Genetic structure of phenotypic robustness in the collaborative cross mouse diallel panel

No relationship found between dental fluctuating asymmetry, birthweight, and birth term in two modern North American samples

OBJECTIVE

Deciduous dental crowns primarily develop during gestation and early infancy and embody early life stress exposures. Composite measures of dental fluctuating asymmetry (DFA) generated from the deciduous teeth may therefore indicate cumulative gestational stress in developmental origins of health and disease (DOHaD) studies. This study examines whether higher composite measures of deciduous DFA are associated with low birthweight and prematurity, two aspects of birth phenotype consistently associated with increased morbidity and mortality risks in adulthood.

SUBJECTS AND METHODS

We evaluated associations between composite deciduous DFA, birthweight, and birth term in two contemporary North American samples: an autopsy sample from New Mexico (n = 94), and sample from a growth cohort study in Burlington, Ontario (n = 304). Dental metric data for each sample was collected from postmortem CT scans and dental casts, respectively. Composite DFA was estimated using buccolingual (BL) and mesiodistal (MD) crown diameters from paired deciduous teeth.

RESULTS

Contrary to expectations, the results of linear regression indicated no significant relationship between birthweight and DFA, or birth term and DFA, in either sample.

CONCLUSIONS

Deciduous DFA does not predict aspects of birth phenotype associated with gestational stress. Birthweight and birth term are plastic relative to the more developmentally stable deciduous dentition, which may only subtly embody early life stress. We suggest that deciduous DFA should be utilized with caution in DOHaD studies until its relationship with gestational stress is clarified.

Mouse hybrid genome mediates diverse brain phenotypes with the specificity of reciprocal crosses

Hybrid species have more genetic diversity than their parents. However, the impact of the hybrid genome of reciprocal crosses on brain function remains largely unknown. We performed behavioral, molecular, and neuronal analyses on C57BL/6J mice (B6), CAST/EiJ mice (CAST), and hybrid mice resulting from reciprocal crosses of the two strains, B6/CAST F1i and B6/CAST F1r, respectively. Hybrid mice displayed greater motor strength and coordination, food grinding, social dominance, and less sociability compared to their parental strains. Parental origin influenced body weight, locomotor speed, and heat nociception of hybrid mice. Parental origin, cell type, and the interaction of both affected expression patterns of hybrid genomes including imprinted genes. There was a correlation between affected genes and corresponding behavioral phenotypes. Hybrid genomes mediated neuronal activity in the locus coeruleus, a brain region implicated in arousal, adaptive behaviors, and sleep-wake cycle due to its norepinephrine projections throughout the central nervous system. The comprehensive brain phenotypes in these hybrid mice reveal important functional readouts associated with interactions of hybrid genomes and impacts of parental genomes.

Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle

Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.

Developmental instability and phenotypic evolution in a small and isolated bear population

We explored fluctuating asymmetry (FA) and morphological integration (MI) in the skull of the small, highly inbred and divergent Apennine bear (Ursus arctos marsicanus), to explore its uniqueness and investigate any potential effects of inbreeding depression. We used 3D geometric morphometrics contrasting Apennine bears with other two large outbred bear populations from Scandinavia and Kamchatka as controls. Shape divergence and variability were explored by a principal component analysis on aligned coordinates of 39 landmarks. Procrustes ANOVA, morphological disparity and the global integration index were used to explore FA, shape variance and MI. By remarking Apennine bears as a highly divergent phenotype, we recorded the highest FA and deviation from self-similarity compared with the other two control populations. We conclude that Apennine bears are likely facing developmental instability as a consequence of inbreeding depression, whereas the divergent trait covariance pattern may represent a potential source of evolutionary novelties. We discuss the implications for the conservation and management of this imperiled taxon.

Schizophrenia: Developmental Variability Interacts with Risk Factors to Cause the Disorder: Nonspecific Variability-Enhancing Factors Combine with Specific Risk Factors to Cause Schizophrenia

A new etiological model is proposed for schizophrenia that combines variability-enhancing nonspecific factors acting during development with more specific risk factors. This model is better suited than the current etiological models of schizophrenia, based on the risk factors paradigm, for predicting and/or explaining several important findings about schizophrenia: high co-morbidity rates, low specificity of many risk factors, and persistence in the population of the associated genetic polymorphisms. Compared with similar models, e.g., de-canalization, common psychopathology factor, sexual-selection, or differential sensitivity to the environment, this proposal is more general and integrative. Recently developed research methods have proven the existence of genetic and environmental factors that enhance developmental variability. Applying such methods to newly collected or already available data can allow for testing the hypotheses upon which this model is built. If validated, this model may change the understanding of the etiology of schizophrenia, the research models, and preventionbrk paradigms.

