BRISK--research-oriented storage kit for biology-related data.

MOTIVATION: In genetic science, large-scale international research collaborations represent a growing trend. These collaborations have demanding and challenging database, storage, retrieval and communication needs. These studies typically involve demographic and clinical data, in addition to the results from numerous genomic studies (omics studies) such as gene expression, eQTL, genome-wide association and methylation studies, which present numerous challenges, thus the need for data integration platforms that can handle these complex data structures. Inefficient methods of data transfer and access control still plague research collaboration. As science becomes more and more collaborative in nature, the need for a system that adequately manages data sharing becomes paramount. RESULTS: Biology-Related Information Storage Kit (BRISK) is a package of several web-based data management tools that provide a cohesive data integration and management platform. It was specifically designed to provide the architecture necessary to promote collaboration and expedite data sharing between scientists. AVAILABILITY AND IMPLEMENTATION: The software, documentation, Java source code and demo are available at http://genapha.icapture.ubc.ca/brisk/index.jsp. BRISK was developed in Java, and tested on an Apache Tomcat 6 server with a MySQL database. CONTACT: denise.daley@hli.ubc.ca.

SLIMS--a user-friendly sample operations and inventory management system for genotyping labs.

SUMMARY: We present the Sample-based Laboratory Information Management System (SLIMS), a powerful and user-friendly open source web application that provides all members of a laboratory with an interface to view, edit and create sample information. SLIMS aims to simplify common laboratory tasks with tools such as a user-friendly shopping cart for subjects, samples and containers that easily generates reports, shareable lists and plate designs for genotyping. Further key features include customizable data views, database change-logging and dynamically filled pre-formatted reports. Along with being feature-rich, SLIMS' power comes from being able to handle longitudinal data from multiple time-points and biological sources. This type of data is increasingly common from studies searching for susceptibility genes for common complex diseases that collect thousands of samples generating millions of genotypes and overwhelming amounts of data. LIMSs provide an efficient way to deal with this data while increasing accessibility and reducing laboratory errors; however, professional LIMS are often too costly to be practical. SLIMS gives labs a feasible alternative that is easily accessible, user-centrically designed and feature-rich. To facilitate system customization, and utilization for other groups, manuals have been written for users and developers. AVAILABILITY: Documentation, source code and manuals are available at http://genapha.icapture.ubc.ca/SLIMS/index.jsp. SLIMS was developed using Java 1.6.0, JSPs, Hibernate 3.3.1.GA, DB2 and mySQL, Apache Tomcat 6.0.18, NetBeans IDE 6.5, Jasper Reports 3.5.1 and JasperSoft's iReport 3.5.1.

Path: a tool to facilitate pathway-based genetic association analysis.

SUMMARY: Traditional methods of genetic study design and analysis work well under the scenario that a handful of single nucleotide polymorphisms (SNPs) independently contribute to the risk of disease. For complex diseases, susceptibility may be determined not by a single SNP, but rather a complex interplay between SNPs. For large studies involving hundreds of thousands of SNPs, a brute force search of all possible combinations of SNPs associated with disease is not only inefficient, but also results in a multiple testing paradigm, whereby larger and larger sample sizes are needed to maintain statistical power. Pathway-based methods are an example of one of the many approaches in identifying a subset of SNPs to test for interaction. To help determine which SNP-SNP interactions to test, we developed Path, a software application designed to help researchers interface their data with biological information from several bioinformatics resources. To this end, our application brings together currently available information from nine online bioinformatics resources including the National Center for Biotechnology Information (NCBI), Online Mendelian Inheritance in Man (OMIM), Kyoto Encyclopedia of Genes and Genomes (KEGG), UCSC Genome Browser, Seattle SNPs, PharmGKB, Genetic Association Database, the Single Nucleotide Polymorphism database (dbSNP) and the Innate Immune Database (IIDB). AVAILABILITY: The software, example datasets and tutorials are freely available from http://genapha.icapture.ubc.ca/PathTutorial.

A thymic stromal lymphopoietin gene variant is associated with asthma and airway hyperresponsiveness.

BACKGROUND: The epithelial cell-derived protein thymic stromal lymphopoietin stimulates dendritic and mast cells to promote proallergic T(H)2 responses. Studies of transgenic expression of thymic stromal lymphopoietin and its receptor knockout mice have emphasized its critical role in the development of allergic inflammation. Association of genetic variation in thymic stromal lymphopoietin with IgE levels has been reported for human subjects. OBJECTIVE: The aim of this study was to evaluate the relationship between variants of thymic stromal lymphopoietin and asthma and related phenotypes. METHODS: We selected 6 single nucleotide polymorphisms in thymic stromal lymphopoietin and genotyped 5565 individuals from 4 independent asthma studies and tested for association with asthma, atopy, atopic asthma, and airway hyperresponsiveness by using a general allelic likelihood ratio test. P values were corrected for the effective number of independent single nucleotide polymorphisms and phenotypes. RESULTS: The A allele of rs1837253, which is 5.7 kb upstream of the transcription start site of the gene, was associated with protection from asthma, atopic asthma, and airway hyperresponsiveness, with the odds ratios and corrected P values for each being 0.79 and 0.0058; 0.75 and 0.0074; and 0.76 and 0.0094, respectively. Associations between thymic stromal lymphopoietin and asthma-related phenotypes were the most statistically significant observations in our study, which has to date examined 98 candidate genes. Full results are available online at http://genapha.icapture.ubc.ca/. CONCLUSIONS: A genetic variant in the region of the thymic stromal lymphopoietin gene is associated with the phenotypes of asthma and airway hyperresponsiveness.

