Colloquium papers: Numbering the hairs on our heads: the shared challenge and promise of phenomics

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets

Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.

Phenomics enables measurement of complex responses of developing animals to global environmental drivers

Phenomics offers technological advances for high-dimensional phenotyping, facilitating rapid, high-throughput assessment of physiological performance and has proven invaluable in global research challenges including drug discovery and food security. However, this rapidly growing discipline has remained largely inaccessible to the increasingly urgent challenge of assessing organismal functional sensitivity to global change drivers. Here, we investigate the response of an ecologically important marine invertebrate to multiple environmental drivers using Energy Proxy Traits (EPTs), a new approach for measuring complex phenotypes captured on video as a spectrum of energy levels across different temporal frequencies in fluctuating pixel values. We imaged three developmental stages of the common prawn Palaemon serratus at different salinities and temperatures, and measured EPTs and heart rate, a major proxy of physiological performance in ectotherms present across stages. Significant interactions were detected between temperature, developmental stage and salinity in frequency-specific energy levels. Despite cardiac activity being a significant contributor to the EPT spectra, treatment interactions were different from those observed on EPTs, highlighting additional phenotypic drivers of EPTs. Elevated temperature resulted in a shift of the EPT spectra towards higher frequency signals, indicating a reallocation of resources within the phenome. Using a non-linear dimensionality reduction, we interrogated the responses of EPT spectra in high-dimensional space. We discovered complex developmental-stage specific sensitivities, highlighting both the complexity of phenotypic responses, and the limits of using univariate approaches with pre-selected traits to assess responses to multiple global environmental drivers. EPTs are a high-dimensional, transferrable method of phenotyping, and are therefore highly relevant to addressing the current limitations of traditional methods of phenotyping applied to assessing biological sensitivity to drivers of global change. We predict that EPTs will become an important tool for indiscriminate phenotyping, transferrable between species, developmental stages and experimental designs.

Automated morphological phenotyping using learned shape descriptors and functional maps: A novel approach to geometric morphometrics

The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation, area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks, and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit.

Unveiling the third dimension in morphometry with automated quantitative volumetric computations

As computed tomography and related technologies have become mainstream tools across a broad range of scientific applications, each new generation of instrumentation produces larger volumes of more-complex 3D data. Lagging behind are step-wise improvements in computational methods to rapidly analyze these new large, complex datasets. Here we describe novel computational methods to capture and quantify volumetric information, and to efficiently characterize and compare shape volumes. It is based on innovative theoretical and computational reformulation of volumetric computing. It consists of two theoretical constructs and their numerical implementation: the spherical wave decomposition (SWD), that provides fast, accurate automated characterization of shapes embedded within complex 3D datasets; and symplectomorphic registration with phase space regularization by entropy spectrum pathways (SYMREG), that is a non-linear volumetric registration method that allows homologous structures to be correctly warped to each other or a common template for comparison. Together, these constitute the Shape Analysis for Phenomics from Imaging Data (SAPID) method. We demonstrate its ability to automatically provide rapid quantitative segmentation and characterization of single unique datasets, and both inter-and intra-specific comparative analyses. We go beyond pairwise comparisons and analyze collections of samples from 3D data repositories, highlighting the magnified potential our method has when applied to data collections. We discuss the potential of SAPID in the broader context of generating normative morphologies required for meaningfully quantifying and comparing variations in complex 3D anatomical structures and systems.

Spectral phenotyping of embryonic development reveals integrative thermodynamic responses

Background

Energy proxy traits (EPTs) are a novel approach to high dimensional organismal phenotyping that quantify the spectrum of energy levels within different temporal frequencies associated with mean pixel value fluctuations from video. They offer significant potential in addressing the phenotyping bottleneck in biology and are effective at identifying lethal endpoints and measuring specific functional traits, but the extent to which they might contribute additional understanding of the phenotype remains unknown. Consequently, here we test the biological significance of EPTs and their responses relative to fundamental thermodynamic principles. We achieve this using the entire embryonic development of Radix balthica, a freshwater pond snail, at different temperatures (20, 25 & 30 °C) and comparing responses against predictions from Arrhenius’ equation (Q₁₀ = 2).

Results

We find that EPTs are thermally sensitive and their spectra of frequency response enable effective high-dimensional treatment clustering throughout organismal development. Temperature-specific deviation in EPTs from thermodynamic predictions were evident and indicative of physiological mitigation, although they differed markedly in their responses from manual measures. The EPT spectrum was effective in capturing aspects of the phenotype predictive of biological outcomes, and suggest that EPTs themselves may reflect levels of energy turnover.

Conclusions

Whole-organismal biology is incredibly complex, and this contributes to the challenge of developing universal phenotyping approaches. Here, we demonstrate the biological relevance of a new holistic approach to phenotyping that is not constrained by preconceived notions of biological importance. Furthermore, we find that EPTs are an effective approach to measuring even the most dynamic life history stages.