Epistasis regulates the developmental stability of the mouse craniofacial shape

Fluctuating asymmetry is a classic concept linked to organismal development. It has traditionally been used as a measure of developmental instability, which is the inability of an organism to buffer environmental fluctuations during development. Developmental stability has a genetic component that influences the final phenotype of the organism and can lead to congenital disorders. According to alternative hypotheses, this genetic component might be either the result of additive genetic effects or a by-product of developmental gene networks. Here we present a genome-wide association study of the genetic architecture of fluctuating asymmetry of the skull shape in mice. Geometric morphometric methods were applied to quantify fluctuating asymmetry: we estimated fluctuating asymmetry as Mahalanobis distances to the mean asymmetry, correcting first for genetic directional asymmetry. We applied the marginal epistasis test to study epistasis among genomic regions. Results showed no evidence of additive effects but several interacting regions significantly associated with fluctuating asymmetry. Among the candidate genes overlapping these interacting regions we found an over-representation of genes involved in craniofacial development. A gene network is likely to be associated with skull developmental stability, and genes originally described as buffering genes (e.g., Hspa2) might occupy central positions within these networks, where regulatory elements may also play an important role. Our results constitute an important step in the exploration of the molecular roots of developmental stability and the first empirical evidence about its genetic architecture.

On Zebrafish Disease Models and Matters of the Heart

Coronary artery disease (CAD) is the leading form of cardiovascular disease (CVD), which is the primary cause of mortality worldwide. It is a complex disease with genetic and environmental risk factor contributions. Reports in human and mammalian models elucidate age-associated changes in cardiac function. The diverse mechanisms involved in cardiac diseases remain at the center of the research interest to identify novel strategies for prevention and therapy. Zebrafish (Danio rerio) have emerged as a valuable vertebrate model to study cardiovascular development over the last few decades. The facile genetic manipulation via forward and reverse genetic approaches combined with noninvasive, high-resolution imaging and phenotype-based screening has provided new insights to molecular pathways that orchestrate cardiac development. Zebrafish can recapitulate human cardiac pathophysiology due to gene and regulatory pathways conservation, similar heart rate and cardiac morphology and function. Thus, generations of zebrafish models utilize the functional analysis of genes involved in CAD, which are derived from large-scale human population analysis. Here, we highlight recent studies conducted on cardiovascular research focusing on the benefits of the combination of genome-wide association studies (GWAS) with functional genomic analysis in zebrafish. We further summarize the knowledge obtained from zebrafish studies that have demonstrated the architecture of the fundamental mechanisms underlying heart development, homeostasis and regeneration at the cellular and molecular levels.

Mouse Skull Mean Shape and Shape Robustness Rely on Different Genetic Architectures and Different Loci

The genetic architecture of skull shape has been extensively studied in mice and the results suggest a highly polygenic and additive basis. In contrast few studies have explored the genetic basis of the skull variability. Canalization and developmental stability are the two components of phenotypic robustness. They have been proposed to be emergent properties of the genetic networks underlying the development of the trait itself, but this hypothesis has been rarely tested empirically. Here we use outbred mice to investigate the genetic architecture of canalization of the skull shape by implementing a genome-wide marginal epistatic test on 3D geometric morphometric data. The same data set had been used previously to explore the genetic architecture of the skull mean shape and its developmental stability. Here, we address two questions: (1) Are changes in mean shape and changes in shape variance associated with the same genomic regions? and (2) Do canalization and developmental stability rely on the same loci and genetic architecture and do they involve the same patterns of shape variation? We found that unlike skull mean shape, among-individual shape variance and fluctuating asymmetry (FA) show a total lack of additive effects. They are both associated with complex networks of epistatic interactions involving many genes (protein-coding and regulatory elements). Remarkably, none of the genomic loci affecting mean shape contribute these networks despite their enrichment for genes involved in craniofacial variation and diseases. We also found that the patterns of shape FA and individual variation are largely similar and rely on similar multilocus epistatic genetic networks, suggesting that the processes channeling variation within and among individuals are largely common. However, the loci involved in these two networks are completely different. This in turn underlines the difference in the origin of the variation at these two levels, and points at buffering processes that may be specific to each level.

