A Predictive Framework for Integrating Disparate Genomic Data Types Using Sample-Specific Gene Set Enrichment Analysis and Multi-Task Learning

Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these expression changes is not well understood. In this study, we developed a framework to integrate gene expression and genotype data to identify biological differences between samples from opposing complex trait classes that are driven by expression changes and genotypic variation. This framework utilizes pathway analysis and multi-task learning to build a predictive model and discover pathways relevant to the complex trait of interest. We simulated expression and genotype data to test the predictive ability of our framework and to measure how well it uncovered pathways with genes both differentially expressed and genetically associated with a complex trait. We found that the predictive performance of the multi-task model was comparable to other similar methods. Also, methods like multi-task learning that considered enrichment analysis scores from both data sets found pathways with both genetic and expression differences related to the phenotype. We used our framework to analyze differences between estrogen receptor (ER) positive and negative breast cancer samples. An analysis of the top 15 gene sets from the multi-task model showed they were all related to estrogen, steroids, cell signaling, or the cell cycle. Although our study suggests that multi-task learning does not enhance predictive accuracy, the models generated by our framework do provide valuable biological pathway knowledge for complex traits.     

The Nature and Extent of Mutational Pleiotropy in Gene Expression of Male Drosophila serrata

The nature and extent of mutational pleiotropy remain largely unknown, despite the central role that pleiotropy plays in many areas of biology, including human disease, agricultural production, and evolution. Here, we investigate the variation in 11,604 gene expression traits among 41 mutation accumulation (MA) lines of Drosophila serrata. We first confirmed that these expression phenotypes were heritable, detecting genetic variation in 96% of them in an outbred, natural population of D. serrata. Among the MA lines, 3385 (29%) of expression traits were variable, with a mean mutational heritability of 0.0005. In most traits, variation was generated by mutations of relatively small phenotypic effect; putative mutations with effects of greater than one phenotypic standard deviation were observed for only 8% of traits. With most (71%) traits unaffected by any mutation, our data provide no support for universal pleiotropy. We further characterized mutational pleiotropy in the 3385 variable traits, using sets of 5, randomly assigned, traits. Covariance among traits chosen at random with respect to their biological function is expected only if pleiotropy is extensive. Taking an analytical approach in which the variance unique to each trait in the random 5-trait sets was partitioned from variance shared among traits, we detected significant (at 5% false discovery rate) mutational covariance in 21% of sets. This frequency of statistically supported covariance implied that at least some mutations must pleiotropically affect a substantial number of traits (>70; 0.6% of all measured traits).     

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F₂ mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm. Electronic supplementary material The online version of this article (doi: ) contains supplementary material, which is available to authorized users. Tova F. Fuller, Anatole Ghazalpour contributed equally to this work.     

Computing evolutionary distinctiveness indices in large scale analysis

Background

A combined quantitative trait loci (QTL) and microarray-based approach is commonly used to find differentially expressed genes which are then identified based on the known function of a gene in the biological process governing the trait of interest. However, a low cutoff value in individual gene analyses may result in many genes with moderate but meaningful changes in expression being missed.

Results

We modified a gene set analysis to identify intersection sets with significantly affected expression for which the changes in the individual gene sets are less significant. The gene expression profiles in liver tissues of four strains of mice from publicly available microarray sources were analyzed to detect trait-associated pathways using information on the QTL regions of blood concentrations of high density lipoproteins (HDL) cholesterol and insulin-like growth factor 1 (IGF-1). Several metabolic pathways related to HDL levels, including lipid metabolism, ABC transporters and cytochrome P450 pathways were detected for HDL QTL regions. Most of the pathways identified for the IGF-1 phenotype were signal transduction pathways associated with biological processes for IGF-1's regulation.

Conclusion

We have developed a method of identifying pathways associated with a quantitative trait using information on QTL. Our approach provides insights into genotype-phenotype relations at the level of biological pathways which may help to elucidate the genetic architecture underlying variation in phenotypic traits.     

