Univariate Traits Research Articles

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture.

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.

Adaptive divergence in bill morphology and other thermoregulatory traits is facilitated by restricted gene flow in song sparrows on the California Channel Islands.

Disentangling the effects of neutral and adaptive processes in maintaining phenotypic variation across environmental gradients is challenging in natural populations. Song sparrows (Melospiza melodia) on the California Channel Islands occupy a pronounced east-west climate gradient within a small spatial scale, providing a unique opportunity to examine the interaction of genetic isolation (reduced gene flow) and the environment (selection) in driving variation. We used reduced representation genomic libraries to infer the role of neutral processes (drift and restricted gene flow) and divergent selection in driving variation in thermoregulatory traits with an emphasis on the mechanisms that maintain bill divergence among islands. Analyses of 22,029 neutral SNPs confirm distinct population structure by island with restricted gene flow and relatively large effective population sizes, suggesting bill differences are probably not a product of genetic drift. Instead, we found strong support for local adaptation using 3294 SNPs in differentiation-based and environmental association analyses coupled with genome-wide association tests. Specifically, we identified several putatively adaptive and candidate loci in or near genes involved in bill development pathways (e.g., BMP, CaM, Wnt), confirming the highly complex and polygenic architecture underlying bill morphology. Furthermore, we found divergence in genes associated with other thermoregulatory traits (i.e., feather structure, plumage colour, and physiology). Collectively, these results suggest strong divergent selection across an island archipelago results in genomic changes in a suite of traits associated with climate adaptation over small spatial scales. Future research should move beyond studying univariate traits to better understand multidimensional responses to complex environmental conditions.

The Intersection of the Genetic Architectures of Orofacial Clefts and Normal Facial Variation.

Unaffected relatives of individuals with non-syndromic cleft lip with or without cleft palate (NSCL/P) show distinctive facial features. The presence of this facial endophenotype is potentially an expression of underlying genetic susceptibility to NSCL/P in the larger unselected population. To explore this hypothesis, we first partitioned the face into 63 partially overlapping regions representing global-to-local facial morphology and then defined endophenotypic traits by contrasting the 3D facial images from 264 unaffected parents of individuals with NSCL/P versus 3,171 controls. We observed distinct facial features between parents and controls across 59 global-to-local facial segments at nominal significance (p ≤ 0.05) and 52 segments at Bonferroni corrected significance (p < 1.2 × 10–3), respectively. Next, we quantified these distinct facial features as univariate traits in another dataset of 8,246 unaffected European individuals and performed a genome-wide association study. We identified 29 independent genetic loci that were associated (p < 5 × 10–8) with at least one of the tested endophenotypic traits, and nine genetic loci also passed the study-wide threshold (p < 8.47 × 10–10). Of the 29 loci, 22 were in proximity of loci previously associated with normal facial variation, 18 were near genes that show strong evidence in orofacial clefting (OFC), and another 10 showed some evidence in OFC. Additionally, polygenic risk scores for NSCL/P showed associations with the endophenotypic traits. This study thus supports the hypothesis of a shared genetic architecture of normal facial development and OFC.

Mosaic patterns of homoplasy accompany the parallel evolution of suspensory adaptations in the forelimb of tree sloths (Folivora: Xenarthra)

AbstractWe examine how derived functional signal and phylogenetic inheritance interact in the forelimb of tree sloths, to understand the relative contribution of each in the evolution of a novel morphobehavioural suite. Molecular and craniodental data demonstrate that extant tree sloths evolved suspensory behaviours and associated morphologies from a non-suspensory ancestor independently of one another, making them a useful model system. We find that convergence in univariate traits is expressed mosaically, although the signal is largely functional. Three-dimensional analyses suggest there is greater conservatism of gross morphology in more proximal bones than in more distal elements. Convergence in some univariate scapular traits is independent of the gross morphology of the scapula itself, demonstrating that functionally relevant morphologies were mapped on to a more conserved scapular shape. Our results suggest that morphological homoplasy is expressed in a mosaic manner. The relationship between homoplasy and trait integration may be more nuanced than previously thought, even within a single adaptive system.

Repeatability of Adaptive Radiation Depends on Spatial Scale: Regional Versus Global Replicates of Stickleback in Lake Versus Stream Habitats.

