Multivariate Trait Research Articles

Multivariate generalized linear model for genetic pleiotropy.

When a single gene influences more than one trait, known as pleiotropy, it is important to detect pleiotropy to improve the biological understanding of a gene. This can lead to improved screening, diagnosis, and treatment of diseases. Yet, most current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or fewer traits are associated with a genetic variant. We recently developed statistical methods to analyze pleiotropy for quantitative traits having a multivariate normal distribution. We now extend this approach to traits that can be modeled by generalized linear models, such as analysis of binary, ordinal, or quantitative traits, or a mixture of these types of traits. Based on methods from estimating equations, we developed a new test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that $k+1$ traits are associated, given that the null of $k$ associated traits was rejected. This provides a testing framework to determine the number of traits associated with a genetic variant, as well as which traits, while accounting for correlations among the traits. By simulations, we illustrate the Type-I error rate and power of our new methods, describe how they are influenced by sample size, the number of traits, and the trait correlations, and apply the new methods to a genome-wide association study of multivariate traits measuring symptoms of major depression. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.

Species Richness, Functional Diversity and Assemblage Structure of Insectivorous Bats Along an Elevational Gradient in Tropical West Africa

Rising global temperatures cause severe changes to the environment resulting, for example, in shifts in biomes and assemblages to higher elevational ranges. Therefore, it is integral to understand how species and assemblages will respond to this threat. Elevational gradients present a useful framework to measure potential changes to diversity due to climate change and human land use. Most studies focus on taxonomic diversity, and ignore functional diversity which provides a measure of the ecological roles of species within ecosystems. The aim of this study was to investigate taxonomic and functional diversity of as well as variation in assemblage structure of the insectivorous bat communities along the Mount Nimba elevational gradient. We predicted that both taxonomic and functional diversity would be negatively correlated with elevation, and that bat assemblages would show a nested pattern along the elevational gradient. Taxonomic diversity was calculated using species richness. Functional diversity of univariate and multivariate traits was calculated using two diversity indices: mean pairwise distance and mean nearest taxon distance. As predicted, species richness of bats was significantly negatively correlated with elevation. Functional diversity however, decreased significantly only at the highest elevation (and this may even be a sampling artifact given high human activities there in recent times). Contrary to predictions, metacommunity analyses revealed quasi-Gleasonian structuring of bat assemblages, indicating weak structuring forces along the elevational gradient. These results suggest that bat assemblages shifting in response to climate change along elevational gradients may change taxonomically but stay largely intact functionally.

MVQTLCIM: composite interval mapping of multivariate traits in a hybrid F1 population of outbred species

BackgroundWith the plummeting cost of the next-generation sequencing technologies, high-density genetic linkage maps could be constructed in a forest hybrid F1 population. However, based on such genetic maps, quantitative trait loci (QTL) mapping cannot be directly conducted with traditional statistical methods or tools because the linkage phase and segregation pattern of molecular markers are not always fixed as in inbred lines.ResultsWe implemented the traditional composite interval mapping (CIM) method to multivariate trait data in forest trees and developed the corresponding software, mvqtlcim. Our method not only incorporated the various segregations and linkage phases of molecular markers, but also applied Takeuchi’s information criterion (TIC) to discriminate the QTL segregation type among several possible alternatives. QTL mapping was performed in a hybrid F1 population of Populus deltoides and P. simonii, and 12 QTLs were detected for tree height over 6 time points. The software package allowed many options for parameters as well as parallel computing for permutation tests. The features of the software were demonstrated with the real data analysis and a large number of Monte Carlo simulations.ConclusionsWe provided a powerful tool for QTL mapping of multiple or longitudinal traits in an outbred F1 population, in which the traditional software for QTL mapping cannot be used. This tool will facilitate studying of QTL mapping and thus will accelerate molecular breeding programs especially in forest trees. The tool package is freely available from https://github.com/tongchf /mvqtlcim.

How does mutation affect the distribution of phenotypes?