Evolution of adaptive phenotypic variation patterns by direct selection for evolvability

A basic assumption of the Darwinian theory of evolution is that heritable variation arises randomly. In this context, randomness means that mutations arise irrespective of the current adaptive needs imposed by the environment. It is broadly accepted, however, that phenotypic variation is not uniformly distributed among phenotypic traits, some traits tend to covary, while others vary independently, and again others barely vary at all. Furthermore, it is well established that patterns of trait variation differ among species. Specifically, traits that serve different functions tend to be less correlated, as for instance forelimbs and hind limbs in bats and humans, compared with the limbs of quadrupedal mammals. Recently, a novel class of genetic elements has been identified in mouse gene-mapping studies that modify correlations among quantitative traits. These loci are called relationship loci, or relationship Quantitative Trait Loci (rQTL), and affect trait correlations by changing the expression of the existing genetic variation through gene interaction. Here, we present a population genetic model of how natural selection acts on rQTL. Contrary to the usual neo-Darwinian theory, in this model, new heritable phenotypic variation is produced along the selected dimension in response to directional selection. The results predict that selection on rQTL leads to higher correlations among traits that are simultaneously under directional selection. On the other hand, traits that are not simultaneously under directional selection are predicted to evolve lower correlations. These results and the previously demonstrated existence of rQTL variation, show a mechanism by which natural selection can directly enhance the evolvability of complex organisms along lines of adaptive change.     

Sexual selection and population divergence II. Divergence in different sexual traits and signal modalities in field crickets (Teleogryllus oceanicus)

Sexual selection can target many different types of traits. However, the relative influence of different sexually selected traits during evolutionary divergence is poorly understood. We used the field cricket Teleogryllus oceanicus to quantify and compare how five traits from each of three sexual signal modalities and components diverge among allopatric populations: male advertisement song, cuticular hydrocarbon (CHC) profiles and forewing morphology. Population divergence was unexpectedly consistent: we estimated the among‐population (genetic) variance‐covariance matrix, D , for all 15 traits, and D_max explained nearly two‐thirds of its variation. CHC and wing traits were most tightly integrated, whereas song varied more independently. We modeled the dependence of among‐population trait divergence on genetic distance estimated from neutral markers to test for signatures of selection versus neutral divergence. For all three sexual trait types, phenotypic variation among populations was largely explained by a neutral model of divergence. Our findings illustrate how phenotypic integration across different types of sexual traits might impose constraints on the evolution of mating isolation and divergence via sexual selection.     

Intraspecific trait variation influences physiological performance and fitness in the South Africa shrub genus Protea (Proteaceae)

Background and AimsGlobal plant trait datasets commonly identify trait relationships that are interpreted to reflect fundamental trade-offs associated with plant strategies, but often these trait relationships are not identified when evaluating them at smaller taxonomic and spatial scales. In this study we evaluate trait relationships measured on individual plants for five widespread Protea species in South Africa to determine whether broad-scale patterns of structural trait (e.g. leaf area) and physiological trait (e.g. photosynthetic rates) relationships can be detected within natural populations, and if these traits are themselves related to plant fitness.MethodsWe evaluated the variance structure (i.e. the proportional intraspecific trait variation relative to among-species variation) for nine structural traits and six physiological traits measured in wild populations. We used a multivariate path model to evaluate the relationships between structural traits and physiological traits, and the relationship between these traits and plant size and reproductive effort.Key ResultsWhile intraspecific trait variation is relatively low for structural traits, it accounts for between 50 and 100 % of the variation in physiological traits. Furthermore, we identified few trait associations between any one structural trait and physiological trait, but multivariate regressions revealed clear associations between combinations of structural traits and physiological performance (R² = 0.37–0.64), and almost all traits had detectable associations with plant fitness.ConclusionsIntraspecific variation in structural traits leads to predictable differences in individual-level physiological performance in a multivariate framework, even though the relationship of any particular structural trait to physiological performance may be weak or undetectable. Furthermore, intraspecific variation in both structural and physiological traits leads to differences in plant size and fitness. These results demonstrate the importance of considering measurements of multivariate phenotypes on individual plants when evaluating trait relationships and how trait variation influences predictions of ecological and evolutionary outcomes.     