Supplementary information

The online version contains supplementary material available at 10.1186/s12859-021-04152-1.

Phenotypic trait variability as an indication of adaptive capacity in a cosmopolitan marine diatom

Determining the adaptive capacity of marine phytoplankton is important in predicting changes in phytoplankton responses to ocean warming. Phytoplankton may consist of high levels of standing phenotypic and genetic variability, the basis of rapid evolution; however, few studies have quantified trait variability within and amongst closely related diatom species. Using 35 clonal cultures of the ubiquitous marine diatom Leptocylindrus isolated from six locations, spanning 2000 km of the south-eastern Australian coastline, we found evidence of significant intraspecific morphological and metabolic trait variability, which for 8 of 9 traits (growth rate, biovolume, C:N, silica deposition, silica incorporation rate, chl-a, and photosynthetic efficiency under dark adapted, growth irradiance, and high-light adaptation) were greater within a species than between species. Moreover, only two traits revealed a latitudinal trend with strains isolated from lower latitudes showing significantly higher silicification rates and protein:lipid content compared to their higher latitude counterparts. These data mirror recent studies on diatom intraspecific genetic diversity, which has found comparable levels of genetic diversity at a single site to those thousands of kilometres apart, and provide evidence of a functional role of diatom diversity that will allow for rapid adaptation via ecological selection on standing variation in response to changing conditions.

A CURE for a Major Challenge in Phenomics: A Practical Guide to Implementing a Quantitative Specimen-Based Undergraduate Research Experience

The measurement and analysis of phenotypes is often a rate-limiting step for many integrative organismal studies but engaging undergraduate researchers can help overcome this challenge. We present a practical guide to implementing a quantitative specimen-based Course-based Undergraduate Research Experience (CURE), which trains students to collect phenotypic data and mentors them through the entire scientific process using the data they help to collect. Direct access to specimens is not necessary to implement this undergraduate research experience, as recent efforts to digitize museum collections along with online image archives allow data extraction to take place in any classroom. We focus in particular on hypothesis development and quantitative skills, as they are essential for modern biological discovery but are rarely emphasized in traditional lecture-based classes. We have implemented this experience, focusing on collecting and analyzing body shape data across fishes, at two institutions with a total of 39 students. It has so far resulted in 14 talks and 4 posters presented by students at local symposia and 2 scientific papers in preparation with undergraduate co-authors. Moreover, the students had a positive experience that, according to their own assessment, improved their critical thinking and analytical skills as well as their knowledge of science and the scientific process.

The odonate phenotypic database, a new open data resource for comparative studies of an old insect order

We present The Odonate Phenotypic Database (OPD): an online data resource of dragonfly and damselfly phenotypes (Insecta: Odonata). Odonata is a relatively small insect order that currently consists of about 6400 species belonging to 32 families. The database consists of multiple morphological, life-history and behavioral traits, and biogeographical information collected from literature sources. We see taxon-specific phenotypic databases from Odonata and other organismal groups as becoming an increasing valuable resource in comparative studies. Our database has phenotypic records for 1011 of all 6400 known odonate species. The database is accessible at http://www.odonatephenotypicdatabase.org/, and a static version with an information file about the variables in the database is archived at Dryad.

Clarifying dairy calf mortality phenotypes through postmortem analysis

Health problems can be thought of as phenotypic expressions of the complex relationships between genes, environments, and phenomes as a whole. Detailed evaluations of phenotypic expressions of illness are required to characterize important biological outcomes. We hypothesized that classifying dairy calf mortality phenotypes via a systematic postmortem analysis would identify different cause-of-death diagnoses than those derived from treatments alone. This cross-sectional study was carried out on a dairy calf ranch in the northwestern United States from June to September 2017 and focused on calves ≤90 d of age. Comparisons were made between causes of death based on 3 levels of information: on-farm treatment records alone, necropsy-based postmortem analyses in addition to treatment records, and Washington Animal Disease Diagnostic Laboratory (WADDL) results in addition to all other information. A total of 210 dairy calves were necropsied during this study, of which 122 cases were submitted to WADDL. Necropsy- and WADDL-derived mortality phenotypes were in almost perfect agreement (Cohen's κ = 0.86) when broadly categorized as diarrhea, respiratory, diarrhea and respiratory combined, or other causes. The level of agreement between on-farm treatment records and postmortem-derived results was low and varied by the level of diagnostic detail provided. There was just fair agreement (κ = 0.22) between treatment-based and necropsy-based phenotypes without WADDL input and only slight agreement (κ = 0.13) between treatment-based and corresponding necropsy-based phenotypes with WADDL input. Even for those cases in which causes of death aligned along a comparable pathologic spectrum, the lack of detail inherent to standard treatment-based causes of death failed to identify meaningful target areas for intervention. This was especially apparent for numerous cases of necrotizing enteritis and typhlitis (cecal inflammation) that were variously categorized as diarrhea and pneumonia by treatment-based diagnoses. The specificity of these lesions stood in stark contrast to the otherwise generic cause of death diagnoses derived from treatments. The findings from this study supported the hypothesis and highlighted the value of on-farm necropsies and laboratory-based diagnostics to (1) detect antemortem disease misclassifications, (2) provide detail regarding disease processes and mortality phenotypes, and (3) direct disease mitigation strategies.

