture of traits, described in terms of variances and covariances, plays a major role in determining the trajectory of evolutionary change. Hence, the genetic variance-covariance matrix (G-matrix) is a critical component of modern quantitative genetics theory. Considerable debate has surrounded the issue of G-matrix constancy because unstable G-matrices provide major difficulties for evolutionary inference. Empirical studies and analytical theory have not resolved the debate. Here we present the results of stochastic models of G-matrix evolution in a population responding to an adaptive landscape with an optimum that moves at a constant rate. This study builds on the previous results of stochastic simulations of G-matrix stability under stabilizing selection arising from a stationary optimum. The addition of a moving optimum leads to several important new insights. First, evolution along genetic lines of least resistance increases stability of the orientation of the G-matrix relative to stabilizing selection alone. Evolution across genetic lines of least resistance decreases G-matrix stability. Second, evolution in response to a continuously changing optimum can produce persistent maladaptation for a correlated trait, even if its optimum does not change. Third, the retrospective analysis of selection performs very well when the mean G-matrix (,) is known with certainty, indicating that covariance between G and the directional selection gradient (3 is usually small enough in magnitude that it introduces only a small bias in estimates of the net selection gradient. Our results also show, however, that the contemporary ,-matrix only serves as a rough guide to ,. The most promising approach for the estimation of G is probably through comparative phylogenetic analysis. Overall, our results show that directional selection actually can increase stability of the G-matrix and that retrospective analysis of selection is inherently feasible. One ?riajor remaining challenge is to gain a sufficient understanding of the G-matrix to allow the confident estimation of ,. [source] Single QTL mapping and nucleotide-level resolution of a physiologic trait in wine Saccharomyces cerevisiae strainsFEMS YEAST RESEARCH, Issue 6 2007Philippe Marullo Abstract Natural Saccharomyces cerevisiae yeast strains exhibit very large genotypic and phenotypic diversity. However, the link between phenotype variation and genetic determinism is still difficult to identify, especially in wild populations. Using genome hybridization on DNA microarrays, it is now possible to identify single-feature polymorphisms among divergent yeast strains. This tool offers the possibility of applying quantitative genetics to wild yeast strains. In this instance, we studied the genetic basis for variations in acetic acid production using progeny derived from two strains from grape must isolates. The trait was quantified during alcoholic fermentation of the two strains and 108 segregants derived from their crossing. A genetic map of 2212 markers was generated using oligonucleotide microarrays, and a major quantitative trait locus (QTL) was mapped with high significance. Further investigations showed that this QTL was due to a nonsynonymous single-nucleotide polymorphism that targeted the catalytic core of asparaginase type I (ASP1) and abolished its activity. This QTL was only effective when asparagine was used as a major nitrogen source. Our results link nitrogen assimilation and CO2 production rate to acetic acid production, as well as, on a broader scale, illustrating the specific problem of quantitative genetics when working with nonlaboratory microorganisms. [source] Bayesian approaches in evolutionary quantitative geneticsJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 4 2008R. B. O'HARA Abstract The study of evolutionary quantitative genetics has been advanced by the use of methods developed in animal and plant breeding. These methods have proved to be very useful, but they have some shortcomings when used in the study of wild populations and evolutionary questions. Problems arise from the small size of data sets typical of evolutionary studies, and the additional complexity of the questions asked by evolutionary biologists. Here, we advocate the use of Bayesian methods to overcome these and related problems. Bayesian methods naturally allow errors in parameter estimates to propagate through a model and can also be written as a graphical model, giving them an inherent flexibility. As packages for fitting Bayesian animal models are developed, we expect the application of Bayesian methods to evolutionary quantitative genetics to grow, particularly as genomic information becomes more and more associated with environmental data. [source] Costs of resistance: genetic correlations and potential trade-offs in an insect immune SystemJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 2 2004S. C. Cotter Abstract Theory predicts that natural selection will