The repeatability of adaptive radiation is expected to be scale-dependent, with determinism decreasing as greater spatial separation among "replicates" leads to their increased genetic and ecological independence. Threespine stickleback (Gasterosteus aculeatus) provide an opportunity to test whether this expectation holds for the early stages of adaptive radiation-their diversification in freshwater ecosystems has been replicated many times. To better understand the repeatability of that adaptive radiation, we examined the influence of geographic scale on levels of parallel evolution by quantifying phenotypic and genetic divergence between lake and stream stickleback pairs sampled at regional (Vancouver Island) and global (North America and Europe) scales. We measured phenotypes known to show lake-stream divergence and used reduced representation genome-wide sequencing to estimate genetic divergence. We assessed the scale dependence of parallel evolution by comparing effect sizes from multivariate models and also the direction and magnitude of lake-stream divergence vectors. At the phenotypic level, parallelism was greater at the regional than the global scale. At the genetic level, putative selected loci showed greater lake-stream parallelism at the regional than the global scale. Generally, the level of parallel evolution was low at both scales, except for some key univariate traits. Divergence vectors were often orthogonal, highlighting possible ecological and genetic constraints on parallel evolution at both scales. Overall, our results confirm that the repeatability of adaptive radiation decreases at increasing spatial scales. We suggest that greater environmental heterogeneity at larger scales imposes different selection regimes, thus generating lower repeatability of adaptive radiation at larger spatial scales.

Realistic scenarios of missing taxa in phylogenetic comparative methods and their effects on model selection and parameter estimation.

Model-based analyses of continuous trait evolution enable rich evolutionary insight. These analyses require a phylogenetic tree and a vector of trait values for the tree’s terminal taxa, but rarely do a tree and dataset include all taxa within a clade. Because the probability that a taxon is included in a dataset depends on ecological traits that have phylogenetic signal, missing taxa in real datasets should be expected to be phylogenetically clumped or correlated to the modelled trait. I examined whether those types of missing taxa represent a problem for model selection and parameter estimation. I simulated univariate traits under a suite of Brownian Motion and Ornstein-Uhlenbeck models, and assessed the performance of model selection and parameter estimation under absent, random, clumped or correlated missing taxa. I found that those analyses perform well under almost all scenarios, including situations with very sparsely sampled phylogenies. The only notable biases I detected were in parameter estimation under a very high percentage (90%) of correlated missing taxa. My results offer a degree of reassurance for studies of continuous trait evolution with missing taxa, but the problem of missing taxa in phylogenetic comparative methods still demands much further investigation. The framework I have described here might provide a starting point for future work.

Femoral morphology of sciuromorph rodents in light of scaling and locomotor ecology.

Sciuromorph rodents are a monophyletic group comprising about 300 species with a body mass range spanning three orders of magnitude and various locomotor behaviors that we categorized into arboreal, fossorial and aerial. The purpose of this study was to investigate how the interplay of locomotor ecology and body mass affects the morphology of the sciuromorph locomotor apparatus. The most proximal skeletal element of the hind limb, i.e. thefemur, was selected, because it was shown to reflect a functional signal in various mammalian taxa. We analyzed univariate traits (effective femoral length, various robustness variables and the in-levers of the muscles attaching to the greater, third and lesser trochanters) as well as femoral shape, representing a multivariate trait. An ordinary least-squares regression including 177 species was used to test for a significant interaction effect between body mass and locomotor ecology on the variables. Specifically, it tested whether the scaling patterns of the fossorial and aerial groups differ when compared with the arboreal, because the latter was identified as the ancestral sciuromorph condition via stochastic character mapping. We expected aerial species to display the highest trait values for a given body mass as well as the steepest slopes, followed by the arboreal and fossorial species along this order. An Ornstein-Uhlenbeck regression fitted to a phylogenetically pruned dataset of 140 species revealed the phylogenetic inertia to be very low in the univariate traits, hence justifying the utilization of standard regressions. These variables generally scaled close to isometry, suggesting that scaling adjustments might not have played a major role for most of the femoral features. Nevertheless, the low phylogenetic inertia indicates that the observed scaling patterns needed to be maintained during sciuromorph evolution. Significant interaction effects were discovered in the femoral length, the centroid size of the condyles, and the in-levers of the greater and third trochanters. Additionally, adjustments in various femoral traits reflect the acquisitions of fossorial and aerial behaviors from arboreal ancestors. Using sciuromorphs as a focal clade, our findings exemplify the importance of statistically accounting for potential interaction effects of different environmental factors in studies relating morphology to ecology.

Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera.