A Multivariate Genome-Wide Association Study of Wing Shape in Drosophila melanogaster

Due to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and informative than a series of univariate analyses. However, in most cases, studies of genotype-phenotype relationships have been analyzed only one trait at a time. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the Drosophila melanogaster wing in the Drosophila Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different laboratories. We found 2396 significant SNPs using a 5% false discovery rate cutoff in the multivariate analyses, but just four significant SNPs in univariate analyses of scores on the first 20 principal component axes. One quarter of these initially significant SNPs retain their effects in regularized models that take into account population structure and linkage disequilibrium. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. We exploit this fact to show that the effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. A subset of SNP effects were replicable in an unrelated panel of inbred lines. Association studies that take a phenomic approach, considering many traits simultaneously, are an important complement to the power of genomics.

A high-throughput and open-source platform for embryo phenomics

Phenomics has the potential to facilitate significant advances in biology but requires the development of high-throughput technologies capable of generating and analysing high-dimensional data. There are significant challenges associated with building such technologies, not least those required for investigating dynamic processes such as embryonic development, during which high rates of temporal, spatial, and functional change are inherently difficult to capture. Here, we present EmbryoPhenomics, an accessible high-throughput platform for phenomics in aquatic embryos comprising an Open-source Video Microscope (OpenVIM) that produces high-resolution videos of multiple embryos under tightly controlled environmental conditions. These videos are then analysed by the Python package Embryo Computer Vision (EmbryoCV), which extracts phenomic data for morphological, physiological, behavioural, and proxy traits during the process of embryonic development. We demonstrate the broad-scale applicability of EmbryoPhenomics in a series of experiments assessing chronic, acute, and multistressor responses to environmental change (temperature and salinity) in >30 million images of >600 embryos of two species with markedly different patterns of development—the pond snail Radix balthica and the marine amphipod Orchestia gammarellus. The challenge of phenomics is significant but so too are the rewards, and it is particularly relevant to the urgent task of assessing complex organismal responses to current rates of environmental change. EmbryoPhenomics can acquire and process data capturing functional, temporal, and spatial responses in the earliest, most dynamic life stages and is potentially game changing for those interested in studying development and phenomics more widely.

EmbryoPhenomics is an open-source technology platform for high-throughput phenome screening of aquatic embryos. This paper demonstrates its application in experiments assessing the sensitivity of aquatic embryos to environmental stress, consisting of more than 600 embryos and more than 30 million images.

Phenomics is the collection of high-dimensional phenotypic data on an organism-wide scale, and it requires high-throughput technologies. However, a lack of technologies for efficiently visualising and measuring whole-organism responses to different environments represents a serious challenge for biologists. This challenge is most apparent when studying complex responses, such as those occurring during the dynamic period of embryonic development, when the phenotype changes markedly through time. Here, we present EmbryoPhenomics (www.embryophenomics.org), a new open-source technological platform comprising high-throughput bioimaging hardware that produces high-resolution video of multiple, developing embryos maintained under controlled environmental conditions and software for automatically quantifying embryo responses from these videos. We demonstrate the broad applicability of EmbryoPhenomics using four experiments assessing responses to global change (elevated temperature and salinity) in which we generate data for more than 600 embryos produced from video comprising more than 30 million images. EmbryoPhenomics was used to capture functional, temporal, and spatial change in morphological, physiological, and behavioural responses in the earliest, most dynamic life stages and addresses a serious bottleneck in biology. Such capabilities are urgently required, particularly within the context of assessing the response of embryos to the current unprecedented rates of global environmental change.

A simple developmental model recapitulates complex insect wing venation patterns

Insect wings are typically supported by thickened struts called veins. These veins form diverse geometric patterns across insects. For many insect species, even the left and right wings from the same individual have veins with unique topological arrangements, and little is known about how these patterns form. We present a large-scale quantitative study of the fingerprint-like "secondary veins." We compile a dataset of wings from 232 species and 17 families from the order Odonata (dragonflies and damselflies), a group with particularly elaborate vein patterns. We characterize the geometric arrangements of veins and develop a simple model of secondary vein patterning. We show that our model is capable of recapitulating the vein geometries of species from other, distantly related winged insect clades.

