Hybrid duplex: a novel method to study the contractile function of heterogeneous myocardium

In an earlier study, we experimentally mimicked the effects of mechanical interaction between different regions of the ventricular wall by allowing pairs of independently maintained cardiac muscle fibers to interact mechanically in series or in parallel. This simple physiological model of heterogeneous myocardium, which has been termed "duplex," has provided new insight into basic effects of cardiac electromechanical heterogeneity. Here, we present a novel "hybrid duplex," where one of the elements is an isolated cardiac muscle and the other a "virtual cardiac muscle." The virtual muscle is represented by a computational model of cardiomyocyte electromechanical activity. We present in detail the computer-based digital control system that governs the mechanical interaction between virtual and biological muscle, the software used for data analysis, and working implementations of the model. Advantages of the hybrid duplex method are discussed, and experimental recordings are presented for illustration and as proof of the principle.     

Adaptive tests for detecting gene-gene and gene-environment interactions

There has been an increasing interest in detecting gene-gene and gene-environment interactions in genetic association studies. A major statistical challenge is how to deal with a large number of parameters measuring possible interaction effects, which leads to reduced power of any statistical test due to a large number of degrees of freedom or high cost of adjustment for multiple testing. Hence, a popular idea is to first apply some dimension reduction techniques before testing, while another is to apply only statistical tests that are developed for and robust to high-dimensional data. To combine both ideas, we propose applying an adaptive sum of squared score (SSU) test and several other adaptive tests. These adaptive tests are extensions of the adaptive Neyman test [Fan, 1996], which was originally proposed for high-dimensional data, providing a simple and effective way for dimension reduction. On the other hand, the original SSU test coincides with a version of a test specifically developed for high-dimensional data. We apply these adaptive tests and their original nonadaptive versions to simulated data to detect interactions between two groups of SNPs (e.g. multiple SNPs in two candidate regions). We found that for sparse models (i.e. with only few non-zero interaction parameters), the adaptive SSU test and its close variant, an adaptive version of the weighted sum of squared score (SSUw) test, improved the power over their non-adaptive versions, and performed consistently well across various scenarios. The proposed adaptive tests are built in the general framework of regression analysis, and can thus be applied to various types of traits in the presence of covariates.     

Mixture modeling for genome-wide localization of transcription factors

Chromatin immunoprecipitation followed by DNA microarray analysis (ChIP-chip methodology) is an efficient way of mapping genome-wide protein-DNA interactions. Data from tiling arrays encompass DNA-protein interaction measurements on thousands or millions of short oligonucleotides (probes) tiling a whole chromosome or genome. We propose a new model-based method for analyzing ChIP-chip data. The proposed model is motivated by the widely used two-component multinomial mixture model of de novo motif finding. It utilizes a hierarchical gamma mixture model of binding intensities while incorporating inherent spatial structure of the data. In this model, genomic regions belong to either one of the following two general groups: regions with a local protein-DNA interaction (peak) and regions lacking this interaction. Individual probes within a genomic region are allowed to have different localization rates accommodating different binding affinities. A novel feature of this model is the incorporation of a distribution for the peak size derived from the experimental design and parameters. This leads to the relaxation of the fixed peak size assumption that is commonly employed when computing a test statistic for these types of spatial data. Simulation studies and a real data application demonstrate good operating characteristics of the method including high sensitivity with small sample sizes when compared to available alternative methods.     

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km² study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (r_s = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery.     

Lipid-cholesterol interactions in the P beta' phase. Application of a statistical mechanical model.

We describe a statistical mechanical model for lipid-cholesterol mixtures in the P beta' (ripple) phase of lipid bilayers. The model is a simple extension of an earlier model for the ripple phase in pure lipid bilayers. The extension consists of adding a degree of freedom to allow for the occupation of underlying lattice sites by cholesterol molecules, and adding a lipid-cholesterol interaction term to the model Hamiltonian. The interaction term was constructed based on numerical calculations of lipid-cholesterol energies for several different packing juxtapositions of the two molecules. Other than the lipid-cholesterol interactions, the extended model uses the same parameter set as the earlier model, so that comparison of the properties of the extended model with experimental data serves as a test of the validity of the original model. Properties of the model were calculated using the Monte Carlo method. Results are displayed as snapshots of the ripple configurations at different cholesterol concentrations. The spacing of the ripples increases with increasing cholesterol concentration and the rate of increase compares very well with experimental data. The success of this model supports the conclusion drawn earlier that frustration arising from anisotropic packing interactions is responsible for the ripple phase in lipid bilayers. In the extended model these packing interactions are responsible for the selective partitioning of cholesterol in the regions between the ripples.     