The potential for mutational processes to influence patterns of neutral or adaptive phenotypic evolution is not well understood. If mutations are directionally biased, shifting trait means in a particular direction, or if mutation generates more variance in some directions of multivariate trait space than others, mutation itself might be a source of bias in phenotypic evolution. Here, we use mutagenesis to investigate the affect of mutation on trait mean and (co)variances in zebrafish, Danio rerio. Mutation altered the relationship between age and both prolonged swimming speed and body shape. These observations suggest that mutational effects on ontogeny or aging have the potential to generate variance across the phenome. Mutations had a far greater effect in males than females, although whether this is a reflection of sex-specific ontogeny or aging remains to be determined. In males, mutations generated positive covariance between swimming speed, size, and body shape suggesting the potential for mutation to affect the evolutionary covariation of these traits. Overall, our observations suggest that mutation does not generate equal variance in all directions of phenotypic space or in each sex, and that pervasive variation in ontogeny or aging within a cohort could affect the variation available to evolution.

Artificial Selection to Increase the Phenotypic Variance in gmax Fails.

Stabilizing selection is important in evolutionary theories of the maintenance of genetic variance and has been invoked as the key process determining macroevolutionary patterns of trait evolution. However, manipulative evidence for the extent of stabilizing selection, particularly on multivariate traits, is lacking. We used artificial disruptive selection in Drosophila serrata as a tool to determine the relative strength of stabilizing selection experienced by multivariate trait combinations with contrasting levels of genetic and mutational variance. Contrary to expectation, when disruptive selection was applied to the major axis of standing genetic variance, gmax, we observed a significant and repeatable decrease in its phenotypic variance. In contrast, the multivariate trait combination predicted to be under strong stabilizing selection showed a significant and repeatable increase in its phenotypic variance. Correlated responses were observed in all selection treatments, and viability selection operating on extreme phenotypes of traits genetically correlated with those directly selected on limited our ability to increase their phenotypic range. Our manipulation revealed that multivariate trait combinations were subject to stabilizing selection; however, we did not observe a direct relationship between the strength of stabilizing selection and the levels of standing genetic variance in multivariate trait combinations. Contrasting patterns of allele frequencies underlying traits with high versus low levels of standing genetic variance may be implicated in determining the response to artificial selection in multivariate trait combinations.

Implications of plant functional traits and drought survival strategies for ecological restoration

Abstract Restoration of degraded grasslands through active revegetation often involves re‐establishing populations of native grasses, which must withstand increasing drought stress to persist beyond initial establishment. In perennial species, superior dehydration tolerance is expected to result in more conservative growth, but this trade‐off has seldom been studied among populations of herbaceous species. We measured seasonal growth and foliar and root functional traits under non‐limiting water conditions, followed by recovery after severe drought in four populations of Elymus glaucus, a perennial grass from California's Mediterranean‐climate region. We hypothesized that populations from harsher sites would be more dehydration tolerant, summer dormant and resource‐conservative. Dehydration tolerance and summer dormancy were associated with a more conservative strategy (lower productivity, lower specific leaf area and specific root length), as well as earlier reproductive phenology. Multivariate trait variation was associated with water availability in both the growing season and summer, while greater dehydration tolerance and summer dormancy were associated with increasing summer climatic water deficit. Synthesis and applications. Our study provides evidence of an intraspecific trade‐off between dehydration tolerance and rapid resource acquisition. We discuss the implication that restored populations with superior drought survival may therefore be less competitive, and recommend further investigation to inform plant materials selection protocols and management practices.

The effects of stress and sex on selection, genetic covariance, and the evolutionary response.

The capacity of a population to adapt to selection (evolvability) depends on whether the structure of genetic variation permits the evolution of fitter trait combinations. Selection, genetic variance and genetic covariance can change under environmental stress, and males and females are not genetically independent, yet the combined effects of stress and dioecy on evolvability are not well understood. Here, we estimate selection, genetic (co)variance and evolvability in both sexes of Tribolium castaneum flour beetles under stressful and benign conditions, using a half-sib breeding design. Although stress uncovered substantial latent heritability, stress also affected genetic covariance, such that evolvability remained low under stress. Sexual selection on males and natural selection on females favoured a similar phenotype, and there was positive intersex genetic covariance. Consequently, sexual selection on males augmented adaptation in females, and intralocus sexual conflict was weak or absent. This study highlights that increased heritability does not necessarily increase evolvability, suggests that selection can deplete genetic variance for multivariate trait combinations with strong effects on fitness, and tests the recent hypothesis that sexual conflict is weaker in stressful or novel environments.