The Quantitative Genetic Control of Root Architecture in Maize

Roots remain an underexplored frontier in plant genetics despite their well-known influence on plant development, agricultural performance and competition in the wild. Visualizing and measuring root structures and their growth is vastly more difficult than characterizing aboveground parts of the plant and is often simply avoided. The majority of research on maize root systems has focused on their anatomy, physiology, development and soil interaction, but much less is known about the genetics that control quantitative traits. In maize, seven root development genes have been cloned using mutagenesis, but no genes underlying the many root-related quantitative trait loci (QTLs) have been identified. In this review, we discuss whether the maize mutants known to control root development may also influence quantitative aspects of root architecture, including the extent to which they overlap with the most recent maize root trait QTLs. We highlight specific challenges and anticipate the impacts that emerging technologies, especially computational approaches, may have toward the identification of genes controlling root quantitative traits.

Powerful and interpretable behavioural features for quantitative phenotyping of Caenorhabditis elegans

Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provide a rich but high-dimensional representation of behaviour, and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine-learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here, we report a new set of handcrafted features to compactly quantify Caenorhabditis elegans behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild-type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets.

This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

Uneven Distribution of Mutational Variance Across the Transcriptome of Drosophila serrata Revealed by High-Dimensional Analysis of Gene Expression

There are essentially an infinite number of traits that could be measured on any organism, and almost all individual traits display genetic variation, yet substantial genetic variance in a large number of independent traits is not plausible under basic models of selection and mutation. One mechanism that may be invoked to explain the observed levels of genetic variance in individual traits is that pleiotropy results in fewer dimensions of phenotypic space with substantial genetic variance. Multivariate genetic analyses of small sets of functionally related traits have shown that standing genetic variance is often concentrated in relatively few dimensions. It is unknown if a similar concentration of genetic variance occurs at a phenome-wide scale when many traits of disparate function are considered, or if the genetic variance generated by new mutations is also unevenly distributed across phenotypic space. Here, we used a Bayesian sparse factor model to characterize the distribution of mutational variance of 3385 gene expression traits of Drosophila serrata after 27 generations of mutation accumulation, and found that 46% of the estimated mutational variance was concentrated in just 21 dimensions with significant mutational heritability. We show that the extent of concentration of mutational variance into such a small subspace has the potential to substantially bias the response to selection of these traits.

Phenome of pearl quality traits in the mollusc transplant model Pinctada margaritifera

The bivalve Pinctada margaritifera exhibits three main transplant phenotypes derived from the donor (from which a mantle graft tissue, the saibo, is excised), the recipient (into which the saibo is implanted with a nucleus, leading to the formation of a pearl sac “chimera”) and the cultured pearls themselves. This first phenome study on the species derived from a large experimental graft. Transplant phenotype was assessed at three scales: 1) macro, pearl size, colour, grade, 2) micro, pearl surface microstructure, and 3) molecular, biomineralisation gene expression level in saibo and pearl sac tissues. From donor to pearl, the phenome revealed fine variations of quality traits dependent on the position on the mantle where the saibo was cut, whose variation could overlap with inter-individual donor phenotype differences. A single donor phenotype could therefore produce multiple pearl phenotypes at the scale of the saibo position, mirroring its original activity at the mantle position level and the colour and shape of the shell. This phenome study provides essential information on phenotypic trait architecture enabling us to explore and explain the main biological functions and pave the way for a phenomic project on P. margaritifera that could benefit the pearl industry.

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.

Structure and stability of genetic variance-covariance matrices: A Bayesian sparse factor analysis of transcriptional variation in the three-spined stickleback

The genetic variance-covariance matrix (G) is a quantity of central importance in evolutionary biology due to its influence on the rate and direction of multivariate evolution. However, the predictive power of empirically estimated G-matrices is limited for two reasons. First, phenotypes are high-dimensional, whereas traditional statistical methods are tuned to estimate and analyse low-dimensional matrices. Second, the stability of G to environmental effects and over time remains poorly understood. Using Bayesian sparse factor analysis (BSFG) designed to estimate high-dimensional G-matrices, we analysed levels variation and covariation in 10,527 expressed genes in a large (n = 563) half-sib breeding design of three-spined sticklebacks subject to two temperature treatments. We found significant differences in the structure of G between the treatments: heritabilities and evolvabilities were higher in the warm than in the low-temperature treatment, suggesting more and faster opportunity to evolve in warm (stressful) conditions. Furthermore, comparison of G and its phenotypic equivalent P revealed the latter is a poor substitute of the former. Most strikingly, the results suggest that the expected impact of G on evolvability-as well as the similarity among G-matrices-may depend strongly on the number of traits included into analyses. In our results, the inclusion of only few traits in the analyses leads to underestimation in the differences between the G-matrices and their predicted impacts on evolution. While the results highlight the challenges involved in estimating G, they also illustrate that by enabling the estimation of large G-matrices, the BSFG method can improve predicted evolutionary responses to selection.