Plant canopy structure can strongly affect crop functions such as yield and stress tolerance, and canopy size is an important aspect of canopy structure. Manual assessment of canopy size is laborious and imprecise, and cannot measure multi-dimensional traits such as projected leaf area and canopy volume. Field-based high throughput phenotyping systems with imaging capabilities can rapidly acquire data about plants in field conditions, making it possible to quantify and monitor plant canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze cotton canopy development in field conditions. A cotton field was planted with 128 plots, including four genotypes of 32 plots each. The field was scanned by GPhenoVision (a customized field-based high throughput phenotyping system) to acquire color and depth images with GPS information in 2016 covering two growth stages: canopy development, and flowering and boll development. A data processing pipeline was developed, consisting of three steps: plot point cloud reconstruction, plant canopy segmentation, and trait extraction. Plot point clouds were reconstructed using color and depth images with GPS information. In colorized point clouds, vegetation was segmented from the background using an excess-green (ExG) color filter, and cotton canopies were further separated from weeds based on height, size, and position information. Static morphological traits were extracted on each day, including univariate traits (maximum and mean canopy height and width, projected canopy area, and concave and convex volumes) and a multivariate trait (cumulative height profile). Growth rates were calculated for univariate static traits, quantifying canopy growth and development. Linear regressions were performed between the traits and fiber yield to identify the best traits and measurement time for yield prediction. The results showed that fiber yield was correlated with static traits after the canopy development stage (R2 = 0.35–0.71) and growth rates in early canopy development stages (R2 = 0.29–0.52). Multi-dimensional traits (e.g., projected canopy area and volume) outperformed one-dimensional traits, and the multivariate trait (cumulative height profile) outperformed univariate traits. The proposed approach would be useful for identification of quantitative trait loci (QTLs) controlling canopy size in genetics/genomics studies or for fiber yield prediction in breeding programs and production environments.

Do community-weighted mean functional traits reflect optimal strategies?

The notion that relationships between community-weighted mean (CWM) traits (i.e. plot-level trait values weighted by species abundances) and environmental conditions reflect selection towards locally optimal phenotypes is challenged by the large amount of interspecific trait variation typically found within ecological communities. Reconciling these contrasting patterns is a key to advancing predictive theories of functional community ecology. We combined data on geographical distributions and three traits (wood density, leaf mass per area and maximum height) of 173 tree species in Puerto Rico. We tested the hypothesis that species are more likely to occur where their trait values are more similar to the local CWM trait values (the'CWM-optimality' hypothesis) by comparing species occurrence patterns (as a proxy for fitness) with the functional composition of forest plots across a precipitation gradient. While 70% of the species supported CWM-optimality for at least one trait, nearly 25% significantly opposed it for at least one trait, thereby contributing to local functional diversity. The majority (85%) of species that opposed CWM-optimality did so only for one trait and few species opposed CWM-optimality in multivariate trait space. Our study suggests that constraints to local functional variation act more strongly on multivariate phenotypes than on univariate traits.

Genetic architecture of pollination syndrome transition between hummingbird-specialist and generalist species in the genus Rhytidophyllum (Gesneriaceae).

Adaptation to pollinators is a key factor of diversification in angiosperms. The Caribbean sister genera Rhytidophyllum and Gesneria present an important diversification of floral characters. Most of their species can be divided in two major pollination syndromes. Large-open flowers with pale colours and great amount of nectar represent the generalist syndrome, while the hummingbird-specialist syndrome corresponds to red tubular flowers with a less important nectar volume. Repeated convergent evolution toward the generalist syndrome in this group suggests that such transitions rely on few genes of moderate to large effect. To test this hypothesis, we built a linkage map and performed a QTL detection for divergent pollination syndrome traits by crossing one specimen of the generalist species Rhytidophyllum auriculatum with one specimen of the hummingbird pollinated R. rupincola. Using geometric morphometrics and univariate traits measurements, we found that floral shape among the second-generation hybrids is correlated with morphological variation observed between generalist and hummingbird-specialist species at the genus level. The QTL analysis showed that colour and nectar volume variation between syndromes involve each one major QTL while floral shape has a more complex genetic basis and rely on few genes of moderate effect. Finally, we did not detect any genetic linkage between the QTLs underlying those traits. This genetic independence of traits could have facilitated evolution toward optimal syndromes.

Comparative Analysis of Principal Components Can be Misleading.