QTL Mapping on a Background of Variance Heterogeneity

Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such "background variance heterogeneity" (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene's test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term "mean-variance QTL mapping", to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight.

QTL Mapping on a Background of Variance Heterogeneity

Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such "background variance heterogeneity" (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene's test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term "mean-variance QTL mapping", to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight.

Redundancy, Feedback, and Robustness in the Arabidopsis thaliana BZR/BEH Gene Family

Organismal development is remarkably robust, tolerating stochastic errors to produce consistent, so-called canalized adult phenotypes. The mechanistic underpinnings of developmental robustness are poorly understood, but recent studies implicate certain features of genetic networks such as functional redundancy, connectivity, and feedback. Here, we examine the BZR/BEH gene family, whose function contributes to embryonic stem development in the plant Arabidopsis thaliana, to test current assumptions on functional redundancy and trait robustness. Our analyses of BZR/BEH gene mutants and mutant combinations revealed that functional redundancy among these gene family members is not necessary for trait robustness. Connectivity is another commonly cited determinant of robustness; however, we found no correlation between connectivity among gene family members or their connectivity with other transcription factors and effects on developmental robustness. Instead, our data suggest that BEH4, the earliest diverged family member, modulates developmental robustness. We present evidence indicating that regulatory cross-talk among gene family members is integrated by BEH4 to promote wild-type levels of developmental robustness. Further, the chaperone HSP90, a known determinant of developmental robustness, appears to act via BEH4 in maintaining robustness of embryonic stem length. In summary, we demonstrate that even among closely related transcription factors, trait robustness can arise through the activity of a single gene family member, challenging common assumptions about the molecular underpinnings of robustness.

The developmental-genetics of canalization

Canalization, or robustness to genetic or environmental perturbations, is fundamental to complex organisms. While there is strong evidence for canalization as an evolved property that varies among genotypes, the developmental and genetic mechanisms that produce this phenomenon are very poorly understood. For evolutionary biology, understanding how canalization arises is important because, by modulating the phenotypic variation that arises in response to genetic differences, canalization is a determinant of evolvability. For genetics of disease in humans and for economically important traits in agriculture, this subject is important because canalization is a potentially significant cause of missing heritability that confounds genomic prediction of phenotypes. We review the major lines of thought on the developmental-genetic basis for canalization. These fall into two groups. One proposes specific evolved molecular mechanisms while the other deals with robustness or canalization as a more general feature of development. These explanations for canalization are not mutually exclusive and they overlap in several ways. General explanations for canalization are more likely to involve emergent features of development than specific molecular mechanisms. Disentangling these explanations is also complicated by differences in perspectives between genetics and developmental biology. Understanding canalization at a mechanistic level will require conceptual and methodological approaches that integrate quantitative genetics and developmental biology.

Widespread epistasis regulates glucose homeostasis and gene expression

The relative contributions of additive versus non-additive interactions in the regulation of complex traits remains controversial. This may be in part because large-scale epistasis has traditionally been difficult to detect in complex, multi-cellular organisms. We hypothesized that it would be easier to detect interactions using mouse chromosome substitution strains that simultaneously incorporate allelic variation in many genes on a controlled genetic background. Analyzing metabolic traits and gene expression levels in the offspring of a series of crosses between mouse chromosome substitution strains demonstrated that inter-chromosomal epistasis was a dominant feature of these complex traits. Epistasis typically accounted for a larger proportion of the heritable effects than those due solely to additive effects. These epistatic interactions typically resulted in trait values returning to the levels of the parental CSS host strain. Due to the large epistatic effects, analyses that did not account for interactions consistently underestimated the true effect sizes due to allelic variation or failed to detect the loci controlling trait variation. These studies demonstrate that epistatic interactions are a common feature of complex traits and thus identifying these interactions is key to understanding their genetic regulation.