Evolvability and robustness: A paradox restored

Evolvability and robustness are crucial for the origin and maintenance of complex organisms, but may not be simultaneously achievable as robust traits are also hard to change. Andreas Wagner has proposed a solution to this paradox by arguing that the many-to-few aspect of genotype-phenotype maps creates neutral networks of genotypes coding for the same phenotype. Phenotypes with large networks are genetically robust, but they may also have more neighboring phenotypes and thus higher evolvability. In this paper, we explore the generality of this idea by sampling large numbers of random genotype-phenotype maps for Boolean genotypes and phenotypes. We show that there is indeed a preponderance of positive correlations between the evolvability and robustness of phenotypes within a genotype-phenotype map, but also that there are negative correlations between average evolvability and robustness across maps. We interpret this as predicting a positive correlation across the phenotypic states of a character, but a negative correlation across characters. We also argue that evolvability and robustness tend to be negatively correlated when phenotypes are measured on ordinal or higher scale types. We conclude that Wagner's conjecture of a positive relation between robustness and evolvability is based on strict and somewhat unrealistic biological assumptions.

The "Mendelian Gene" and the "Molecular Gene": Two Relevant Concepts of Genetic Units

We focus here on two prevalent meanings of the word gene in research articles. On one hand, the gene, named here "molecular gene," is a stretch of DNA that is transcribed and codes for an RNA or a polypeptide with a known or presumed function (as in "gene network"), whose exact spatial delimitation on the chromosome remains a matter of debate, especially in cases with alternative splicing, antisense transcripts, etc. On the other hand, the gene, called here "Mendelian gene," is a segregating genetic unit which is detected through phenotypic differences associated with different alleles at the same locus (as in "gene flow"). We show that the "Mendelian gene" concept is still extensively used today in biology research and is sometimes confused with the "molecular gene." We try here to clarify the distinction between both concepts. Efforts to delineate the beginning and the end of the DNA sequence corresponding to the "Mendelian gene" and the "molecular gene" reveal that both entities do not always match. We argue that both concepts are part of two relevant frameworks for explaining the biological world.

Genomic Characterization of the Evolutionary Potential of the Sea Urchin Strongylocentrotus droebachiensis Facing Ocean Acidification

Ocean acidification (OA) is increasing due to anthropogenic CO₂ emissions and poses a threat to marine species and communities worldwide. To better project the effects of acidification on organisms’ health and persistence, an understanding is needed of the 1) mechanisms underlying developmental and physiological tolerance and 2) potential populations have for rapid evolutionary adaptation. This is especially challenging in nonmodel species where targeted assays of metabolism and stress physiology may not be available or economical for large-scale assessments of genetic constraints. We used mRNA sequencing and a quantitative genetics breeding design to study mechanisms underlying genetic variability and tolerance to decreased seawater pH (-0.4 pH units) in larvae of the sea urchin Strongylocentrotus droebachiensis. We used a gene ontology-based approach to integrate expression profiles into indirect measures of cellular and biochemical traits underlying variation in larval performance (i.e., growth rates). Molecular responses to OA were complex, involving changes to several functions such as growth rates, cell division, metabolism, and immune activities. Surprisingly, the magnitude of pH effects on molecular traits tended to be small relative to variation attributable to segregating functional genetic variation in this species. We discuss how the application of transcriptomics and quantitative genetics approaches across diverse species can enrich our understanding of the biological impacts of climate change.

Pleiotropy, constraint, and modularity in the evolution of life histories: insights from genomic analyses

Multicellular organisms display an enormous range of life history (LH) strategies and present an evolutionary conundrum; despite strong natural selection, LH traits are characterized by high levels of genetic variation. To understand the evolution of life histories and maintenance of this variation, the specific phenotypic effects of segregating alleles and the genetic networks in which they act need to be elucidated. In particular, the extent to which LH evolution is constrained by the pleiotropy of alleles contributing to LH variation is generally unknown. Here, we review recent empirical results that shed light on this question, with an emphasis on studies employing genomic analyses. While genome-scale analyses are increasingly practical and affordable, they face limitations of genetic resolution and statistical power. We describe new research approaches that we believe can produce new insights and evaluate their promise and applicability to different kinds of organisms. Two approaches seem particularly promising: experiments that manipulate selection in multiple dimensions and measure phenotypic and genomic response and analytical approaches that take into account genome-wide associations between markers and phenotypes, rather than applying a traditional marker-by-marker approach.