Hypothesis testing in semiparametric additive mixed models

We consider testing whether the nonparametric function in a semiparametric additive mixed model is a simple fixed degree polynomial, for example, a simple linear function. This test provides a goodness-of-fit test for checking parametric models against nonparametric models. It is based on the mixed-model representation of the smoothing spline estimator of the nonparametric function and the variance component score test by treating the inverse of the smoothing parameter as an extra variance component. We also consider testing the equivalence of two nonparametric functions in semiparametric additive mixed models for two groups, such as treatment and placebo groups. The proposed tests are applied to data from an epidemiological study and a clinical trial and their performance is evaluated through simulations.     

Adaptive and optimized COVID-19 vaccination strategies across geographical regions and age groups

We evaluate the efficiency of various heuristic strategies for allocating vaccines against COVID-19 and compare them to strategies found using optimal control theory. Our approach is based on a mathematical model which tracks the spread of disease among different age groups and across different geographical regions, and we introduce a method to combine age-specific contact data to geographical movement data. As a case study, we model the epidemic in the population of mainland Finland utilizing mobility data from a major telecom operator. Our approach allows to determine which geographical regions and age groups should be targeted first in order to minimize the number of deaths. In the scenarios that we test, we find that distributing vaccines demographically and in an age-descending order is not optimal for minimizing deaths and the burden of disease. Instead, more lives could be saved by using strategies which emphasize high-incidence regions and distribute vaccines in parallel to multiple age groups. The level of emphasis that high-incidence regions should be given depends on the overall transmission rate in the population. This observation highlights the importance of updating the vaccination strategy when the effective reproduction number changes due to the general contact patterns changing and new virus variants entering.     

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.     

Quantitative trait loci for spawning date and body weight in rainbow trout: testing for conserved effects across ancestrally duplicated chromosomes

We incorporated 69 microsatellite loci into an existing data set of 132 markers to test for quantitative trait loci (QTLs) affecting spawning date and body weight in a backcross between two outbred strains of rainbow trout (Oncorhynchus mykiss). Twenty-six linkage groups were identified and synteny of duplicated microsatellite markers was used to confirm 13 homeologous chromosome pairs. Gene-centromere data were used to localize the centromeres for 13 linkage groups whose orientations were previously unknown. We applied a combination of interval mapping and single marker analysis to the segregating maternal and paternal alleles at 201 microsatellite loci. Four spawning date QTLs with suggestive evidence for an additional two QTLs were detected in female trout spawning at 3 and 4 years of age. Similarly we detected three QTLs for body weight in females at 2 years of age plus four suggestive QTLs for this trait. We found marginal evidence that three pairs of ancestral homeologues contained detectable QTLs for the same trait. In one of the three pairs of homeologues, the duplicated QTL regions mapped to the same relative chromosomal location, while the exact localization of the QTL position in one of the other pairs was difficult to infer since it was based on data from a male-derived map. The existing data were unable to refute a hypothesis that duplicated functional genes will be maintained within the telomeric regions of salmonids due to preferential male-mediated crossing over in this region. Two of the four spawning date QTLs were detected on linkage groups with unknown homeologous relationships. QTLs with possible pleiotropic effects on both spawning date and body size were localized to two linkage groups.     

Is Model Fitting Necessary for Model-Based fMRI?

Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice. We first developed general closed-form expressions for the relationship between results of fMRI analyses using different regressors, e.g., one corresponding to the true process underlying the measured data and one a model-derived approximation of the true generative regressor. Then, as a specific test case, we examined the sensitivity of model-based fMRI to the learning rate parameter in reinforcement learning, both in theory and in two previously-published datasets. We found that even gross errors in the learning rate lead to only minute changes in the neural results. Our findings thus suggest that precise model fitting is not always necessary for model-based fMRI. They also highlight the difficulty in using fMRI data for arbitrating between different models or model parameters. While these specific results pertain only to the effect of learning rate in simple reinforcement learning models, we provide a template for testing for effects of different parameters in other models.     

Permutation Testing for Treatment–Covariate Interactions and Subgroup Identification

We consider the problem of using permutation-based methods to test for treatment–covariate interactions from randomized clinical trial data. Testing for interactions is common in the field of personalized medicine, as subgroups with enhanced treatment effects arise when treatment-by-covariate interactions exist. Asymptotic tests can often be performed for simple models, but in many cases, more complex methods are used to identify subgroups, and non-standard test statistics proposed, and asymptotic results may be difficult to obtain. In such cases, it is natural to consider permutation-based tests, which shuffle selected parts of the data in order to remove one or more associations of interest; however, in the case of interactions, it is generally not possible to remove only the associations of interest by simple permutations of the data. We propose a number of alternative permutation-based methods, designed to remove only the associations of interest, but preserving other associations. These methods estimate the interaction term in a model, then create data that “looks like” the original data except that the interaction term has been permuted. The proposed methods are shown to outperform traditional permutation methods in a simulation study. In addition, the proposed methods are illustrated using data from a randomized clinical trial of patients with hypertension.     

DNA replication timing and long-range DNA interactions predict mutational landscapes of cancer genomes

Somatic copy-number alterations (SCNA) are a hallmark of many cancer types, but the mechanistic basis underlying their genome-wide patterns remains incompletely understood. Here we integrate data on DNA replication timing, long-range interactions between genomic material, and 331,724 SCNAs from 2,792 cancer samples classified into 26 cancer types. We report that genomic regions of similar replication timing are clustered spatially in the nucleus, that the two boundaries of SCNAs tend to be found in such regions, and that regions replicated early and late display distinct patterns of frequencies of SCNA boundaries, SCNA size and a preference for deletions over insertions. We show that long-range interaction and replication timing data alone can identify a significant proportion of SCNAs in an independent test data set. We propose a model for the generation of SCNAs in cancer, suggesting that data on spatial proximity of regions replicating at the same time can be used to predict the mutational landscapes of cancer genomes.     

Proceedings of the SMBE Tri-National Young Investigators' Workshop 2005. Genome-wide associations between hybrid sterility QTL and marker transmission ratio distortion

Marker transmission ratio distortion (TRD) in genetic mapping populations is frequently ascribed to selection against allelic combinations that cause hybrid incompatibility. Accordingly, genomic regions of TRD should be nonrandomly associated (colocated) with loci that underlie hybrid incompatibility. To directly test this hypothesis, we evaluated the genome-wide qualitative and quantitative agreement between chromosomal regions exhibiting marker TRD and those known to contain hybrid incompatibility quantitative trait locus (QTL). Incompatibility data came from a near-isogenic line (NIL) analysis of pollen and seed sterility in a cross between two Solanum (formerly Lycopersicon) species. We assessed (1) whether these incompatibility loci are colocated with markers that show significant TRD in two earlier generations preceding these introgression lines and (2) whether the magnitude of marker distortion quantitatively matches the estimated strength of selection against each incompatibility locus. We found evidence that TRD regions are chromosomally colocated with hybrid incompatibility loci more frequently than is expected by chance: pollen sterility QTLs were most closely associated with distorted heterozygote frequencies in later-generation backcrosses. Nonetheless, there was no evidence for an association between TRD and seed sterility and little evidence of a quantitative association between the magnitude of marker TRD and the fitness effects of heterospecific alleles at each chromosomal location. We propose and test a model (the "dance partner" model) to explain several cases where regions of TRD are not associated with hybrid incompatibility loci. Under this model, some NILs containing greater than one heterospecific introgression may not express hybrid incompatibility phenotypes because they carry both appropriate genetic dance partners required for a fully functional interaction. Accordingly, negative interactions expressed in earlier backcross generations are masked in these double-introgression NILs. Based on this model, we identify the location of several new putative pairwise interactors underlying hybrid incompatibility in this species cross.     