erode additive genetic variation in fitness-related traits. However, numerous studies have found considerable heritable variation in traits related to immune function, which should be closely linked to fitness. This could be due to trade-offs maintaining variation in these traits. We used the Egyptian cotton leafworm, Spodoptera littoralis, as a model system to examine the quantitative genetics of insect immune function. We estimated the heritabilities of several different measures of innate immunity and the genetic correlations between these immune traits and a number of life history traits. Our results provide the first evidence for a potential genetic trade-off within the insect immune system, with antibacterial activity (lysozyme-like) exhibiting a significant negative genetic correlation with haemocyte density, which itself is positively genetically correlated with both haemolymph phenoloxidase activity and cuticular melanization. We speculate on a potential trade-off between defence against parasites and predators, mediated by larval colour, and its role in maintaining genetic variation in traits under natural selection. [source] Quantitative genetics parameters show partial independent evolutionary potential for body mass and metabolism in stonechats from different populationsJOURNAL OF ZOOLOGY, Issue 2 2009B. I. Tieleman Abstract Phenotypic variation in physiological traits, such as energy metabolism, is commonly subjected to adaptive interpretations, but little is known about the heritable basis or genetic correlations among physiological traits in non-domesticated species. Basal metabolic rate (BMR) and body mass are related in complex ways. We studied the quantitative genetics of BMR, residual BMR (on body mass), mass-specific BMR and body mass of stonechats originating from four different populations and bred in captivity. Heritabilities ranged from 0.2 to 0.7. The genetic variance,covariance structure implied that BMR, mass-specific BMR and body mass can in part evolve independently of each other, because we found genetic correlations deviating significantly from one and minus one. BMR, mass-specific BMR and body mass further differed among populations at the phenotypic level; differences in the genetic correlation among populations are discussed. [source] Are QST,FST comparisons for natural populations meaningful?MOLECULAR ECOLOGY, Issue 22 2008B. PUJOL Abstract Comparisons between putatively neutral genetic differentiation amongst populations, FST, and quantitative genetic variation, QST, are increasingly being used to test for natural selection. However, we find that approximately half of the comparisons that use only data from wild populations confound phenotypic and genetic variation. We urge the use of a clear distinction between narrow-sense QST, which can be meaningfully compared with FST, and phenotypic divergence measured between populations, PST, which is inadequate for comparisons in the wild. We also point out that an unbiased estimate of QST can be found using the so-called ,animal model' of quantitative genetics. [source] Inclusive heritability: combining genetic and non-genetic information to study animal behavior and cultureOIKOS, Issue 2 2010Étienne Danchin Phenotypic variance results from variation in biological information possessed by individuals. Quantitative geneticists often strive to partition out all environmental variance to measure heritability. Behavioral biologists and ecologists however, require methods to integrate genetic and environmental components of inherited phenotypic variance in order to estimate the evolutionary potential of traits, which encompasses any form of information that is inherited. To help develop this integration, we build on the tools of quantitative genetics and offer the concept of ,inclusive heritability' which identifies and unifies the various mechanisms of information transmission across generations. A controversial component of non-genetic information is animal culture, which is the part of phenotypic variance inherited through social learning. Culture has the unique property of being transmitted horizontally and obliquely, as well as vertically. Accounting for cultural variation would allow us to examine a broader range of evolutionary mechanisms. Culture may, for instance, produce behavioral isolating mechanisms leading to speciation. To advance the study of animal culture, we offer a definition of culture that is rooted in quantitative genetics. We also offer four testable criteria to determine whether a trait is culturally inherited. These criteria may constitute a conceptual tool to study animal culture. We briefly discuss methods to partition out cultural variance. Several authors have recently called for ,modernizing the modern synthesis' by including non-genetic factors such as epigenetics and phenotypic plasticity in order to more fully explain phenotypic evolution. Here, we further propose to broaden the concept of