Synergism and Antagonism of Proximate Mechanisms Enable and Constrain the Response to Simultaneous Selection on Body Size and Development Time: An Empirical Test Using Experimental Evolution

Natural selection acts on multiple traits simultaneously. How mechanisms underlying such traits enable or constrain their response to simultaneous selection is poorly understood. We show how antagonism and synergism among three traits at the developmental level enable or constrain evolutionary change in response to simultaneous selection on two focal traits at the phenotypic level. After 10 generations of 25% simultaneous directional selection on all four combinations of body size and development time in Manduca sexta (Sphingidae), the changes in the three developmental traits predict 93% of the response of development time and 100% of the response of body size. When the two focal traits were under synergistic selection, the response to simultaneous selection was enabled by juvenile hormone and ecdysteroids and constrained by growth rate. When the two focal traits were under antagonistic selection, the response to selection was due primarily to change in growth rate and constrained by the two hormonal traits. The approach used here reduces the complexity of the developmental and endocrine mechanisms to three proxy traits. This generates explicit predictions for the evolutionary response to selection that are based on biologically informed mechanisms. This approach has broad applicability to a diverse range of taxa, including algae, plants, amphibians, mammals, and insects.

Whole-Organism Cellular Pathology: A Systems Approach to Phenomics

Phenotype is defined as the state of an organism resulting from interactions between genes, environment, disease, molecular mechanisms, and chance. The purpose of the emerging field of phenomics is to systematically determine and measure phenotypes across biology for the sake of understanding. Phenotypes can affect more than one cell type and life stage, so ideal phenotyping would include the state of every cell type within the context of both tissue architecture and the whole organism at each life stage. In medicine, high-resolution anatomic assessment of phenotype is obtained from histology. Histology's interpretative power, codified by Virchow as cellular pathology, is derived from its ability to discern diagnostic and characteristic cellular changes in diseased tissues. Cellular pathology is observed in every major human disease and relies on the ability of histology to detect cellular change in any cell type due to unbiased pan-cellular staining, even in optically opaque tissues. Our laboratory has shown that histology is far more sensitive than stereomicroscopy for detecting phenotypes in zebrafish mutants. Those studies have also shown that more complete sampling, greater consistency in sample orientation, and the inclusion of phenotypes extending over longer length scales would provide greater coverage of common phenotypes. We are developing technical approaches to achieve an ideal detection of cellular pathology using an improved form of X-ray microtomography that retains the strengths and addresses the weaknesses of histology as a screening tool. We are using zebrafish as a vertebrate model based on the overlaps between zebrafish and mammalian tissue architecture, and a body size small enough to allow whole-organism, volumetric imaging at cellular resolution. Automation of whole-organism phenotyping would greatly increase the value of phenomics. Potential societal benefits would include reduction in the cost of drug development, a reduction in the incidence of unexpected severe drug and environmental toxicity, and more rapid elucidation of the contributions of genes and the environment to phenotypes, including the validation of candidate disease alleles identified in population and personal genetics.

Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability

BACKGROUND

Recent advances in genome sequencing technologies have shifted the research bottleneck in plant sciences from genotyping to phenotyping. This shift has driven the development of phenomics, high-throughput non-invasive phenotyping technologies.

RESULTS

We describe an automated high-throughput phenotyping platform, the Phenovator, capable of screening 1440 Arabidopsis plants multiple times per day for photosynthesis, growth and spectral reflectance at eight wavelengths. Using this unprecedented phenotyping capacity, we have been able to detect significant genetic differences between Arabidopsis accessions for all traits measured, across both temporal and environmental scales. The high frequency of measurement allowed us to observe that heritability was not only trait specific, but for some traits was also time specific.

CONCLUSIONS

Such continuous real-time non-destructive phenotyping will allow detailed genetic and physiological investigations of the kinetics of plant homeostasis and development. The success and ultimate outcome of a breeding program will depend greatly on the genetic variance which is sampled. Our observation of temporal fluctuations in trait heritability shows that the moment of measurement can have lasting consequences. Ultimately such phenomic level technologies will provide more dynamic insights into plant physiology, and the necessary data for the omics revolution to reach its full potential.

Morphometrics, 3D Imaging, and Craniofacial Development

Recent studies have shown how volumetric imaging and morphometrics can add significantly to our understanding of morphogenesis, the developmental basis for variation, and the etiology of structural birth defects. On the other hand, the complex questions and diverse imaging data in developmental biology present morphometrics with more complex challenges than applications in virtually any other field. Meeting these challenges is necessary in order to understand the mechanistic basis for variation in complex morphologies. This chapter reviews the methods and theory that enable the application of modern landmark-based morphometrics to developmental biology and craniofacial development, in particular. We discuss the theoretical foundations of morphometrics as applied to development and review the basic approaches to the quantification of morphology. Focusing on geometric morphometrics, we discuss the principal statistical methods for quantifying and comparing morphological variation and covariation structure within and among groups. Finally, we discuss the future directions for morphometrics in developmental biology that will be required for approaches that enable quantitative integration across the genotype-phenotype map.

