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

Scale invariant distribution functions in quantum systems with few degrees of freedom

Scale invariance usually occurs in extended systems where correlation functions decay algebraically in space and/or time. Here we introduce a new type of scale invariance, occurring in the distribution functions of physical observables. At equilibrium these functions decay over a typical scale set by the temperature, but they can become scale invariant in a sudden quantum quench. We exemplify this effect through the analysis of linear and non-linear quantum oscillators. We find that their distribution functions generically diverge logarithmically close to the stable points of the classical dynamics. Our study opens the possibility to address integrability and its breaking in distribution functions, with immediate applications to matter-wave interferometers.Comment: 8+10 pages. Scipost Submissio

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

An Optimization Strategy for Scheduling Various Thermal Energy Storage Technologies in Office Buildings Connected to Smart Grid

AbstractAn optimization strategy for scheduling various thermal energy storage capacities in an office building is investigated. An activated building wall (building thermal mass), a phase change material (PCM) tank, a hot water (HW) tank and a thermochemical material (TCM) storage are simulated and their charging and discharging behavior are optimized. Therefore, a case study is performed using a very simplified room model (office) and typical weather data for the Netherlands. To model the storage's scheduling behavior for control and optimization, a resistance capacitance (RC) network is applied. The RC network represents the critical energy storage parameters for optimization and control. The minimization of electricity costs is defined as optimization objective towards the Smart Grid. Cost saving potentials up to 12.5% are calculated using an electrical heat pump and a solar collector for heating the room and charging the thermal energy storage capacities

Prosumer Cluster of Single-Family Houses under the Danish Net Metering Policy

In the energy sector, prosumers are typically houses with rooftop PV. With the drastically falling prices of PV panels, the number of installations is rising. Prosumers can have negative impacts, on power grids especially in the distribution grid. In order to mitigate this effect, and for the own benefit of the prosumers, they can function as groups sharing their resources. A literature overview is given focusing on studies that deal with this issue from the prosumer perspective, showing that many optimization studies focus on maximizing economical benefits and others on self-consumption or related indicators by means of energy management strategies and market models, most often hourly based. A case study is presented in the context of the current Danish net-metering scheme. The results show that savings for prosumers and increase of total self-consumption can be achieved by redistributing energy within the building cluster with rule-based control

Neural network based predictive control of personalized heating systems

The aim of a personalized heating system is to provide a desirable microclimate for each individual when heating is needed. In this paper, we present a method based on machine learning algorithms for generation of predictive models for use in control of personalized heating systems. Data was collected from two individual test subjects in an experiment that consisted of 14 sessions per test subject with each session lasting 4 h. A dynamic recurrent nonlinear autoregressive neural network with exogenous inputs (NARX) was used for developing the models for the prediction of personalized heating settings. The models for subjects A and B were tested with the data that was not used in creating the neural network (unseen data) to evaluate the accuracy of the prediction. Trained NARX showed good performance when tested with the unseen data, with no sign of overfitting. For model A, the optimal network was with 12 hidden neurons with root mean square error equal to 0.043 and Pearson correlation coefficient equal to 0.994. The best result for model B was obtained with a neural network with 16 hidden neurons with root mean square error equal to 0.049 and Pearson correlation coefficient equal to 0.966. In addition to the neural network models, several other machine learning algorithms were tested. Furthermore, the models were on-line tested and the results showed that the test subjects were satisfied with the heating settings that were automatically controlled using the models. Tests with automatic control showed that both test subjects felt comfortable throughout the tests and test subjects expressed their satisfaction with the automatic control

Energy Flexibility of Building Cluster – Part I: Occupancy Modelling

With the growing application of renewable energy, the stability of power systems can be seriously affected due to the fluctuations in the instantaneous generated power. As one of the potential solutions for this upcoming challenge, energy flexibility of buildings has received attention for research and technology development. Demand response and energy flexibility should be implemented at a large scale to have the accumulated energy flexibility to a magnitude, which can be meaningful for energy sectors. Studies have shown that the energy flexibility of a building is greatly influenced by both building physical characteristics and occupancy pattern of residents. To the best knowledge of authors, occupancy has not been considered in the study of building cluster. The aim of this paper is to present the modelling process of occupancy/vacancy of Danish households based on Danish Time Use Survey (DTUS) 2008/09 data. In this paper, we present a data-driven approach to generate occupancy/vacancy models for different types of household and for building cluster of different scales. As the result, vacancy profile and vacancy duration models are developed. The stochasticity of occupancy is also unveiled. The next step is to apply these models to quantify energy flexibility of building cluster and the uncertainty of energy flexibility due to the stochastic occupancy

Are building users prepared for energy flexible buildings—A large-scale survey in the Netherlands

Building energy flexibility might play a crucial role in demand side management for integrating intermittent renewables into smart grids. The potential of building energy flexibility depends not only on the physical characteristics of a building but also on occupant behaviour in the building. Building users will have to adopt smart technologies and to change their daily energy use behaviours or routines, if energy flexibility is to be achieved. The willingness of users to make changes will determine how much demand flexibility can be achieved in buildings and whether energy flexible buildings can be realized. This will have a considerable impact on the transition to smart grids. This study is thus to assess the perception of smart grids and energy flexible buildings by building users, and their readiness for them on a large scale. We attempted to identify the key characteristics of the ideal user of flexible buildings. A questionnaire was designed and administered as an online survey in the Netherlands. The questionnaire consisted of questions about the sociodemographic characteristics of the current users, house type, household composition, current energy use behaviour, willingness to use smart technologies, and willingness to change energy use behaviour. The survey was completed by 835 respondents, of which 785 (94%) were considered to have provided a genuine response. Our analysis showed that the concept of smart grids is an unfamiliar one, as more than 60% of the respondents had never heard of smart grids. However, unfamiliarity with smart grids increased with age, and half of the respondents aged 20–29 years old were aware of the concept. Monetary incentives were identified as the biggest motivating factor for adoption of smart grid technologies. It was also found that people would be most in favour of acquiring smart dishwashers (65% of the respondents) and refrigerator/freezers (60%). Statistical analysis shows that people who are willing to use smart technologies are also willing to change their behaviour, and can thus be categorised as potentially flexible building users. Given certain assumptions, 11% of the respondents were found to be potentially flexible building users. To encourage people to be prepared for energy flexible buildings, awareness of smart grids will have to be increased, and the adoption of smart technologies may have to be promoted by providing incentives such as financial rewards

Modeling the Genetic Control of HIV-1 Dynamics After Highly Active Antiretroviral Therapy

The progression of HIV disease has been markedly slowed by the use of highly active antiretroviral therapy (HAART). However, substantial genetic variation was observed to occur among different people in the decay rate of viral loads caused by HAART. The characterization of specific genes involved in HIV dynamics is central to design personalized drugs for the prevention of this disease, but usually cannot be addressed by experimental methods alone rather than require the help of mathematical and statistical methods. A novel statistical model has been recently developed to detect genetic variants that are responsible for the shape of HAART-induced viral decay curves. This model was employed to an HIV/AIDS trial, which led to the identification of a major genetic determinant that triggers an effect on HIV dynamics. This detected major genetic determinant also affects several clinically important parameters, such as half-lives of infected cells and HIV eradication times