Leaf traits and performance vary with plant age and water availability in Artemisia californica

Background and AimsLeaf functional traits are strongly tied to growth strategies and ecological processes across species, but few efforts have linked intraspecific trait variation to performance across ontogenetic and environmental gradients. Plants are believed to shift towards more resource-conservative traits in stressful environments and as they age. However, uncertainty as to how intraspecific trait variation aligns with plant age and performance in the context of environmental variation may limit our ability to use traits to infer ecological processes at larger scales.MethodsWe measured leaf physiological and morphological traits, canopy volume and flowering effort for Artemisia californica (California sagebrush), a dominant shrub species in the coastal sage scrub community, under conditions of 50, 100 and 150 % ambient precipitation for 3 years.Key ResultsPlant age was a stronger driver of variation in traits and performance than water availability. Older plants demonstrated trait values consistent with a more conservative resource-use strategy, and trait values were less sensitive to drought. Several trait correlations were consistent across years and treatments; for example, plants with high photosynthetic rates tended to have high stomatal conductance, leaf nitrogen concentration and light-use efficiency. However, the trade-off between leaf construction and leaf nitrogen evident in older plants was absent for first-year plants. While few traits correlated with plant growth and flowering effort, we observed a positive correlation between leaf mass per area and performance in some groups of older plants.ConclusionsOverall, our results suggest that trait sensitivity to the environment is most visible during earlier stages of development, after which intraspecific trait variation and relationships may stabilize. While plant age plays a major role in intraspecific trait variation and sensitivity (and thus trait-based inferences), the direct influence of environment on growth and fecundity is just as critical to predicting plant performance in a changing environment.     

Evolutionary genomics of animal personality

Research on animal personality can be approached from both a phenotypic and a genetic perspective. While using a phenotypic approach one can measure present selection on personality traits and their combinations. However, this approach cannot reconstruct the historical trajectory that was taken by evolution. Therefore, it is essential for our understanding of the causes and consequences of personality diversity to link phenotypic variation in personality traits with polymorphisms in genomic regions that code for this trait variation. Identifying genes or genome regions that underlie personality traits will open exciting possibilities to study natural selection at the molecular level, gene-gene and gene-environment interactions, pleiotropic effects and how gene expression shapes personality phenotypes. In this paper, we will discuss how genome information revealed by already established approaches and some more recent techniques such as high-throughput sequencing of genomic regions in a large number of individuals can be used to infer micro-evolutionary processes, historical selection and finally the maintenance of personality trait variation. We will do this by reviewing recent advances in molecular genetics of animal personality, but will also use advanced human personality studies as case studies of how molecular information may be used in animal personality research in the near future.     

Effects of intrinsic environmental predictability on intra-individual and intra-population variability of plant reproductive traits and eco-evolutionary consequences

Background and AimsIt is widely accepted that changes in the environment affect mean trait expression, but little is known about how the environment shapes intra-individual and intra-population variance. Theory suggests that intra-individual variance might be plastic and under natural selection, rather than reflecting developmental noise, but evidence for this hypothesis is scarce. Here, we experimentally tested whether differences in intrinsic environmental predictability affect intra-individual and intra-population variability of different reproductive traits, and whether intra-individual variability is under selection.MethodsUnder field conditions, we subjected Onobrychis viciifolia to more and less predictable precipitation over 4 generations and 4 years. We analysed effects on the coefficient of intra-individual variation (CV_i-i) and the coefficient of intra-population variation (CV_i-p), assessed whether the coefficients of intra-individual variation (CsV_i-i) are under natural selection and tested for transgenerational responses (ancestor environmental effects on offspring).Key ResultsLess predictable precipitation led to higher CsV_i-i and CsV_i-p, consistent with plastic responses. The CsV_i-i of all studied traits were under consistent stabilizing selection, and precipitation predictability affected the strength of selection and the location of the optimal CV_i-i of a single trait. All CsV_i-i differed from the optimal CV_i-i and the maternal and offspring CsV_i-i were positively correlated, showing that there was scope for change. Nevertheless, no consistent transgenerational effects were found in any of the three descendant generations, which contrasts with recent studies that detected rapid transgenerational responses in the trait means of different plant species. This suggests that changes in intra-individual variability take longer to evolve than changes in trait means, which may explain why high intra-individual variability is maintained, despite the stabilizing selection.ConclusionsThe results indicate that plastic changes of intra-individual variability are an important determinant of whether plants will be able to cope with changes in environmental predictability induced by the currently observed climatic change.     