Most existing methods for modeling trait evolution are univariate, although researchers are often interested in investigating evolutionary patterns and processes across multiple traits. Principal components analysis (PCA) is commonly used to reduce the dimensionality of multivariate data so that univariate trait models can be fit to individual principal components. The problem with using standard PCA on phylogenetically structured data has been previously pointed out yet it continues to be widely used in the literature. Here we demonstrate precisely how using standard PCA can mislead inferences: The first few principal components of traits evolved under constant-rate multivariate Brownian motion will appear to have evolved via an "early burst" process. A phylogenetic PCA (pPCA) has been proprosed to alleviate these issues. However, when the true model of trait evolution deviates from the model assumed in the calculation of the pPCA axes, we find that the use of pPCA suffers from similar artifacts as standard PCA. We show that data sets with high effective dimensionality are particularly likely to lead to erroneous inferences. Ultimately, all of the problems we report stem from the same underlying issue--by considering only the first few principal components as univariate traits, we are effectively examining a biased sample of a multivariate pattern. These results highlight the need for truly multivariate phylogenetic comparative methods. As these methods are still being developed, we discuss potential alternative strategies for using and interpreting models fit to univariate axes of multivariate data.

Multi-character approach reveals a discordant pattern of phenotypic variation during ontogeny in Culex pipiens biotypes (Diptera: Culicidae).

Culex (Culex) pipiens s.l. (Diptera: Culicidae) comprises two distinct biotypes, pipiens ('rural') and molestus ('urban'), both of which are thought to have differing capacities due to different host preferences. To better understand West Nile encephalitis epidemiology and improve risk assessment, local distinction between these forms is essential. This study assesses phenotypic variation at larval and adult stages of 'urban' and 'rural' biotypes of the species by complementary use of meristic, univariate and multivariate traits analyzed by traditional and geometric morphometrics. Third- and fourth-instar larvae from a broad area of the city of Novi Sad (Serbia) were collected and reared in the laboratory. After adult eclosion, the sex of each larva was recorded based on the sex of the corresponding adult. Examination of the association between variations of larval traits revealed contrasting variations regarding pecten spines vs. siphonal size and siphonal shape in the 'rural' biotype. Siphons of larvae collected in marshes and forest ecosystems outside urban areas were found to be the largest, but possessed the smallest number of pecten spines. In addition, statistically significant female-biased sexual dimorphism was observed in siphonal size, wing size and wing shape. Finally, we propose that an integrative approach is essential in delimitation of Cx. pipiens s.l. biotypes, since their differentiation was not possible based solely on larval and adult traits. Our findings shed light on the phenotypic plasticity important for population persistence in the changing environment of these medically important taxa.

Testing genetic association by regressing genotype over multiple phenotypes.

Complex disorders are typically characterized by multiple phenotypes. Analyzing these phenotypes jointly is expected to be more powerful than dealing with one of them at a time. A recent approach (O'Reilly et al. 2012) is to regress the genotype at a SNP marker on multiple phenotypes and apply the proportional odds model. In the current research, we introduce an explicit expression for the score test statistic and its non-centrality parameter that determines its power. Same simulation studies as those reported in Galesloot et al. (2014) were conducted to assess its performance. We demonstrate by theoretical arguments and simulation studies that, despite its potential usefulness for multiple phenotypes, the proportional odds model method can be less powerful than regular methods for univariate traits. We also introduce an implementation of the proposed score statistic in an R package named iGasso.

A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data.

Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (K(mult)) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of K(mult) remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on K(mult) and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in high-dimensional data. Statistical properties of K(mult) were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that K(mult) provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders.

Quantifying and Comparing Phylogenetic Evolutionary Rates for Shape and Other High-Dimensional Phenotypic Data

Many questions in evolutionary biology require the quantification and comparison of rates of phenotypic evolution. Recently, phylogenetic comparative methods have been developed for comparing evolutionary rates on a phylogeny for single, univariate traits (σ(2)), and evolutionary rate matrices (R) for sets of traits treated simultaneously. However, high-dimensional traits like shape remain under-examined with this framework, because methods suited for such data have not been fully developed. In this article, I describe a method to quantify phylogenetic evolutionary rates for high-dimensional multivariate data (σ2 mult), found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices (R-mode and Q-mode methods). I then use simulations to evaluate the statistical performance of hypothesis-testing procedures that compare σ2 mult for two or more groups of species on a phylogeny. Under both isotropic and non-isotropic conditions, and for differing numbers of trait dimensions, the proposed method displays appropriate Type I error and high statistical power for detecting known differences in σ2 mult among groups. In contrast, the Type I error rate of likelihood tests based on the evolutionary rate matrix (R) increases as the number of trait dimensions (p) increases, and becomes unacceptably large when only a few trait dimensions are considered. Further, likelihood tests based on R cannot be computed when the number of trait dimensions equals or exceeds the number of taxa in the phylogeny (i.e., when p ≥ N). These results demonstrate that tests based on σ2 mult provide a useful means of comparing evolutionary rates for high-dimensional data that are otherwise not analytically accessible to methods based on the evolutionary rate matrix. This advance thus expands the phylogenetic comparative toolkit for high-dimensional phenotypic traits like shape. Finally, I illustrate the utility of the new approach by evaluating rates of head shape evolution in a lineage of Plethodon salamanders.