Phylogenetic convergence and multiple shell shape optima for gliding scallops (Bivalvia: Pectinidae)

An important question in evolutionary biology is how often, and to what extent, do similar ecologies elicit distantly related taxa to evolve towards the same phenotype? In some scenarios, the repeated evolution of particular phenotypes may be expected, for instance when species are exposed to common selective forces that result from strong functional demands. In bivalved scallops (Pectinidae), some species exhibit a distinct swimming behaviour (gliding), which requires specific biomechanical attributes to generate lift and reduce drag during locomotive events. Further, a phylogenetic analysis revealed that gliding behaviour has independently evolved at least four times, which raises the question as to whether these independent lineages have also converged on a similar phenotype. Here, we test the hypothesis that gliding scallops display shell shape convergence using a combination of geometric morphometrics and phylogenetic comparative methods that evaluate patterns of multivariate trait evolution. Our findings reveal that the gliding species display less morphological disparity and significant evolutionary convergence in morphospace, relative to expectations under a neutral model of Brownian motion for evolutionary phenotypic change. Intriguingly, the phylomorphospace patterns indicate that gliding lineages follow similar evolutionary trajectories to not one, but two regions of morphological space, and subsequent analyses identified significant differences in their biomechanical parameters, suggesting that these two groups of scallops accomplish gliding in different ways. Thus, whereas there is a clear gliding morphotype that has evolved convergently across the phylogeny, functionally distinct morphological subforms are apparent, suggesting that there may be two optima for the gliding phenotype in the Pectinidae.

Functional diversity and composition of bryophyte water-related traits in Azorean native vegetation

Background: Functional trait-based approaches link species diversity patterns to ecosystem functioning. In the context of global change, understanding these links is vital for developing holistic biodiversity management strategies. Bryophytes, important ecosystem components owing to their biogeochemical functions, have not been the focus of many functional studies.Aims: This is the first assessment of bryophyte functional diversity in the Azores archipelago, aiming to uncover multivariate trait richness and composition patterns along the elevational gradient on Terceira Island.Methods: Based on five water acquisition and retention traits of leafy liverworts and mosses, we calculated functional diversity metrics within and among six bryophyte communities sampled along a 1021-m elevational transect.Results: Trait composition differed significantly between coastal and mountain communities. Mosses presented inrolled leaves and ornamented leaf cells at low elevation but not at high elevation. These patterns were associated with an uphill shift from drier and warmer conditions to a moister and cooler environment.Conclusions: Future climatic changes might affect bryophyte functional diversity patterns in Terceira Island, particularly for mosses. These results can be directly compared with those obtained for other archipelagos where the same protocol has been applied, allowing a joint assessment of insular vegetation functional diversity patterns.

Variation in species-level plant functional traits over wetland indicator status categories.

Wetland indicator status (WIS) describes the habitat affinity of plant species and is used in wetland delineations and resource inventories. Understanding how species‐level functional traits vary across WIS categories may improve designations, elucidate mechanisms of adaptation, and explain habitat optima and niche. We investigated differences in species‐level traits of riparian flora across WIS categories, extending their application to indicate hydrologic habitat. We measured or compiled data on specific leaf area (SLA), stem specific gravity (SSG), seed mass, and mature height of 110 plant species that occur along the Colorado River in Grand Canyon, Arizona. Additionally, we measured leaf δ13C, δ15N, % carbon, % nitrogen, and C/N ratio of 56 species with C3 photosynthesis. We asked the following: (i) How do species‐level traits vary over WIS categories? (ii) Does the pattern differ between herbaceous and woody species? (iii) How well do multivariate traits define WIS categories? (iv) Which traits are correlated? The largest trait differences among WIS categories for herbaceous species occurred for SSG, seed mass, % leaf carbon and height, and for woody species occurred for height, SSG, and δ13C. SSG increased and height decreased with habitat aridity for both woody and herbaceous species. The δ13C and hence water use efficiency of woody species increased with habitat aridity. Water use efficiency of herbaceous species increased with habitat aridity via greater occurrence of C4 grasses. Multivariate trait assemblages differed among WIS categories. Over all species, SLA was correlated with height, δ13C, % leaf N, and C/N; height was correlated with SSG and % leaf C; SSG was correlated with % leaf C. Adaptations of both herbaceous and woody riparian species to wet, frequently inundated habitats include low‐density stem tissue. Adaptations to drier habitats in the riparian zone include short, high‐density cavitation‐resistant stem tissue, and high water use efficiency. The results enhance understanding about using traits to describe plant habitat in riparian systems.

Rapid maximum likelihood ancestral state reconstruction of continuous characters: A rerooting-free algorithm.

