Longitudinal hierarchical Bayesian models of covariate effects on airway and alveolar nitric oxide.

Biomarkers such as exhaled nitric oxide (FeNO), a marker of airway inflammation, have applications in the study of chronic respiratory disease where longitudinal studies of within-participant changes in the biomarker are particularly relevant. A cutting-edge approach to assessing FeNO, called multiple flow FeNO, repeatedly assesses FeNO across a range of expiratory flow rates at a single visit and combines these data with a deterministic model of lower respiratory tract NO to estimate parameters quantifying airway wall and alveolar NO sources. Previous methodological work for multiple flow FeNO has focused on methods for data from a single participant or from cross-sectional studies. Performance of existing ad hoc two-stage methods for longitudinal multiple flow FeNO in cohort or panel studies has not been evaluated. In this paper, we present a novel longitudinal extension to a unified hierarchical Bayesian (L_U_HB) model relating longitudinally assessed multiple flow FeNO to covariates. In several simulation study scenarios, we compare the L_U_HB method to other unified and two-stage frequentist methods. In general, L_U_HB produced unbiased estimates, had good power, and its performance was not sensitive to the magnitude of the association with a covariate and correlations between NO parameters. In an application relating height to longitudinal multiple flow FeNO in schoolchildren without asthma, unified analysis methods estimated positive, statistically significant associations of height with airway and alveolar NO concentrations and negative associations with airway wall diffusivity while estimates from two-stage methods were smaller in magnitude and sometimes non-significant.

Functional human genes typically exhibit epigenetic conservation.

Recent DepMap CRISPR-Cas9 single gene disruptions have identified genes more essential to proliferation in tissue culture. It would be valuable to translate these finding with measurements more practical for human tissues. Here we show that DepMap essential genes and other literature curated functional genes exhibit cell-specific preferential epigenetic conservation when DNA methylation measurements are compared between replicate cell lines and between intestinal crypts from the same individual. Culture experiments indicate that epigenetic drift accumulates through time with smaller differences in more functional genes. In NCI-60 cell lines, greater targeted gene conservation correlated with greater drug sensitivity. These studies indicate that two measurements separated in time allow normal or neoplastic cells to signal through conservation which human genes are more essential to their survival in vitro or in vivo.

Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide.

Exhaled breath biomarkers are an important emerging field. The fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation with clinical and epidemiological applications (e.g., air pollution health effects studies). Systems of differential equations describe FeNO-measured non-invasively at the mouth-as a function of exhalation flow rate and parameters representing airway and alveolar sources of NO in the airway. Traditionally, NO parameters have been estimated separately for each study participant (Stage I) and then related to covariates (Stage II). Statistical properties of these two-step approaches have not been investigated. In simulation studies, we evaluated finite sample properties of existing two-step methods as well as a novel Unified Hierarchical Bayesian (U-HB) model. The U-HB is a one-step estimation method developed with the goal of properly propagating uncertainty as well as increasing power and reducing type I error for estimating associations of covariates with NO parameters. We demonstrated the U-HB method in an analysis of data from the southern California Children's Health Study relating traffic-related air pollution exposure to airway and alveolar airway inflammation.

Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.

Genomic diversity and genome-wide association analysis related to yield and fatty acid composition of wild American oil palm.

Existing Elaeis guineensis cultivars lack sufficient genetic diversity due to extensive breeding. Harnessing variation in wild crop relatives is necessary to expand the breadth of agronomically valuable traits. Using RAD sequencing, we examine the natural diversity of wild American oil palm populations (Elaeis oleifera), a sister species of the cultivated Elaeis guineensis oil palm. We genotyped 192 wild E. oleifera palms collected from seven Latin American countries along with four cultivated E. guineensis palms. Honduras, Costa Rica, Panama and Colombia palms are panmictic and genetically similar. Genomic patterns of diversity suggest that these populations likely originated from the Amazon Basin. Despite evidence of a genetic bottleneck and high inbreeding observed in these populations, there is considerable genetic and phenotypic variation for agronomically valuable traits. Genome-wide association revealed several candidate genes associated with fatty acid composition along with vegetative and yield-related traits. These observations provide valuable insight into the geographic distribution of diversity, phenotypic variation and its genetic architecture that will guide choices of wild genotypes for crop improvement.

iMutSig: a web application to identify the most similar mutational signature using shiny.