Most complex traits and diseases are regulated by the combined influence of multiple genetic variants. However, it remains controversial whether these genetic variants independently influence complex traits, and therefore the impact of each variant could be simply added together (additivity), or whether the variants work together to influence trait variation, in which case the combined impact of multiple variants would differ from the summed impact of each individual variant (epistasis). In this study in mice, we discovered that the genetic regulation of blood sugar levels and gene expression in the liver were predominantly controlled by non-additive interactions, whereas body weight was predominantly controlled by additive interactions. Remarkably, the expression level of nearly 25% of all genes in the liver was controlled by non-additive interactions. The non-additive interactions typically acted to return trait values to the levels detected in control mice, thus contributing to a reduction in trait variation. We also demonstrated that not accounting for non-additive interactions significantly underestimated the phenotypic effect of a genetic variant on a particular genetic background, suggesting that many previously identified risk loci may have significantly larger effects on disease susceptibility in a subset of individuals. These studies highlight the importance of understanding interactions between genetic variants to better understand disease risk and personalize clinical care.

A population level atlas of Mus musculus craniofacial skeleton and automated image-based shape analysis

Laboratory mice are staples for evo/devo and genetics studies. Inbred strains provide a uniform genetic background to manipulate and understand gene-environment interactions, while their crosses have been instrumental in studies of genetic architecture, integration and modularity, and mapping of complex biological traits. Recently, there have been multiple large-scale studies of laboratory mice to further our understanding of the developmental basis, evolution, and genetic control of shape variation in the craniofacial skeleton (i.e. skull and mandible). These experiments typically use micro-computed tomography (micro-CT) to capture the craniofacial phenotype in 3D and rely on manually annotated anatomical landmarks to conduct statistical shape analysis. Although the common choice for imaging modality and phenotyping provides the potential for collaborative research for even larger studies with more statistical power, the investigator (or lab-specific) nature of the data collection hampers these efforts. Investigators are rightly concerned that subtle differences in how anatomical landmarks were recorded will create systematic bias between studies that will eventually influence scientific findings. Even if researchers are willing to repeat landmark annotation on a combined dataset, different lab practices and software choices may create obstacles for standardization beyond the underlying imaging data. Here, we propose a freely available analysis system that could assist in the standardization of micro-CT studies in the mouse. Our proposal uses best practices developed in biomedical imaging and takes advantage of existing open-source software and imaging formats. Our first contribution is the creation of a synthetic template for the adult mouse craniofacial skeleton from 25 inbred strains and five F1 crosses that are widely used in biological research. The template contains a fully segmented cranium, left and right hemi-mandibles, endocranial space, and the first few cervical vertebrae. We have been using this template in our lab to segment and isolate cranial structures in an automated fashion from a mixed population of mice, including craniofacial mutants, aged 4-12.5 weeks. As a secondary contribution, we demonstrate an application of nearly automated shape analysis, using symmetric diffeomorphic image registration. This approach, which we call diGPA, closely approximates the popular generalized Procrustes analysis (GPA) but negates the collection of anatomical landmarks. We achieve our goals by using the open-source advanced normalization tools (ANT) image quantification library, as well as its associated R library (ANTsR) for statistical image analysis. Finally, we make a plea to investigators to commit to using open imaging standards and software in their labs to the extent possible to increase the potential for data exchange and improve the reproducibility of findings. Future work will incorporate more anatomical detail (such as individual cranial bones, turbinals, dentition, middle ear ossicles) and more diversity into the template.

Development Shapes a Consistent Inbreeding Effect in Mouse Crania of Different Line Crosses

Development translates genetic variation into a multivariate pattern of phenotypic variation, distributing it among traits in a nonuniform manner. As developmental processes are largely shared within species, this suggests that heritable phenotypic variation will be patterned similarly, in spite of the different segregating alleles. To investigate developmental effect on the variational pattern in the shape of the mouse skull across genetically differentiated lines, we employed the full set of reciprocal crosses (a.k.a. diallel) between eight inbred mouse strains of the Collaborative Cross Project. We used geometric morphometrics and multivariate analysis to capture cranial size and shape changes in 8 parentals and their 54 F1 crosses. The high heterozygosity generated in the F1 crosses allowed us to compare the multivariate deviations of the F1 phenotypes from the expected midparental phenotypes in different haplotype combinations. In contrast to body weight, we found a high degree of nonadditive deviation in craniofacial shape. Whereas the phenotypic and genetic divergence of parental strains manifested in high dimensionality of additive effects, the nonadditive deviations exhibited lesser dimensionality and in particular a strikingly coherent direction in shape space. We interpret this finding as evidence for a strong structuring effect of a relatively small set of developmental processes on the mapping of genetic to phenotypic variation.