Evolutionary branching under slow directional evolution

Evolutionary branching is the process by which ecological interactions induce evolutionary diversification. In asexual populations with sufficiently rare mutations, evolutionary branching occurs through trait-substitution sequences caused by the sequential invasion of successful mutants. A necessary and sufficient condition for evolutionary branching of univariate traits is the existence of a convergence stable trait value at which selection is locally disruptive. Real populations, however, undergo simultaneous evolution in multiple traits. Here we extend conditions for evolutionary branching to bivariate trait spaces in which the response to disruptive selection on one trait can be suppressed by directional selection on another trait. To obtain analytical results, we study trait-substitution sequences formed by invasions that possess maximum likelihood. By deriving a sufficient condition for evolutionary branching of bivariate traits along such maximum-likelihood-invasion paths (MLIPs), we demonstrate the existence of a threshold ratio specifying how much disruptive selection in one trait direction is needed to overcome the obstruction of evolutionary branching caused by directional selection in the other trait direction. Generalizing this finding, we show that evolutionary branching of bivariate traits can occur along evolutionary-branching lines on which residual directional selection is sufficiently weak. We then present numerical analyses showing that our generalized condition for evolutionary branching is a good indicator of branching likelihood even when trait-substitution sequences do not follow MLIPs and when mutations are not rare. Finally, we extend the derived conditions for evolutionary branching to multivariate trait spaces.

Morphometrics and the comparative method: studying the evolution of biological shape

Phylogenetic comparative methods are one of the most important parts of the morphometric toolkit for studies of morphological evolution. The assessment of repeated independent events of evolution of phenotypic and associated ecological-functional traits is still a starting point for the study of adaptation, but modern comparative approaches go beyond correlative methods, allowing for the modeling of evolutionary scenarios and analyses of trait evolution patterns. The evidence for adaptive change due to ecological diversification is stronger (even if still circumstantial) if models that predict increases in diversification rate fit the data well and the morphological changes are associated with ecological and functional changes. A large body of literature is dedicated to methodological and theoretical aspects of comparative methods, but in the context of univariate traits. On the other hand, biological shape is a complex trait, and morphometric data is essentially multivariate. Whereas most comparative methods allow for direct multivariate extensions, dimension reduction is an almost certain requirement due to the high dimensionality of morphometric data sets and the large number of evolutionary parameters that need to be estimated by comparative methods. Objective methods with considerable statistical support to determine data dimensionality exist, but the applied literature usually relies on subjective criteria to how many shape dimensions should be retained. The most appropriate calculation and interpretations of principal components, the most popular dimension reduction method, are also topics that should be considered more carefully in applications. The maturity of comparative methods and the development of model-based approaches linking macroevolutionary patterns and microevolutionary processes provide an exciting perspective for the study of morphological evolution. Download the complete Yellow Book on Virtual Morphology and Evolutionary Morphometrics in the new millenium.6283

Bayesian Non-Parametric Multivariate Statistical Models for Testing Association Between Quantitative Traits and Candidate Genes in Structured Populations

Summary Population-based linkage disequilibrium mapping permits finer scale mapping than linkage analysis. However, the population-based association mapping is subject to false positive results due to the population structure and the kinship between the samples. Although there is interest in simultaneously testing the association between a candidate gene and the multiple phenotypes of interest, the currently available association mapping methods are limited to univariate traits only. Here we present a new method for population-based multitrait candidate gene association mapping as a Bayesian semiparametric approach, where the error distribution is flexibly modelled via a multivariate mixture of Polya trees centred on the family of multivariate normal distributions. The method that we develop accounts for the population structure and the complex relatedness between the samples. We compare the new proposal in type I error rate and power with the existing multivariate version of the parametric model of Yu and co-workers and Li’s univariate semiparametric model by using the previously published two type Arabidopsis thaliana flowering data sets of association mapping, as well as simulated data.