Ancestral state reconstruction is a method used to study the evolutionary trajectories of quantitative characters on phylogenies. Although efficient methods for univariate ancestral state reconstruction under a Brownian motion model have been described for at least 25 years, to date no generalization has been described to allow more complex evolutionary models, such as multivariate trait evolution, non‐Brownian models, missing data, and within‐species variation. Furthermore, even for simple univariate Brownian motion models, most phylogenetic comparative R packages compute ancestral states via inefficient tree rerooting and full tree traversals at each tree node, making ancestral state reconstruction extremely time‐consuming for large phylogenies. Here, a computationally efficient method for fast maximum likelihood ancestral state reconstruction of continuous characters is described. The algorithm has linear complexity relative to the number of species and outperforms the fastest existing R implementations by several orders of magnitude. The described algorithm is capable of performing ancestral state reconstruction on a 1,000,000‐species phylogeny in fewer than 2 s using a standard laptop, whereas the next fastest R implementation would take several days to complete. The method is generalizable to more complex evolutionary models, such as phylogenetic regression, within‐species variation, non‐Brownian evolutionary models, and multivariate trait evolution. Because this method enables fast repeated computations on phylogenies of virtually any size, implementation of the described algorithm can drastically alleviate the computational burden of many otherwise prohibitively time‐consuming tasks requiring reconstruction of ancestral states, such as phylogenetic imputation of missing data, bootstrapping procedures, Expectation‐Maximization algorithms, and Bayesian estimation. The described ancestral state reconstruction algorithm is implemented in the Rphylopars functions anc.recon and phylopars.

Competition drives trait evolution and character displacement between Mimulus species along an environmental gradient.

Closely related species may evolve to coexist stably in sympatry through niche differentiation driven by in situ competition, a process termed character displacement. Alternatively, past evolution in allopatry may have already sufficiently reduced niche overlap to permit establishment in sympatry, a process called ecological sorting. The relative importance of each process to niche differentiation is contentious even though they are not mutually exclusive and are both mediated via multivariate trait evolution. We explore how competition has impacted niche differentiation in two monkeyflowers, Mimulus alsinoides and M. guttatus, which often co-occur. Through field observations, common gardens, and competition experiments, we demonstrate that M. alsinoides is restricted to marginal habitats in sympatry and that the impacts of character displacement on niche differentiation are complex. Competition with M. guttatus alters selection gradients and has favored taller M. alsinoides with earlier seasonal flowering at low elevation and floral shape divergence at high elevation. However, no trait exhibits the pattern typically associated with character displacement, higher divergence between species in sympatry than allopatry. Thus, although character displacement was unlikely the process driving initial divergence along niche axes necessary for coexistence, we conclude that competition in sympatry has likely driven trait evolution along additional niche axes.

Predicting species establishment using absent species and functional neighborhoods.

Species establishment within a community depends on their interactions with the local environment and resident community. Such environmental and biotic filtering is frequently inferred from functional trait and phylogenetic patterns within communities; these patterns may also predict which additional species can establish. However, differentiating between environmental and biotic filtering can be challenging, which may complicate establishment predictions. Creating a habitat‐specific species pool by identifying which absent species within the region can establish in the focal habitat allows us to isolate biotic filtering by modeling dissimilarity between the observed and biotically excluded species able to pass environmental filters. Similarly, modeling the dissimilarity between the habitat‐specific species pool and the environmentally excluded species within the region can isolate local environmental filters. Combined, these models identify potentially successful phenotypes and why certain phenotypes were unsuccessful. Here, we present a framework that uses the functional dissimilarity among these groups in logistic models to predict establishment of additional species. This approach can use multivariate trait distances and phylogenetic information, but is most powerful when using individual traits and their interactions. It also requires an appropriate distance‐based dissimilarity measure, yet the two most commonly used indices, nearest neighbor (one species) and mean pairwise (all species) distances, may inaccurately predict establishment. By iteratively increasing the number of species used to measure dissimilarity, a functional neighborhood can be chosen that maximizes the detection of underlying trait patterns. We tested this framework using two seed addition experiments in calcareous grasslands. Although the functional neighborhood size that best fits the community's trait structure depended on the type of filtering considered, selecting these functional neighborhood sizes allowed our framework to predict up to 50% of the variation in actual establishment from seed. These results indicate that the proposed framework may be a powerful tool for studying and predicting species establishment.