There are two frameworks for characterizing mutational signatures which are commonly used to describe the nucleotide patterns that arise from mutational processes. Estimated mutational signatures from fitting these two methods in human cancer can be found online, in the Catalogue Of Somatic Mutations In Cancer (COSMIC) website or a GitHub repository. The two frameworks make differing assumptions regarding independence of base pairs and for that reason may produce different results. Consequently, there is a need to compare and contrast the results of the two methods, but no such tool currently exists. In this paper, we provide a simple and intuitive interface that allows such comparisons to be easily performed. When using our software, the user may download published mutational signatures of either type. Mutational signatures from the pmsignature data source are expanded to probabilistic vectors of 96-possible mutation types, the same model specification used by COSMIC, and then compared to COSMIC signatures. Cosine similarity measures the extent of signature similarity. iMutSig provides a simple and user-friendly web application allowing researchers to compare signatures from COSMIC to those from pmsignature, and vice versa. Furthermore, iMutSig allows users to input a self-defined mutational signature and examine its similarity to published signatures from both data sources. iMutSig is accessible online and source code is available for download on GitHub.

Epigenetic Conservation Is a Beacon of Function: An Analysis Using Methcon5 Software for Studying Gene Methylation.

PURPOSE: Different epigenetic configurations allow one genome to develop into multiple cell types. Although the rules governing what epigenetic features confer gene expression are increasingly being understood, much remains uncertain. Here, we used a novel software package, Methcon5, to explore whether the principle of biologic conservation can be used to identify expressed genes. The hypothesis is that epigenetic configurations of important expressed genes will be conserved within a tissue. MATERIALS AND METHODS: We compared the DNA methylation of approximately 850,000 CpG sites between multiple clonal crypts or glands of human colon, small intestine, and endometrium. We performed this analysis using the new software package, Methcon5, which enables detection of regions of high (or low) conservation. RESULTS: We showed that DNA methylation is preferentially conserved at gene-associated CpG sites, particularly in gene promoters (eg, near the transcription start site) or the first exon. Furthermore, higher conservation correlated well with gene expression levels and performed better than promoter DNA methylation levels. Most conserved genes are in canonical housekeeping pathways. CONCLUSION: This study introduces the new software package, Methcon5. In this example application, we showed that epigenetic conservation provides an alternative method for identifying functional genomic regions in human tissues.

Impact of different fixed flow sampling protocols on flow-independent exhaled nitric oxide parameter estimates using the Bayesian dynamic two-compartment model.

Exhaled nitric oxide (FeNO) is an established respiratory biomarker with clinical applications in the diagnosis and management of asthma. Because FeNO depends strongly on the flow (exhalation) rate, early protocols specified that measurements should be taken when subjects exhaled at a fixed rate of 50 ml/s. Subsequently, multiple flow (or "extended") protocols were introduced which measure FeNO across a range of fixed flow rates, allowing estimation of parameters including Caw NO and CA NO which partition the physiological sources of NO into proximal airway wall tissue and distal alveolar regions (respectively). A recently developed dynamic model of FeNO uses flow-concentration data from the entire exhalation maneuver rather than plateau means, permitting estimation of Caw NO and CA NO from a wide variety of protocols. In this paper, we use a simulation study to compare Caw NO and CA NO estimation from a variety of fixed flow protocols, including: single maneuvers (30, 50,100, or 300 ml/s) and three established multiple maneuver protocols. We quantify the improved precision with multiple maneuvers and the importance of low flow maneuvers in estimating Caw NO. We conclude by applying the dynamic model to FeNO data from 100 participants of the Southern California Children's Health Study, establishing the feasibility of using the dynamic method to reanalyze archived online FeNO data and extract new information on Caw NO and CA NO in situations where these estimates would have been impossible to obtain using traditional steady-state two compartment model estimation methods.

Mutational signatures in colon cancer.

OBJECTIVE: Recently, many tumor sequencing studies have inferred and reported on mutational signatures, short nucleotide patterns at which particular somatic base substitutions appear more often. A number of signatures reflect biological processes in the patient and factors associated with cancer risk. Our goal is to infer mutational signatures appearing in colon cancer, a cancer for which environmental risk factors vary by cancer subtype, and compare the signatures to those in adult stem cells from normal colon. We also compare the mutational signatures to others in the literature. RESULTS: We apply a probabilistic mutation signature model to somatic mutations previously reported for six adult normal colon stem cells and 431 colon adenocarcinomas. We infer six mutational signatures in colon cancer, four being specific to tumors with hypermutation. Just two signatures explained the majority of mutations in the small number of normal aging colon samples. All six signatures are independently identified in a series of 295 Chinese colorectal cancers.

