190 research outputs found
DYNAMICS OF ACTION POTENTIAL DURATION: EFFECTS ON RESTITUTION AND REPOLARIZATION ALTERNANS
The presented studies investigate dynamics of action potential duration (APD) tobetter understand the underlying mechanism for repolarization alternans.We recorded trans-membrane potentials (TMP) in canine endocardial muscle tissueusing standard glass microelectrode under the control of an explicit diastolic interval (DI)control pacing protocol, i.e. feedback protocol. During sequential sinusoidal DI activation,the trajectory of APD dynamics has multiple values of APD correspondent to the sameDI, i.e. restitution is a bi-modal relationship. Our results indicate that: 1) there is a delay,similar to hysteresis, of change in APD responding to change in DI, 2) and the timecourse of the delay is asymmetric for fast or slow pacing history. The alternans wasobserved during constant DI pacing, i.e. the DI preceding each APD was invariant orchanged within a limited range. This finding suggests that alternans of APD do not needthe oscillation of preceding DI, i.e. DI dependent restitution is not a necessary conditionfor the alternans. This result implies that DI independent component exists in themechanism of the alternans. Nonetheless, the amplitude of alternans was statisticallylarger during constant pacing cycle length (PCL) pacing than that during constant DIpacing, even though both PCL and DI pacing trials used similar average activation rate.These results also demonstrate the ability of the feedback protocol to analyze the memoryeffects and dissect different components in the mechanism of alternans.Two computational models, Luo-Rudy dynamics (LRD) and cardiac ventricle model(CVM) were used to study the hysteresis in restitution. By perturbing membrane current:L-type calcium current, rapid and slow potassium rectifier, and intracellular calciumtransfer rate in sarcoplasmic reticulum (SR) and using sinusoidal DI pacing sequence, weshowed that the asymmetric calcium current across the membrane and its interaction withcalcium buffer in SR during increasing and decreasing DI phase plays an important rolein the hysteresis. CVM was used to study the alternans during constant DI pacing.However CVM failed to replicate the alternans that occurred in the experiments. Thisresult implies that CVM lacks the electrophysiological kinetics related to alternans thatwas shown in our experiment
A fast algorithm for detecting gene-gene interactions in genome-wide association studies
With the recent advent of high-throughput genotyping techniques, genetic data
for genome-wide association studies (GWAS) have become increasingly available,
which entails the development of efficient and effective statistical
approaches. Although many such approaches have been developed and used to
identify single-nucleotide polymorphisms (SNPs) that are associated with
complex traits or diseases, few are able to detect gene-gene interactions among
different SNPs. Genetic interactions, also known as epistasis, have been
recognized to play a pivotal role in contributing to the genetic variation of
phenotypic traits. However, because of an extremely large number of SNP-SNP
combinations in GWAS, the model dimensionality can quickly become so
overwhelming that no prevailing variable selection methods are capable of
handling this problem. In this paper, we present a statistical framework for
characterizing main genetic effects and epistatic interactions in a GWAS study.
Specifically, we first propose a two-stage sure independence screening (TS-SIS)
procedure and generate a pool of candidate SNPs and interactions, which serve
as predictors to explain and predict the phenotypes of a complex trait. We also
propose a rates adjusted thresholding estimation (RATE) approach to determine
the size of the reduced model selected by an independence screening.
