summaryHardy-Weinberg disequilibrium (HWD) among affected individuals has recently been proposed for fine-scale mapping of disease susceptibility genes. We investigate the statistical properties of several available HWD measures and develop a new HWD measure J for fine-scale mapping. It is shown both theoretically and through simulations that the available HWD measures depend not only on the genetic distance between the marker locus of interest and the disease susceptibility locus, but also on the allele frequencies at the marker locus. On the contrary, the new measure is not affected by the allele frequencies at the marker locus under the following assumptions: (a) there is initial complete linkage disequilibrium between the marker and the disease loci, (b) there are no new mutations at the marker and the disease loci, and (c) the population under study is large. We develop a novel method to estimate the location of the disease susceptibility gene based on the HWD measure J. The estimator is robust to low mutation rates at the marker and the disease loci. We compare the standard error of the estimated disease gene loci using P excess for case-control studies with the standard error using J for case-only studies under various disease models.The newly developed method is successfully applied to a data set on hereditary haemochromatosis (HH).introduction

The transmission\disequilibrium test (TDT) is a powerful method of locating disease genes. The TDT was originally proposed for use in studies of qualitative traits in families with both parents available. Recently, the TDT has been extended to studies of qualitative traits in sibships without parents available and in families with one parent available. It has also been extended for use in studies of quantitative traits in families with both parents available and in sibships with multiple offspring. In this paper, we first propose a new class of TDT-type tests for linkage in the presence of linkage disequilibrium for use in studies of families with both parents available. The TDT of Spielman et al. (1993) for qualitative traits and the TDT of Rabinowitz (1997) for quantitative traits are special cases of the new tests. Second, we propose a new class of TDT-type tests for linkage for use in studies of families with one parent available. Third, we study the validity and the power of the tests using simulations. Finally, we propose a method of combining data from different types of families. The combined test is valuable and allows researchers full use of the available data in detecting linkage between a marker locus and an unobservable quantitative trait locus. An important feature of the tests proposed in this paper is that no assumptions on the distribution of the quantitative traits are needed.

Abstract-There has been a recent push towards applying information technology principles, such as workflows, to bring greater efficiency to reservoir management tasks. These workflows are data intensive in nature, and the data is derived from heterogenous data sources. This has placed an emphasis on the quality and reliability of data that is used in reservoir engineering applications. Data provenance is metadata that pertains to the history of the data and can be used to assess data quality. In this paper, we present an approach for collecting provenance information from application logs in the domain of reservoir engineering. In doing so, we address challenges due to: 1) the lack of a workflow orchestration framework in reservoir engineering and 2) the inability of many reservoir engineering applications to collect provenance information. We present an approach that uses the workflow instances detection algorithm and the Open Provenance Model (OPM) for capturing provenance information from the logs.

A system that captures knowledge from experienced users is of great interest in the oil industry. An important source of knowledge is application logs that record user activities. However, most of the log files are sequential records of pre-defined low level actions. It is often inconvenient or even impossible for humans to view and obtain useful information from these log entries. Also, the heterogeneity of log data in terms of syntax and granularity makes it challenging to extract the underlying knowledge from log files. In this paper, we propose a semantically rich workflow model to capture the semantics of user activities in a hierarchical structure. The mapping from low level log entries to semantic level workflow components enables automatic aggregation of log entries and their high level representation. We model and analyze two cases from the petroleum engineering domain in detail. We also present an algorithm that detects workflow instances from log files. Experimental results show that the detection algorithm is efficient and scalable.