HiLDA: a statistical approach to investigate differences in mutational signatures.

We propose a hierarchical latent Dirichlet allocation model (HiLDA) for characterizing somatic mutation data in cancer. The method allows us to infer mutational patterns and their relative frequencies in a set of tumor mutational catalogs and to compare the estimated frequencies between tumor sets. We apply our method to two datasets, one containing somatic mutations in colon cancer by the time of occurrence, before or after tumor initiation, and the second containing somatic mutations in esophageal cancer by sex, age, smoking status, and tumor site. In colon cancer, the relative frequencies of mutational patterns were found significantly associated with the time of occurrence of mutations. In esophageal cancer, the relative frequencies were significantly associated with the tumor site. Our novel method provides higher statistical power for detecting differences in mutational signatures.

Bayesian model selection for the Drosophila gap gene network.

BACKGROUND: The gap gene system controls the early cascade of the segmentation pathway in Drosophila melanogaster as well as other insects. Owing to its tractability and key role in embryo patterning, this system has been the focus for both computational modelers and experimentalists. The gap gene expression dynamics can be considered strictly as a one-dimensional process and modeled as a system of reaction-diffusion equations. While substantial progress has been made in modeling this phenomenon, there still remains a deficit of approaches to evaluate competing hypotheses. Most of the model development has happened in isolation and there has been little attempt to compare candidate models. RESULTS: The Bayesian framework offers a means of doing formal model evaluation. Here, we demonstrate how this framework can be used to compare different models of gene expression. We focus on the Papatsenko-Levine formalism, which exploits a fractional occupancy based approach to incorporate activation of the gap genes by the maternal genes and cross-regulation by the gap genes themselves. The Bayesian approach provides insight about relationship between system parameters. In the regulatory pathway of segmentation, the parameters for number of binding sites and binding affinity have a negative correlation. The model selection analysis supports a stronger binding affinity for Bicoid compared to other regulatory edges, as shown by a larger posterior mean. The procedure doesn't show support for activation of Kruppel by Bicoid. CONCLUSIONS: We provide an efficient solver for the general representation of the Papatsenko-Levine model. We also demonstrate the utility of Bayes factor for evaluating candidate models for spatial pattering models. In addition, by using the parallel tempering sampler, the convergence of Markov chains can be remarkably improved and robust estimates of Bayes factors obtained.

WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants.

The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks limiting the current methods. We demonstrated its performance by predicting quantitative disease resistance and quantitative functional traits in the wild model plant species, Medicago truncatula, using geographical locations as covariates for admixture analysis. The method's prediction reliability equals or outperforms all existing algorithms for quantitative phenotype prediction. WhoGEM analysis produces evidence that variation in genome admixture proportions explains most of the phenotypic variation for quantitative phenotypes.

Threshold response to stochasticity in morphogenesis.

During development of biological organisms, multiple complex structures are formed. In many instances, these structures need to exhibit a high degree of order to be functional, although many of their constituents are intrinsically stochastic. Hence, it has been suggested that biological robustness ultimately must rely on complex gene regulatory networks and clean-up mechanisms. Here we explore developmental processes that have evolved inherent robustness against stochasticity. In the context of the Drosophila eye disc, multiple optical units, ommatidia, develop into crystal-like patterns. During the larva-to-pupa stage of metamorphosis, the centers of the ommatidia are specified initially through the diffusion of morphogens, followed by the specification of R8 cells. Establishing the R8 cell is crucial in setting up the geometric, and functional, relationships of cells within an ommatidium and among neighboring ommatidia. Here we study an PDE mathematical model of these spatio-temporal processes in the presence of parametric stochasticity, defining and applying measures that quantify order within the resulting spatial patterns. We observe a universal sigmoidal response to increasing transcriptional noise. Ordered patterns persist up to a threshold noise level in the model parameters. In accordance with prior qualitative observations, as the noise is further increased past a threshold point of no return, these ordered patterns rapidly become disordered. Such robustness in development allows for the accumulation of genetic variation without any observable changes in phenotype. We argue that the observed sigmoidal dependence introduces robustness allowing for sizable amounts of genetic variation and transcriptional noise to be tolerated in natural populations without resulting in phenotype variation.