inheritance by incorporating the cultural component of behavior. Applying the concept of inclusive heritability may advance the integration of multiple forms of inheritance into the study of evolution. [source] Genetic parameters and QTL analysis of ,13C and ring width in maritime pinePLANT CELL & ENVIRONMENT, Issue 8 2002O. Brendel Abstract Classical quantitative genetics and quantitative trait dissection analysis (QTL) approaches were used in order to investigate the genetic determinism of wood cellulose carbon isotope composition (,13C, a time integrated estimate of water use efficiency) and of diameter growth and their relationship on adult trees (15 years) of a forest tree species (maritime pine). A half diallel experimental set-up was used to (1) estimate heritabilities for ,13C and ring width and (2) to decompose the phenotypic ,13C/growth correlation into its genetic and environmental components. Considerable variation was found for ,13C (range of over 3,) and for ring width (range of over 5 mm) and significant heritabilities (narrow sense 0ˇ17/0ˇ19 for ,13C and ring width, respectively, 100% additivity). The significant phenotypic correlation between ,13C and ring width was not determined by the genetic component, but was attributable to environmental components. Using a genetic linkage map of a full-sib family, four significant and four suggestive QTLs were detected for ,13C, the first for ,13C in a forest tree species, as far as known to the authors. Two significant and four suggestive QTLs were found for ring width. No co-location of QTLs was found between ,13C and growth. [source] Mechanisms of Regulation of Litter Size in Pigs on the Genome LevelREPRODUCTION IN DOMESTIC ANIMALS, Issue 2007O Distl Contents Improvement in litter size has become of great interest in pig industry as good fecundity is directly related to a sow's productive life. Genetic regulation of litter size is complex and the main component traits so far defined are ovulation rate, embryonic survival, uterus capacity, foetal survival and pre-weaning losses. Improvements using concepts of the quantitative genetics let expect only slow genetic progress due to its low heritability of approximately 0.09 for number of piglets born alive. Marker assisted selection allows to dissect litter size in its component traits and using molecular genetic markers for the components of litter size traits promises more progress and advantages in optimum balancing of the different physiological mechanisms influencing litter size. In this review, efforts being made to unravel the genetic determinants of litter size are accounted and discussed. For litter size traits, more than 50 quantitative trait loci (QTL) were mapped and in more than 12 candidate genes associations confirmed. The number of useful candidate genes is much larger as shown by expression profiles and in addition, much more QTL can be assumed. These functional genomic approaches, both QTL mapping and candidate gene analysis, have to be merged for a better understanding of a wider application across different pig breeds and lines. Newly developed tools based on microarray techniques comprising DNA variants or expressed tags of many genes or even the whole genome appear useful for in depth understanding of the genetics of litter size in pigs. [source] Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial DatasetsBIOMETRICS, Issue 2 2009Andrew O. Finley Summary This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large spatial datasets are, however, computationally infeasible because of cubic-order matrix algorithms involved in estimation. The situation is even worse in Markov chain Monte Carlo (MCMC) contexts where such computations are performed for several iterations. Here, we discuss approaches that help obviate these hurdles without sacrificing the richness in modeling. For genetic effects, we demonstrate how an initial spectral decomposition of the relationship matrices negate the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects, we outline two approaches for circumventing the prohibitively expensive matrix decompositions: the first leverages analytical results from Ornstein,Uhlenbeck processes that yield computationally efficient tridiagonal structures, whereas the second derives a modified predictive process model from the original model by projecting its realizations to a lower-dimensional subspace, thereby reducing the computational burden. We illustrate the proposed methods using a synthetic dataset with additive, dominance, genetic effects and anisotropic spatial residuals, and a large dataset from a Scots pine (Pinus sylvestris L.) progeny study conducted in northern Sweden. Our approaches enable us to provide a comprehensive analysis of this large trial, which amply demonstrates that, in addition to violating basic assumptions of the linear model, ignoring spatial effects can result in downwardly biased measures of heritability. [source] | |||||||||||||