Heads you win, tails you lose: Life‐history traits predict invasion and extinction risk of the world's freshwater fishes

Abstract Freshwater fishes have the distinction of being both a highly imperilled taxonomic group as well as one that has produced many invasive species with widespread ecological impacts. Faced with the difficult task of identifying those species with the greatest need for management action, ecologists have turned to using predictive suites of ecological and life‐history traits to provide reasonable estimates of fish invasion and extinction risk. Whether traits associated with invasiveness are the inverse of those associated with imperillment, known as the ‘two‐sides‐of‐the‐same‐coin’ hypothesis, remains unclear. A global trait analysis (including maximum total body size, longevity, size at maturation, age at maturation, fecundity and egg size) for 6293 freshwater fishes was conducted to examine the trait correlates of species proneness to invasion or extinction. A meta‐analytical procedure was deployed using univariate and multivariate trait analyses that accounted for the effects of shared phylogeny. Mean trait differences (measured as Hedges' d effect size) were found between invasive and threatened species when compared with native species, thus supporting the two‐sides‐of‐the‐same‐coin hypothesis for freshwater fish. Invasive species were characterized by larger body size, greater longevity, delayed maturation and higher fecundity than threatened species. Furthermore, invasive species were found to display greater trait variability compared with threatened species, suggesting that different traits may be selected at different stages of the invasion process (from pathway entrainment to establishment) whereas more specific trait combinations may predispose species to higher extinction risk. The present study demonstrated a strong trait basis to global‐scale invasion risk and extinction vulnerability for freshwater fishes. Given that both time and resources are too limited for detailed species‐by‐species assessments, the results suggest that trait correlates provide a reasonable estimate of invasion and extinction risk that can inform more targeted and proactive conservation strategies.

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel.

Litter chemistry and chemical diversity drive ecosystem processes in forest ponds.

Research suggests that a positive relationship exists between diversity and ecological function, yet the multi-trophic effects of biodiversity remain poorly understood. The resource complementarity hypothesis suggests that increasing the trait diversity of resources provides a more complete diet for consumers, elevating consumer feeding rates. Whereas previous tests of this mechanism have measured trait diversity as the variation of single traits or the richness of functional groups, we employed a multivariate trait index to manipulate the chemical diversity of temperate tree litter species in outdoor pond mesocosms. We inoculated outdoor mesocosms with diverse and multi-trophic communities of microbial and macro-consumer species that rely on leaf litter for energy and nutrients. Litter was provided at three levels of chemical trait diversity, a constant level of species richness, and an equal representation of all litter species. Over threemonths, we measured more than 65 responses, and assessed the effects of litter chemical diversity and chemical trait means (i.e., community-weighted means). We found that litter chemical diversity positively correlated with decomposition rate of leaf litter, but had no effect on biomass or density of producers and consumers. However, the pond communities often responded to chemical trait means, particularly those related to nutrients, structure, and defense. Our results suggest that resource complementarity does have some effect on the release of energy and nutrients from decomposing substrates in forest ponds, but does not have multi-trophic effects. Our results further suggest that loss of tree biodiversity could affect forest ecosystem functionality, and particularly the processes occurring in and around ponds and wetlands.

Assessing statistical significance in variance components linkage analysis: A theoretical justification

Variance components analysis has been a standard means in family-based genetic data analysis. The variance components technique treats genetic effects as random, and tests whether variance components are zero using the likelihood ratio (LR) test. In the literature, the asymptotic distribution of the LR is claimed to follow a mixture chi-square distribution, where the mixture proportions are calculated based on the binomial coefficients, a special case in Self and Liang (1987). This threshold calculation, however, often yields conservative test results as discussed in a number of studies, especially in multi-trait analyses. In this work, we show that the LR statistic asymptotically follows a mixture chi-square distribution where the mixture proportions depend on the estimated Fisher information matrix in both univariate and multivariate trait analyses. We provide a general approximation form for the distribution of the LR under the null hypothesis of no genetic effects. We illustrate our idea with three variance components models in genetic linkage analysis. The performance of the new threshold calculation method is demonstrated via simulation studies, and its application is further illustrated via a real data analysis.