Penalized estimation of a class of single-index varying-coefficient models for integrative genomic analysis

Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study.     

A simple model of linkage disequilibrium and genetic drift in human genomic SNPs: importance of demography and SNP age

We propose a simple model of evolution at a pair of SNP loci, under mutation, genetic drift and recombination. The developed model allows to consider evolution of SNPs under different demographic scenarios. We applied it to SNP data containing polymorphisms spanning 19 gene regions. We initially matched the linkage disequilibrium (LD) data only, and then we reconciled both LD and heterozygosity data. The imbalance between LD and heterozygosity data, observed for some of the analyzed genomic regions, may be a signature of selection acting in these regions. However, assuming neutrality, we obtain estimates of the age of population expansion of modern humans, which are consistent with the consensus estimates. In addition, we are able to estimate the ages of the polymorphisms observed in different genomic regions and we find that they vary widely with respect to their age. Polymorphisms at loci implicated in human disease, seem to be younger than average. Our results supplement the conclusions originally obtained by Reich and co-workers for the same set of data.     

Controlling the false-positive rate in multilocus genome scans for selection

Rapid typing of genetic variation at many regions of the genome is an efficient way to survey variability in natural populations in an effort to identify segments of the genome that have experienced recent natural selection. Following such a genome scan, individual regions may be chosen for further sequencing and a more detailed analysis of patterns of variability, often to perform a parametric test for selection and to estimate the strength of a recent selective sweep. We show here that not accounting for the ascertainment of loci in such analyses leads to false inference of natural selection when the true model is selective neutrality, because the procedure of choosing unusual loci (in comparison to the rest of the genome-scan data) selects regions of the genome with genealogies similar to those expected under models of recent directional selection. We describe a simple and efficient correction for this ascertainment bias, which restores the false-positive rate to near-nominal levels. For the parameters considered here, we find that obtaining a test with the expected distribution of P-values depends on accurately accounting both for ascertainment of regions and for demography. Finally, we use simulations to explore the utility of relying on outlier loci to detect recent selective sweeps. We find that measures of diversity and of population differentiation are more effective than summaries of the site-frequency spectrum and that sequencing larger regions (2.5 kbp) in genome-scan studies leads to more power to detect recent selective sweeps.     

Genetic and environmental (inter)actions in male mouse lines selected for aggressive and nonaggressive behavior

The purpose of this study was to investigate the effects of genetic and environmental factors, as well as their interaction, in the etiology of aggressive behavior in two mouse lines bidirectionally selected for offensive aggression. To this end, we raised the Finnish TA (aggressive) and TNA (nonagressive) selection lines either in isolation or in cohabitation with a female after weaning. At the age of 3 months we determined their aggressive behavior in three paradigms (intruder resident, neutral cage, resident intruder) against a male standard opponent. We also determined the animals' aggressive behavior against a female mouse. The results show genetic and environmental effects, as well as gene–environment interaction. We see prominent genotype effects under all conditions but each test is sensitive to a specific combination of environmental effects. A particularly noteworthy result is that variation in the unusual behavior of aggression towards a female is largely explained by the interaction of genotype with isolation. We also examined whether test experience influenced the outcome of an encounter between an experimental animal and an opponent, and found that this factor should not be underestimated, its effect size and direction depending on the type of paradigm and way of housing. These data suggest that the identification of genes underlying aggressive behavior in mice is by no means straightforward and that the result of this search will depend on the environmental design of the study (type of paradigm, housing conditions). These data also suggest that the use of 'test battery' mice might produce different results than the use of test-naïve animals.     

Optimization and evaluation of a coarse-grained model of protein motion using x-ray crystal data

Simple coarse-grained models, such as the Gaussian network model, have been shown to capture some of the features of equilibrium protein dynamics. We extend this model by using atomic contacts to define residue interactions and introducing more than one interaction parameter between residues. We use B-factors from 98 ultra-high resolution (相似文献