OBJECTIVE: Electronically linked datasets have become an important part of clinical research. Information from multiple sources can be used to identify comorbid conditions and patient outcomes, measure use of healthcare services, and enrich demographic and clinical variables of interest. Innovative approaches for creating research infrastructure beyond a traditional data system are necessary. MATERIALS AND METHODS: Records from a large healthcare system's enterprise data warehouse (EDW) were linked to a statewide population database, and a master subject index was created. The authors evaluate the linkage, along with the impact of missing information in EDW records and the coverage of the population database. The makeup of the EDW and population database provides a subset of cancer records that exist in both resources, which allows a cancer-specific evaluation of the linkage. RESULTS: About 3.4 million records (60.8%) in the EDW were linked to the population database with a minimum accuracy of 96.3%. It was estimated that approximately 24.8% of target records were absent from the population database, which enabled the effect of the amount and type of information missing from a record on the linkage to be estimated. However, 99% of the records from the oncology data mart linked; they had fewer missing fields and this correlated positively with the number of patient visits. DISCUSSION AND CONCLUSION: A general-purpose research infrastructure was created which allows disease-specific cohorts to be identified. The usefulness of creating an index between institutions is that it allows each institution to maintain control and confidentiality of their own information.

UNLABELLED: It has been argued that the missing heritability in common diseases may be in part due to rare variants and gene-gene effects. Haplotype analyses provide more power for rare variants and joint analyses across genes can address multi-gene effects. Currently, methods are lacking to perform joint multi-locus association analyses across more than one gene/region. Here, we present a haplotype-mining gene-gene analysis method, which considers multi-locus data for two genes/regions simultaneously. This approach extends our single region haplotype-mining algorithm, hapConstructor, to two genes/regions. It allows construction of multi-locus SNP sets at both genes and tests joint gene-gene effects and interactions between single variants or haplotype combinations. A Monte Carlo framework is used to provide statistical significance assessment of the joint and interaction statistics, thus the method can also be used with related individuals. This tool provides a flexible data-mining approach to identifying gene-gene effects that otherwise is currently unavailable. AVAILABILITY: http://bioinformatics.med.utah.edu/Genie/hapConstructor.html.

BACKGROUND: Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols. RESULTS: Our new phasing algorithm substantially outperformed the standard expectation-maximization algorithm that is ignorant of pedigree structure, and hence is preferable for resources that include pedigree structure. Through simulation we show that our Monte Carlo procedure maintains the correct type 1 error rates for all resource types. Power comparisons suggest that transmission-disequilibrium statistics are superior for performing association in resources of only nuclear families. For mixed structure resources, however, the newly implemented pseudocontrol approach appears to be the best choice. Results also indicated the value of large high-risk pedigrees for association analysis, which, in the simulations considered, were comparable in power to case-control resources of the same sample size. CONCLUSIONS: We propose hapMC as a valuable new tool to perform haplotype association analyses, particularly for resources of mixed structure. The availability of meta-association and haplotype-mining modules in our suite of Monte Carlo haplotype procedures adds further value to the approach.

MOTIVATION: Efficient models for genetic linkage disequilibrium (LD) are needed to enable appropriate statistical analysis of the dense, genome-wide single nucleotide polymorphism assays currently available. RESULTS: Estimation of graphical models for LD within a restricted class of decomposable models is shown to be possible using computer time and storage that scale linearly with the number of loci. Programs for estimation and for simulating from these models on a whole-genome basis are described and provided. AVAILABILITY: Java classes and source code for IntervalLD and GeneDrops are freely available over the internet at http://bioinformatics.med.utah.edu/~alun.

UNLABELLED: High throughput mutation screening in an automated environment generates large data sets that have to be organized and stored reliably. Complex multistep workflows require strict process management and careful data tracking. We have developed a Laboratory Information Management Systems (LIMS) tailored to high throughput candidate gene mutation scanning and resequencing that respects these requirements. Designed with a client/server architecture, our system is platform independent and based on open-source tools from the database to the web application development strategy. Flexible, expandable and secure, the LIMS, by communicating with most of the laboratory instruments and robots, tracks samples and laboratory information, capturing data at every step of our automated mutation screening workflow. An important feature of our LIMS is that it enables tracking of information through a laboratory workflow where the process at one step is contingent on results from a previous step. AVAILABILITY: Script for MySQL database table creation and source code of the whole JSP application are freely available on our website: http://www-gcs.iarc.fr/lims/. SUPPLEMENTARY INFORMATION: System server configuration, database structure and additional details on the LIMS and the mutation screening workflow are available on our website: http://www-gcs.iarc.fr/lims/

SUMMARY: Graphical modeling is used to extend the gene counting method to compute maximum likelihood estimates of allele frequencies for samples of individuals related in extended pedigrees. Genotypes may be missing or partially observed, and error rates can be simultaneously estimated. AVAILABILITY: The Java classes and Javadocs pages for \mathsf\hbox GeneCountAlleles can be obtained from bioinformatics.med.utah.edu/~alun, which also has more information on its use and file formats.

Summary: GMCheck uses graphical modeling to find the posterior probabilities of data errors given genotypes or phenotypes in a specified pedigree structure. Availability: The Java classes and Javadocs pages for GMCheck can be obtained from bioinformatics.med.utah.edu/~alun, which also has information on use, parameter settings and file formats. Contact: [email protected]

UNLABELLED: GCHap quickly finds maximum likelihood estimates (MLEs) of frequencies of haplotypes given genotype information on a random sample of individuals. It uses the gene counting method but by excluding haplotypes with zero MLE at an early stage, this implementation uses many orders of magnitude less space and time than naive implementations. A second program, ApproxGCHap, is provided to give alternate estimates for data sets with large numbers of loci or large amounts of missing genotypes. AVAILABILITY: The Java classes and Javadocs pages for GCHap can be obtained from bioinformatics.med.utah.edu/~alun