Nutrient supply alters goldenrod's induced response to herbivory

SummaryRecent interest in using trait‐based approaches to understand and predict ecosystem processes and evolutionary responses to environmental change highlights the need to understand the relative importance of genetic and environmental sources of intraspecific trait variation within local populations of dominant species.Here, I combine plant defence theory with functional approaches to quantify genetic trait variation and phenotypic trait plasticity of nine goldenrod (Solidago altissima) genotypes derived from a local field population in Connecticut, USA, to herbivory along a nutrient supply gradient.I found that increasing nutrient supply changed the dominant plant defence strategy from tolerance to induced resistance. Induced resistance was detected through decreased herbivore growth rates and a behavioural feeding shift of grasshoppers to older leaf tissue. This could not be fully accounted for through stoichiometric changes in leaf tissue quality.A multidimensional phenotype approach revealed that abiotic and biotic environments (nutrients and herbivory) accounted for almost as much whole‐plant trait variation (31%) as did plant genotype (36%). Increasing nutrient supply and herbivory resulted in independent and differential effects on whole‐plant trait expression. Increasing both treatments concurrently produced a unique plant phenotype with increased leaf carbon content and allocation to asexual reproduction (E × E).Notably, individual genotypes exhibited different magnitudes of multivariate trait plasticity to nutrient and herbivory gradients. However, the population of genotypes as a whole within a given environment expressed an approximately equal magnitude of trait variation across both permissive (high nutrient, no herbivory) and stressful (low nutrient, high herbivory) environments.Quantifying plasticity in defensive strategy in concert with correlated whole‐plant trait expression changes across multiple abiotic and biotic factors may be key to providing a mechanistic understanding of how heterogeneous landscapes impact community interactions and ecosystem processes.Alay summaryis available for this article.

Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data

Testing for genetic association with multiple traits has become increasingly important, not only because of its potential to boost statistical power, but also for its direct relevance to applications. For example, there is accumulating evidence showing that some complex neurodegenerative and psychiatric diseases like Alzheimer's disease are due to disrupted brain networks, for which it would be natural to identify genetic variants associated with a disrupted brain network, represented as a set of multiple traits, one for each of multiple brain regions of interest. In spite of its promise, testing for multivariate trait associations is challenging: if not appropriately used, its power can be much lower than testing on each univariate trait separately (with a proper control for multiple testing). Furthermore, differing from most existing methods for single-SNP-multiple-trait associations, we consider SNP set-based association testing to decipher complicated joint effects of multiple SNPs on multiple traits. Because the power of a test critically depends on several unknown factors such as the proportions of associated SNPs and of traits, we propose a highly adaptive test at both the SNP and trait levels, giving higher weights to those likely associated SNPs and traits, to yield high power across a wide spectrum of situations. We illuminate relationships among the proposed and some existing tests, showing that the proposed test covers several existing tests as special cases. We compare the performance of the new test with that of several existing tests, using both simulated and real data. The methods were applied to structural magnetic resonance imaging data drawn from the Alzheimer's Disease Neuroimaging Initiative to identify genes associated with gray matter atrophy in the human brain default mode network (DMN). For genome-wide association studies (GWAS), genes AMOTL1 on chromosome 11 and APOE on chromosome 19 were discovered by the new test to be significantly associated with the DMN. Notably, gene AMOTL1 was not detected by single SNP-based analyses. To our knowledge, AMOTL1 has not been highlighted in other Alzheimer's disease studies before, although it was indicated to be related to cognitive impairment. The proposed method is also applicable to rare variants in sequencing data and can be extended to pathway analysis.

Linking intra- and interspecific assortative mating: Consequences for asymmetric sexual isolation.

Assortative mating is of interest because of its role in speciation and the maintenance of species boundaries. However, we know little about how within-species assortment is related to interspecific sexual isolation. Most previous studies of assortative mating have focused on a single trait in males and females, rather than utilizing multivariate trait information. Here, we investigate how intraspecific assortative mating relates to sexual isolation in two sympatric and congeneric damselfly species (genus Calopteryx). We connect intraspecific assortment to interspecific sexual isolation by combining field observations, mate preference experiments, and enforced copulation experiments. Using canonical correlation analysis, we demonstrate multivariate intraspecific assortment for body size and body shape. Males of the smaller species mate more frequently with heterospecific females than males of the larger species, which showed less attraction to small heterospecific females. Field experiments suggest that sexual isolation asymmetry is caused by male preferences for large heterospecific females, rather than by mechanical isolation due to interspecific size differences or female preferences for large males. Male preferences for large females and male-male competition for high quality females can therefore counteract sexual isolation. This sexual isolation asymmetry indicates that sexual selection currently opposes a species boundary.