Formal Testing Approach Research Articles

297 GWAS for complex models accounting for populations structure with GBLUP and ssGBLUP

Abstract Genomic Best Linear Unbiased Prediction (GBLUP) is the method of choice for incorporating genomic information into the genetic evaluation of livestock species. Furthermore, single step GBLUP (ssGBLUP) is adopted by many breeders’ associations and private entities managing large scale breeding programs. While prediction of breeding values remains the primary use of genomic markers in animal breeding, a secondary interest focuses on performing genome-wide association studies (GWAS). The goal of GWAS is to uncover genomic regions that harbor variants that explain a large proportion of the phenotypic variance, and thus become candidates for discovering and studying causative variants. Several methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most methods commonly avoid formal hypothesis testing and resort to estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions. However, with the advent of high throughput phenomics and transcriptomics, a more formal testing approach with automatic discovery thresholds is more appealing. In this work we present the methodological details of a method for performing formal hypothesis testing for GWAS in GBLUP models. First, we present the method and its equivalencies and differences with other GWAS methods. Moreover, we demonstrate through simulation analyses that the proposed method controls type I error rate at the nominal level. Second, we demonstrate two possible computational implementations based on mixed model equations for ssGBLUP and based on the generalized least square equations (GLS). We show that ssGBLUP can deal with datasets with extremely large number of animals and markers and with multiple traits. GLS implementations are well suited for dealing with smaller number of animals with tens of thousands of phenotypes. Third, we show several useful extensions, such as: testing multiple markers at once, testing pleiotropic effects and testing association of social genetic effects.

A Formal Data-Centric Approach for Passive Testing of Communication Protocols

There is currently a high level of consciousness of the importance and impact of formally testing communicating networks. By applying formal description techniques and formal testing approaches, we are able to validate the conformance of implementations to the requirements of communication protocols. In this context, passive testing techniques are used whenever the system under test cannot be interrupted or access to its interfaces is unavailable. Under such conditions, communication traces are extracted from points of observation and compared to the expected behavior formally specified as properties. Since most works on the subject come from a formal model context, they are optimized for testing the control part of the communication with a secondary focus on the data parts. In the current work, we provide a data-centric approach for black-box testing of network protocols. A formalism is provided to express complex properties in a bottom-up fashion starting from expected data relations in messages. A novel algorithm is provided for evaluation of properties in protocol traces. Experimental results on Session Initiation Protocol (SIP) traces for IP Multimedia Subsystem (IMS) services are provided.

Tests for Independence between Marks and Points of a Marked Point Process

A convenient assumption while modeling a marked point process is that the observations (i.e., marks) and the locations (i.e., points) are independent. We propose new graphical and formal testing approaches to test for this assumption. The proposed graphical procedures are easy to obtain and can be used to diagnose the nature and range of dependence between marks and points. The formal testing procedures require only minimal conditions on marks and thus can be applied to a variety of settings. We illustrate these procedures through a simulation study and an application to some real data.

Assessing missing data assumptions in longitudinal studies: an example using a smoking cessation trial

Due to the chaotic nature of the clinical disorder, longitudinal data analysis in substance abuse research is plagued by missing values. To obtain an unbiased estimation on intervention effects, different longitudinal modeling strategies require various assumptions on the patterns and mechanisms of missing data. By defining missingness as intermittent missingness (occasional omission) and dropout (premature withdrawal), this article demonstrates statistical ways for assessing missing data assumptions using evidence from a clinical trial. Within the framework of multiple imputation, intermittent missing data are imputed first so that dropouts can be isolated and treated specifically. A computational tool called “pattern reduction resampling” is proposed to simplify missing data methods when the number of intra-subject repeated measures is large. To test whether missingness patterns are nondifferential across treatment conditions, a formal testing approach treats indicators of missingness as a special type of repeated measures (e.g., 0: intermittent missing, 1: observed, and 2: dropout missing). After reviewing the idea of ignorability for missing data and of classifying missingness mechanisms into subcategories, the article provides an example for assessing common assumptions on missingness mechanisms and how these assumptions affect model selection for significance testing. A carbon monoxide longitudinal data set in a smoking cessation study is used for illustration.

Statistical inferences for varying-coefficint models based on locally weighted regression technique

Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fited by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.

Statechart testing method for aircraft control systems

AbstractA number of current control systems for aircraft have been specified with statecharts. The risk of failures requires the use of a formal testing approach to ensure that all possible faults are considered. However, testing the compliance of an implementation of a system to its specification is dependent on the specification method and little work has been reported relating to the use of statechart‐specific methods. This paper describes a modification of a formal testing method for extended finite‐state machines to handle the above problem. The method allows one to demonstrate correct behaviour of an implementation of some system, with respect to its specification, provided certain specific requirements for both of them are satisfied. The case study illustrates these and shows the applicability of the method. By considering the process used to develop the system it is possible to reduce the size of the test set dramatically; the method to be described is easy to automate. Copyright © 2001 John Wiley & Sons, Ltd.