Disentangling fluctuating asymmetry from otolith shape

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 399:261-272 (2010) - DOI: https://doi.org/10.3354/meps08347 Disentangling fluctuating asymmetry from otolith shape M. Palmer*, M. Linde, B. Morales-Nin IMEDEA (CSIC-UIB), Instituto Mediterráneo de Estudios Avanzados, Miquel Marquès 21, 07190 Esporles (Mallorca), Spain *Email: palmer@imedea.uib-csic.es ABSTRACT: Fluctuating asymmetry (FA), defined as random deviations from ideal, perfect symmetry, has been hypothesized to be negatively associated with fitness and positively associated with stress. Fish otoliths deserve special attention in regard to this topic because their FA is relatively easy to measure, unlike the fitness of wild fish themselves. In addition, otoliths are convenient for estimating FA, because they are systematically extracted for aging purposes, and otolith collections might allow the use of FA as a bioindicator of change at different spatio-temporal scales. Here we demonstrate 2 key methodological tools: the use of shape instead of previously used univariate (metric or meristic) traits, and the use of a statistical protocol capable of disentangling true FA from other types of asymmetry (namely, directional asymmetry and antisymmetry) and from measurement error. In the case of the razor fish Xyrichthys novacula; fluctuation asymmetry of the otolith shape of 180 individuals from 5 populations accounted for 23.2% total shape variability (5 fold larger than the measurement error). Moreover, FA clearly differed between populations. In contrast, FA measured using a univariate trait (radius at the sulcus) did not detect between-population differences. Moreover, in a simulation experiment, we demonstrate that FA measured using shape is able to reveal a true pattern, while FA measured using a univariate trait extracted from the same data is unable to do so. Therefore, the analysis of FA using shape paired with an adequate statistical model may increase our knowledge of the potential links between otolith FA and fitness or stress. KEY WORDS: Fluctuating asymmetry · Shape · Geometric morphometrics · Two-factor mixed effects model · Procrustes ANOVA · Directional asymmetry Full text in pdf format PreviousNextCite this article as: Palmer M, Linde M, Morales-Nin B (2010) Disentangling fluctuating asymmetry from otolith shape. Mar Ecol Prog Ser 399:261-272. https://doi.org/10.3354/meps08347 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 399. Online publication date: January 28, 2010 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2010 Inter-Research.

Quantitative genetics, version 3.0: where have we gone since 1987 and where are we headed?

The last 20 years since the previous World Congress have seen tremendous advancements in quantitative genetics, in large part due to the advancements in genomics, computation, and statistics. One central theme of this last 20 years has been the exploitation of the vast harvest of molecular markers--examples include QTL and association mapping, marker-assisted selection and introgression, scans for loci under selection, and methods to infer degree of coancestry, population membership, and past demographic history. One consequence of this harvest is that phenotyping, rather than genotyping, is now the bottleneck in molecular quantitative genetics studies. Equally important have been advances in statistics, many developed to effectively use this treasure trove of markers. Computational improvements in statistics, and in particular Markov Chain Monte Carlo (MCMC) methods, have facilitated many of these methods, as have significantly improved computational abilities for mixed models. Indeed, one could argue that mixed models have had at least as great an impact in quantitative genetics as have molecular markers. A final important theme over the past 20 years has been the fusion of population and quantitative genetics, in particular the importance of coalescence theory with its applications for association mapping, scans for loci under selection, and estimation of the demography history of a population. What are the future directions of the field? While obviously important surprises await us, the general trend seems to be moving into higher and higher dimensional traits and, in general, dimensional considerations. We have methods to deal with infinite-dimensional traits indexed by a single variable (such as a trait varying over time), but the future will require us to treat much more complex objects, such as infinite-dimensional traits indexed over several variables and with graphs and dynamical networks. A second important direction is the interfacing of quantitative genetics with physiological and developmental models as a step towards both the gene-phenotype map as well as predicting the effects of environmental changes. The high-dimensional objects we will need to consider almost certainly have most of their variation residing on a lower (likely much lower) dimensional subspace, and how to treat these constraints will be an important area of future research. Conversely, the univariate traits we currently deal with are themselves projections of more complex structures onto a lower dimensional space, and simply treating these as univariate traits can result in serious errors in understanding their selection and biology. As a field, our future is quite bright. We have new tools and techniques, and (most importantly) new talent with an exciting international group of vibrant young investigators who have received their degrees since the last Congress. One cloud for concern, however, has been the replacement at many universities of plant and animal breeders with plant and animal molecular biologists. Molecular tools are now an integral part of breeding, but breeding is not an integral part of molecular biology.