Conservation of social effects (Ψ) between two species of Drosophila despite reversal of sexual dimorphism.

Indirect genetic effects (IGEs) describe the effect of the genes of social partners on the phenotype of a focal individual. Here, we measure indirect genetic effects using the "coefficient of interaction" (Ψ) to test whether Ψ evolved between Drosophila melanogaster and D. simulans. We compare Ψ for locomotion between ethanol and nonethanol environments in both species, but only D. melanogaster utilizes ethanol ecologically. We find that while sexual dimorphism for locomotion has been reversed in D. simulans, there has been no evolution of social effects between these two species. What did evolve was the interaction between genotype-specific Ψ and the environment, as D. melanogaster varies unpredictably between environments and D. simulans does not. In this system, this suggests evolutionary lability of sexual dimorphism but a conservation of social effects, which brings forth interesting questions about the role of the social environment in sexual selection.

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network.

Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects.

Basic reversal-learning capacity in flies suggests rudiments of complex cognition.

The most basic models of learning are reinforcement learning models (for instance, classical and operant conditioning) that posit a constant learning rate; however many animals change their learning rates with experience. This process is sometimes studied by reversing an existing association between cues and rewards, and measuring the rate of relearning. Augmented reversal-learning, where learning rates increase with practice, can be an important component of behavioral flexibility; and may provide insight into higher cognition. Previous studies of reversal-learning in Drosophila have not measured learning rates, but have tended to focus on measuring gross deficits in reversal-learning, as the ratio of two timepoints. These studies have uncovered a diversity of mechanisms underlying reversal-learning, but natural genetic variation in this trait has yet to be assessed. We conducted a reversal-learning regime on a diverse panel of Drosophila melanogaster genotypes. We found highly significant genetic variation in their baseline ability to learn. We also found that they have a consistent, and strong (1.3×), increase in their learning speed with reversal. We found no evidence, however, that there was genetic variation in their ability to increase their learning rates with experience. This may suggest that Drosophila have a hitherto unrecognized ability to integrate acquired information, and improve their decision making; but that their mechanisms for doing so are under strong constraints.

Sure enough: efficient Bayesian learning and choice.

Probabilistic decision-making is a general phenomenon in animal behavior, and has often been interpreted to reflect the relative certainty of animals' beliefs. Extensive neurological and behavioral results increasingly suggest that animal beliefs may be represented as probability distributions, with explicit accounting of uncertainty. Accordingly, we develop a model that describes decision-making in a manner consistent with this understanding of neuronal function in learning and conditioning. This first-order Markov, recursive Bayesian algorithm is as parsimonious as its minimalist point-estimate, Rescorla-Wagner analogue. We show that the Bayesian algorithm can reproduce naturalistic patterns of probabilistic foraging, in simulations of an experiment in bumblebees. We go on to show that the Bayesian algorithm can efficiently describe the behavior of several heuristic models of decision-making, and is consistent with the ubiquitous variation in choice that we observe within and between individuals in implementing heuristic decision-making. By describing learning and decision-making in a single Bayesian framework, we believe we can realistically unify descriptions of behavior across contexts and organisms. A unified cognitive model of this kind may facilitate descriptions of behavioral evolution.

Social effects for locomotion vary between environments in Drosophila melanogaster females.

Despite strong purifying or directional selection, variation is ubiquitous in populations. One mechanism for the maintenance of variation is indirect genetic effects (IGEs), as the fitness of a given genotype will depend somewhat on the genes of its social partners. IGEs describe the effect of genes in social partners on the expression of the phenotype of a focal individual. Here, we ask what effect IGEs, and variation in IGEs between abiotic environments, has on locomotion in Drosophila. This trait is known to be subject to intralocus sexually antagonistic selection. We estimate the coefficient of interaction, Ψ, using six inbred lines of Drosophila. We found that Ψ varied between abiotic environments, and that it may vary across among male genotypes in an abiotic environment specific manner. We also found evidence that social effects of males alter the value of a sexually dimorphic trait in females, highlighting an interesting avenue for future research into sexual antagonism. We conclude that IGEs are an important component of social and sexual interactions and that they vary between individuals and abiotic environments in complex ways, with the potential to promote the maintenance of phenotypic variation.