Variation in flooding-induced morphological traits in natural populations of white clover (Trifolium repens) and their effects on plant performance during soil flooding

Background and Aims

Soil flooding leads to low soil oxygen concentrations and thereby negatively affects plant growth. Differences in flooding tolerance have been explained by the variation among species in the extent to which traits related to acclimation were expressed. However, our knowledge of variation within natural species (i.e. among individual genotypes) in traits related to flooding tolerance is very limited. Such data could tell us on which traits selection might have taken place, and will take place in future. The aim of the present study was to show that variation in flooding-tolerance-related traits is present among genotypes of the same species, and that both the constitutive variation and the plastic variation in flooding-induced changes in trait expression affect the performance of genotypes during soil flooding.

Methods

Clones of Trifolium repens originating from a river foreland were subjected to either drained, control conditions or to soil flooding. Constitutive expression of morphological traits was recorded on control plants, and flooding-induced changes in expression were compared with these constitutive expression levels. Moreover, the effect of both constitutive and flooding-induced trait expression on plant performance was determined.

Key Results

Constitutive and plastic variation of several morphological traits significantly affected plant performance. Even relatively small increases in root porosity and petiole length contributed to better performance during soil flooding. High specific leaf area, by contrast, was negatively correlated with performance during flooding.

Conclusions

The data show that different genotypes responded differently to soil flooding, which could be linked to variation in morphological trait expression. As flooded and drained conditions exerted different selection pressures on trait expression, the optimal value for constitutive and plastic traits will depend on the frequency and duration of flooding. These data will help us understanding the mechanisms affecting short- and long-term dynamics in flooding-prone ecosystems.Key words: Secondary roots, aerenchyma, genotypic variation, petiole length, plant performance, root porosity, selection, soil flooding, specific leaf area (SLA), Trifolium repens, white clover     

Leading trait dimensions in flood-tolerant plants

Background and AimsWhile trait-based approaches have provided critical insights into general plant functioning, we lack a comprehensive quantitative view on plant strategies in flooded conditions. Plants adapted to flooded conditions have specific traits (e.g. root porosity, low root/shoot ratio and shoot elongation) to cope with the environmental stressors including anoxic sediments, and the subsequent presence of phytotoxic compounds. In flooded habitats, plants also respond to potential nutrient and light limitations, e.g. through the expression of leaf economics traits and size-related traits, respectively. However, we do not know whether and how these trait dimensions are connected.MethodsBased on a trait dataset compiled on 131 plant species from 141 studies in flooded habitats, we quantitatively analysed how flooding-induced traits are positioned in relation to the other two dominant trait dimensions: leaf economics traits and size-related traits. We evaluated how these key trait components are expressed along wetness gradients, across habitat types and among plant life forms.Key ResultsWe found that flooding-induced traits constitute a trait dimension independent from leaf economics traits and size-related traits, indicating that there is no generic trade-off associated with flooding adaptations. Moreover, individual flooding-induced traits themselves are to a large extent decoupled from each other. These results suggest that adaptation to stressful environments, such as flooding, can be stressor specific without generic adverse effects on plant functioning (e.g. causing trade-offs on leaf economics traits).ConclusionsThe trait expression across multiple dimensions promotes plant adaptations and coexistence across multifaceted flooded environments. The decoupled trait dimensions, as related to different environmental drivers, also explain why ecosystem functioning (including, for example, methane emissions) are species and habitat specific. Thus, our results provide a backbone for applying trait-based approaches in wetland ecology by considering flooding-induced traits as an independent trait dimension.     