Regularization regression methods, such as LASSO or SCAD, are then applied to
further identify important genetic effects. Simulation studies show that the
TS-SIS procedure is computationally efficient and has an outstanding finite
sample performance in selecting potential SNPs as well as gene-gene
interactions. We apply the proposed framework to analyze an
ultrahigh-dimensional GWAS data set from the Framingham Heart Study, and select
23 active SNPs and 24 active epistatic interactions for the body mass index
variation. It shows the capability of our procedure to resolve the complexity
of genetic control.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS771 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies
Although genome-wide association studies (GWAS) have proven powerful for
comprehending the genetic architecture of complex traits, they are challenged
by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors,
the presence of complex environmental factors, and longitudinal or functional
natures of many complex traits or diseases. To address these challenges, we
propose a high-dimensional varying-coefficient model for incorporating
functional aspects of phenotypic traits into GWAS to formulate a so-called
functional GWAS or fGWAS. The Bayesian group lasso and the associated MCMC
algorithms are developed to identify significant SNPs and estimate how they
affect longitudinal traits through time-varying genetic actions. The model is
generalized to analyze the genetic control of complex traits using
subject-specific sparse longitudinal data. The statistical properties of the
new model are investigated through simulation studies. We use the new model to
analyze a real GWAS data set from the Framingham Heart Study, leading to the
identification of several significant SNPs associated with age-specific changes
of body mass index. The fGWAS model, equipped with the Bayesian group lasso,
will provide a useful tool for genetic and developmental analysis of complex
traits or diseases.Comment: Published at http://dx.doi.org/10.1214/15-AOAS808 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Propensity score regression for causal inference with treatment heterogeneity
Understanding how treatment effects vary on individual characteristics is
critical in the contexts of personalized medicine, personalized advertising and
policy design. When the characteristics are of practical interest are only a
subset of full covariate, non-parametric estimation is often desirable; but few
methods are available due to the computational difficult. Existing
non-parametric methods such as the inverse probability weighting methods have
limitations that hinder their use in many practical settings where the values
of propensity scores are close to 0 or 1. We propose the propensity score
regression (PSR) that allows the non-parametric estimation of the heterogeneous
treatment effects in a wide context. PSR includes two non-parametric
regressions in turn, where it first regresses on the propensity scores together
with the characteristics of interest, to obtain an intermediate estimate; and
then, regress the intermediate estimates on the characteristics of interest
only. By including propensity scores as regressors in the non-parametric
manner, PSR is capable of substantially easing the computational difficulty
while remain (locally) insensitive to any value of propensity scores. We
present several appealing properties of PSR, including the consistency and
asymptotical normality, and in particular the existence of an explicit variance
estimator, from which the analytical behaviour of PSR and its precision can be
assessed. Simulation studies indicate that PSR outperform existing methods in
varying settings with extreme values of propensity scores. We apply our method
to the national 2009 flu survey (NHFS) data to investigate the effects of
seasonal influenza vaccination and having paid sick leave across different age
groups
Towards Visually Explaining Variational Autoencoders
Recent advances in Convolutional Neural Network (CNN) model interpretability
have led to impressive progress in visualizing and understanding model
predictions. In particular, gradient-based visual attention methods have driven
much recent effort in using visual attention maps as a means for visual
explanations. A key problem, however, is these methods are designed for
classification and categorization tasks, and their extension to explaining
generative models, e.g. variational autoencoders (VAE) is not trivial. In this
work, we take a step towards bridging this crucial gap, proposing the first
technique to visually explain VAEs by means of gradient-based attention. We
present methods to generate visual attention from the learned latent space, and
also demonstrate such attention explanations serve more than just explaining
VAE predictions. We show how these attention maps can be used to localize
anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD
dataset. We also show how they can be infused into model training, helping
bootstrap the VAE into learning improved latent space disentanglement,
demonstrated on the Dsprites dataset
Model and Algorithm for Linkage Disequilibrium Analysis in a Non-Equilibrium Population
The multilocus analysis of polymorphisms has emerged as a vital ingredient of population genetics and evolutionary biology. A fundamental assumption used for existing multilocus analysis approaches is Hardy–Weinberg equilibrium at which maternally- and paternally-derived gametes unite randomly during fertilization. Given the fact that natural populations are rarely panmictic, these approaches will have a significant limitation for practical use. We present a robust model for multilocus linkage disequilibrium analysis which does not rely on the assumption of random mating. This new disequilibrium model capitalizes on Weir’s definition of zygotic disequilibria and is based on an open-pollinated design in which multiple maternal individuals and their half-sib families are sampled from a natural population. This design captures two levels of associations: one is at the upper level that describes the pattern of cosegregation between different loci in the parental population and the other is at the lower level that specifies the extent of co-transmission of non-alleles at different loci from parents to their offspring. An MCMC method was implemented to estimate genetic parameters that define these associations. Simulation studies were used to validate the statistical behavior of the new model
A Bayesian Framework for Functional Mapping through Joint Modeling of Longitudinal and Time-to-Event Data
The most powerful and comprehensive approach of study in modern biology is to understand the whole process of development and all events of importance to development which occur in the process. As a consequence, joint modeling of developmental processes and events has become one of the most demanding tasks in statistical research. Here, we propose a joint modeling framework for functional mapping of specific quantitative trait loci (QTLs) which controls developmental processes and the timing of development and their causal correlation over time. The joint model contains two submodels, one for a developmental process, known as a longitudinal trait, and the other for a developmental event, known as the time to event, which are connected through a QTL mapping framework. A nonparametric approach is used to model the mean and covariance function of the longitudinal trait while the traditional Cox proportional hazard (PH) model is used to model the event time. The joint model is applied to map QTLs that control whole-plant vegetative biomass growth and time to first flower in soybeans. Results show that this model should be broadly useful for detecting genes controlling physiological and pathological processes and other events of interest in biomedicine
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