GRAPHIE: graph based histology image explorer

Background

Histology images comprise one of the important sources of knowledge for phenotyping studies in systems biology. However, the annotation and analyses of histological data have remained a manual, subjective and relatively low-throughput process.

Results

We introduce Graph based Histology Image Explorer (GRAPHIE)-a visual analytics tool to explore, annotate and discover potential relationships in histology image collections within a biologically relevant context. The design of GRAPHIE is guided by domain experts' requirements and well-known InfoVis mantras. By representing each image with informative features and then subsequently visualizing the image collection with a graph, GRAPHIE allows users to effectively explore the image collection. The features were designed to capture localized morphological properties in the given tissue specimen. More importantly, users can perform feature selection in an interactive way to improve the visualization of the image collection and the overall annotation process. Finally, the annotation allows for a better prospective examination of datasets as demonstrated in the users study. Thus, our design of GRAPHIE allows for the users to navigate and explore large collections of histology image datasets.

Conclusions

We demonstrated the usefulness of our visual analytics approach through two case studies. Both of the cases showed efficient annotation and analysis of histology image collection.

An image database of Drosophila melanogaster wings for phenomic and biometric analysis

BACKGROUND

Extracting important descriptors and features from images of biological specimens is an ongoing challenge. Features are often defined using landmarks and semi-landmarks that are determined a priori based on criteria such as homology or some other measure of biological significance. An alternative, widely used strategy uses computational pattern recognition, in which features are acquired from the image de novo. Subsets of these features are then selected based on objective criteria. Computational pattern recognition has been extensively developed primarily for the classification of samples into groups, whereas landmark methods have been broadly applied to biological inference.

RESULTS

To compare these approaches and to provide a general community resource, we have constructed an image database of Drosophila melanogaster wings - individually identifiable and organized by sex, genotype and replicate imaging system - for the development and testing of measurement and classification tools for biological images. We have used this database to evaluate the relative performance of current classification strategies. Several supervised parametric and nonparametric machine learning algorithms were used on principal components extracted from geometric morphometric shape data (landmarks and semi-landmarks). For comparison, we also classified phenotypes based on de novo features extracted from wing images using several computer vision and pattern recognition methods as implemented in the Bioimage Classification and Annotation Tool (BioCAT).

CONCLUSIONS

Because we were able to thoroughly evaluate these strategies using the publicly available Drosophila wing database, we believe that this resource will facilitate the development and testing of new tools for the measurement and classification of complex biological phenotypes.

Finding our way through phenotypes

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.

Display of individuality in avoidance behavior and risk assessment of inbred mice

Factors determining individuality are still poorly understood. Rodents are excellent model organisms to study individuality, due to a rich behavioral repertoire and the availability of well-characterized isogenic populations. However, most current behavioral assays for rodents have short test duration in novel test environments and require human interference, which introduce coercion, thereby limiting the assessment of naturally occurring individuality. Thus, we developed an automated behavior system to longitudinally monitor conditioned fear for assessing PTSD-like behavior in individual mice. The system consists of a safe home compartment connected to a risk-prone test compartment (TC). Entry and exploration of the TC is solely based on deliberate choice determined by individual fear responsiveness and fear extinction. In this novel ethological assay, C57BL/6J mice show homogeneous responses after shock exposure (innate fear), but striking variation in long-lasting fear responses based on avoidance and risk assessment (learned fear), including automated stretch-attend posture quantification. TC entry (retention) latencies after foot shock differed >24 h and the re-explored TC area differed >50% among inbred mice. Next, we compared two closely related C57BL/6 substrains. Despite substantial individual differences, previously observed higher fear of C57BL/6N vs. C57BL/6J mice was reconfirmed, whereas fear extinction was fast and did not differ. The observed variation in fear expression in isogenic mice suggests individual differences in coping style with PTSD-like avoidance. Investigating the assumed epigenetic mechanisms, with reduced interpretational ambiguity and enhanced translational value in this assay, may help improve understanding of personality type-dependent susceptibility and resilience to neuropsychiatric disorders such as PTSD.

Pleiotropic mutations are subject to strong stabilizing selection

The assumption that pleiotropic mutations are more deleterious than mutations with more restricted phenotypic effects is an important premise in models of evolution. However, empirical evidence supporting this assumption is limited. Here, we estimated the strength of stabilizing selection on mutations affecting gene expression in male Drosophila serrata. We estimated the mutational variance (VM) and the standing genetic variance (VG) from two well-matched panels of inbred lines: a panel of mutation accumulation (MA) lines derived from a single inbred ancestral line and a panel of inbred lines derived from an outbred population. For 855 gene-expression traits, we estimated the strength of stabilizing selection as s = VM/VG. Selection was observed to be relatively strong, with 17% of traits having s > 0.02, a magnitude typically associated with life-history traits. Randomly assigning expression traits to five-trait sets, we used factor analytic mixed modeling in the MA data set to identify covarying traits that shared pleiotropic mutations. By assigning traits to the same trait sets in the outbred line data set, we then estimated s for the combination of traits affected by pleiotropic mutation. For these pleiotropic combinations, the median s was three times greater than s acting on the individual component traits, and 46% of the pleiotropic trait combinations had s > 0.02. Although our analytical approach was biased toward detecting mutations with relatively large effects, likely overestimating the average strength of selection, our results provide widespread support for the prediction that stronger selection can act against mutations with pleiotropic effects.