Identification of associations between SSR markers and fiber traits in an exotic germplasm derived from multiple crosses among Gossypium tetraploid species

Genetic improvement in yield and fiber quality is needed for worldwide cotton production. Identification of molecular markers associated with fiber-related traits can facilitate selection for these traits in breeding. This study was designed to identify associations between SSR markers and fiber traits using an exotic germplasm population, species polycross (SP), derived from multiple crosses among Gossypium tetraploid species. The SP population underwent 11 generations of mixed random mating and selfing followed by 12 generations of selfing. A total of 260 lines were evaluated for fiber-related traits under three environments in 2005 and 2006. Large genotypic variance components in traits were identified relative to components of genotype × environment. Eighty-six primer pairs amplified a total of 314 polymorphic fragments among 260 lines. A total of 202 fragments with above 6% allele frequency were analyzed for associations. Fifty-nine markers were found to have a significant (P < 0.05, 0.01, or 0.001) association with six fiber traits. There were six groups identified within the population using structure analysis. Allele frequency divergence among six groups ranged from 0.11 to 0.27. Of the 59 marker–trait associations, 39 remained significant after correction for population structure and kinship using a mixed linear model. The effect of population sub-structure on associations was most significant in boll weight among the traits analyzed. The sub-structure among the SP lines may be caused by natural selection, the breeding method applied during development of inbred lines, and unknown factors. The identified marker–trait associations can be useful in breeding and help determine genetic mechanisms underlying interrelationships among fiber traits. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

GENOME DIVERGENCE AND THE GENETIC ARCHITECTURE OF BARRIERS TO GENE FLOW BETWEEN LYCAEIDES IDAS AND L. MELISSA

Genome divergence during speciation is a dynamic process that is affected by various factors, including the genetic architecture of barriers to gene flow. Herein we quantitatively describe aspects of the genetic architecture of two sets of traits, male genitalic morphology and oviposition preference, that putatively function as barriers to gene flow between the butterfly species Lycaeides idas and L. melissa. Our analyses are based on unmapped DNA sequence data and a recently developed Bayesian regression approach that includes variable selection and explicit parameters for the genetic architecture of traits. A modest number of nucleotide polymorphisms explained a small to large proportion of the variation in each trait, and average genetic variant effects were nonnegligible. Several genetic regions were associated with variation in multiple traits or with trait variation within‐ and among‐populations. In some instances, genetic regions associated with trait variation also exhibited exceptional genetic differentiation between species or exceptional introgression in hybrids. These results are consistent with the hypothesis that divergent selection on male genitalia has contributed to heterogeneous genetic differentiation, and that both sets of traits affect fitness in hybrids. Although these results are encouraging, we highlight several difficulties related to understanding the genetics of speciation.     

Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle

Background

Four traits related to carcass performance have been identified as economically important in beef production: carcass weight, carcass fat, carcass conformation of progeny and cull cow carcass weight. Although Holstein-Friesian cattle are primarily utilized for milk production, they are also an important source of meat for beef production and export. Because of this, there is great interest in understanding the underlying genomic structure influencing these traits. Several genome-wide association studies have identified regions of the bovine genome associated with growth or carcass traits, however, little is known about the mechanisms or underlying biological pathways involved. This study aims to detect regions of the bovine genome associated with carcass performance traits (employing a panel of 54,001 SNPs) using measures of genetic merit (as predicted transmitting abilities) for 5,705 Irish Holstein-Friesian animals. Candidate genes and biological pathways were then identified for each trait under investigation.

Results

Following adjustment for false discovery (q-value < 0.05), 479 quantitative trait loci (QTL) were associated with at least one of the four carcass traits using a single SNP regression approach. Using a Bayesian approach, 46 QTL were associated (posterior probability > 0.5) with at least one of the four traits. In total, 557 unique bovine genes, which mapped to 426 human orthologs, were within 500kbs of QTL found associated with a trait using the Bayesian approach. Using this information, 24 significantly over-represented pathways were identified across all traits. The most significantly over-represented biological pathway was the peroxisome proliferator-activated receptor (PPAR) signaling pathway.

Conclusions

A large number of genomic regions putatively associated with bovine carcass traits were detected using two different statistical approaches. Notably, several significant associations were detected in close proximity to genes with a known role in animal growth such as glucagon and leptin. Several biological pathways, including PPAR signaling, were shown to be involved in various aspects of bovine carcass performance. These core genes and biological processes may form the foundation for further investigation to identify causative mutations involved in each trait. Results reported here support previous findings suggesting conservation of key biological processes involved in growth and metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-837) contains supplementary material, which is available to authorized users.