Quantification of facial skeletal shape variation in fibroblast growth factor receptor-related craniosynostosis syndromes

BACKGROUND

fibroblast growth factor receptor (FGFR) -related craniosynostosis syndromes are caused by many different mutations within FGFR-1, 2, 3, and certain FGFR mutations are associated with more than one clinical syndrome. These syndromes share coronal craniosynostosis and characteristic facial skeletal features, although Apert syndrome (AS) is characterized by a more dysmorphic facial skeleton relative to Crouzon (CS), Muenke (MS), or Pfeiffer syndromes.

METHODS

Here we perform a detailed three-dimensional evaluation of facial skeletal shape in a retrospective sample of cases clinically and/or genetically diagnosed as AS, CS, MS, and Pfeiffer syndrome to quantify variation in facial dysmorphology, precisely identify specific facial features pertaining to these four syndromes, and further elucidate what knowledge of the causative FGFR mutation brings to our understanding of these syndromes.

RESULTS

Our results confirm a strong correspondence between genotype and facial phenotype for AS and MS with severity of facial dysmorphology diminishing from Apert FGFR2(S252W) to Apert FGFR2(P253R) to MS. We show that AS facial shape variation is increased relative to CS, although CS has been shown to be caused by numerous distinct mutations within FGFRs and reduced dosage in ERF.

CONCLUSION

Our quantitative analysis of facial phenotypes demonstrate subtle variation within and among craniosynostosis syndromes that might, with further research, provide information about the impact of the mutation on facial skeletal and nonskeletal development. We suggest that precise studies of the phenotypic consequences of genetic mutations at many levels of analysis should accompany next-generation genetic research and that these approaches should proceed cooperatively.

Using flatbed scanners to collect high-resolution time-lapsed images of the arabidopsis root gravitropic response

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories can face is the development and attainment of these methods. Observation of phenotypes in a process of interest is a typical objective of research labs studying gene function and this is often achieved through image capture. A particular process that is amenable to observation using imaging approaches is the corrective growth of a seedling root that has been displaced from alignment with the gravity vector. Imaging platforms used to measure the root gravitropic response can be expensive, relatively low in throughput, and/or labor intensive. These issues have been addressed by developing a high-throughput image capture method using inexpensive, yet high-resolution, flatbed scanners. Using this method, images can be captured every few minutes at 4,800 dpi. The current setup enables collection of 216 individual responses per day. The image data collected is of ample quality for image analysis applications.

The transcriptomic basis of oviposition behaviour in the parasitoid wasp Nasonia vitripennis

Linking behavioural phenotypes to their underlying genotypes is crucial for uncovering the mechanisms that underpin behaviour and for understanding the origins and maintenance of genetic variation in behaviour. Recently, interest has begun to focus on the transcriptome as a route for identifying genes and gene pathways associated with behaviour. For many behavioural traits studied at the phenotypic level, we have little or no idea of where to start searching for "candidate" genes: the transcriptome provides such a starting point. Here we consider transcriptomic changes associated with oviposition in the parasitoid wasp Nasonia vitripennis. Oviposition is a key behaviour for parasitoids, as females are faced with a variety of decisions that will impact offspring fitness. These include choosing between hosts of differing quality, as well as making decisions regarding clutch size and offspring sex ratio. We compared the whole-body transcriptomes of resting or ovipositing female Nasonia using a "DeepSAGE" gene expression approach on the Illumina sequencing platform. We identified 332 tags that were significantly differentially expressed between the two treatments, with 77% of the changes associated with greater expression in resting females. Oviposition therefore appears to focus gene expression away from a number of physiological processes, with gene ontologies suggesting that aspects of metabolism may be down-regulated during egg-laying. Nine of the most abundant differentially expressed tags showed greater expression in ovipositing females though, including the genes purity-of-essence (associated with behavioural phenotypes in Drosophila) and glucose dehydrogenase (GLD). The GLD protein has been implicated in sperm storage and release in Drosophila and so provides a possible candidate for the control of sex allocation by female Nasonia during oviposition. Oviposition in Nasonia therefore clearly modifies the transcriptome, providing a starting point for the genetic dissection of oviposition.

Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices

Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.