Analyses of associations with asthma in four asthma population samples from Canada and Australia.

Asthma, atopy, and related phenotypes are heterogeneous complex traits, with both genetic and environmental risk factors. Extensive research has been conducted and over hundred genes have been associated with asthma and atopy phenotypes, but many of these findings have failed to replicate in subsequent studies. To separate true associations from false positives, candidate genes need to be examined in large well-characterized samples, using standardized designs (genotyping, phenotyping and analysis). In an attempt to replicate previous associations we amalgamated the power and resources of four studies and genotyped 5,565 individuals to conduct a genetic association study of 93 previously associated candidate genes for asthma and related phenotypes using the same set of 861 single-nucleotide polymorphisms (SNPs), a common genotyping platform, and relatively harmonized phenotypes. We tested for association between SNPs and the dichotomous outcomes of asthma, atopy, atopic asthma, and airway hyperresponsiveness using a general allelic likelihood ratio test. No SNP in any gene reached significance levels that survived correction for all tested SNPs, phenotypes, and genes. Even after relaxing the usual stringent multiple testing corrections by performing a gene-based analysis (one gene at a time as if no other genes were typed) and by stratifying SNPs based on their prior evidence of association, no genes gave strong evidence of replication. There was weak evidence to implicate the following: IL13, IFNGR2, EDN1, and VDR in asthma; IL18, TBXA2R, IFNGR2, and VDR in atopy; TLR9, TBXA2R, VDR, NOD2, and STAT6 in airway hyperresponsiveness; TLR10, IFNGR2, STAT6, VDR, and NPSR1 in atopic asthma. Additionally we found an excess of SNPs with small effect sizes (OR < 1.4). The low rate of replication may be due to small effect size, differences in phenotypic definition, differential environmental effects, and/or genetic heterogeneity. To aid in future replication studies of asthma genes a comprehensive database was compiled and is available to the scientific community at http://genapha.icapture.ubc.ca/.

Deoxynucleotides can replace dideoxynucleotides in minisequencing by arrayed primer extension.

Scientific literature describing arrayed primer extension and other array-based minisequencing technologies consistently cite the requirement for four fluorescent dideoxynucleotides (with concomitant absence/inactivation of deoxynucleotides) to ensure single-base extension and thus sequence-specific intensity data that can be interpreted as a base call or genotype. We present compelling evidence that fluorescent deoxynucleotides can reliably be used in microarray minisequencing experiments, generating fluorescent sequence extension intensity profiles that are homologous to the single-base extensions obtained with terminator dideoxynucleotides. Due to the almost 10-fold higher costs (and limited fluorophore choice) of many commercially available fluorescent dideoxynucleotides, compared to fluorescent deoxynucleotides, as well as other potentially constraining intellectual property and licensing issues, this hitherto dismissed microarray chemistry represents an important reevaluation in the field of array-based genotyping and related enzymology.

Microarray genotyping resource to determine population stratification in genetic association studies of complex disease.

We have developed a robust microarray genotyping chip that will help advance studies in genetic epidemiology. In population-based genetic association studies of complex disease, there could be hidden genetic substructure in the study populations, resulting in false-positive associations. Such population stratification may confound efforts to identify true associations between genotype/haplotype and phenotype. Methods relying on genotyping additional null single nucleotide polymorphism (SNP) markers have been proposed, such as genomic control (GC) and structured association (SA), to correct association tests for population stratification. If there is an association of a disease with null SNPs, this suggests that there is a population subset with different genetic background plus different disease susceptibility. Genotyping over 100 null SNPs in the large numbers of patient and control DNA samples that are required in genetic association studies can be prohibitively expensive. We have therefore developed and tested a resequencing chip based on arrayed primer extension (APEX) from over 2000 DNA probe features that facilitate multiple interrogations of each SNP, providing a powerful, accurate, and economical means to simultaneously determine the genotypes at 110 null SNP loci in any individual. Based on 1141 known genotypes from other research groups, our GC SNP chip has an accuracy of 98.5%, including non-calls.

Robust SNP genotyping by multiplex PCR and arrayed primer extension.

BACKGROUND: Arrayed primer extension (APEX) is a microarray-based rapid minisequencing methodology that may have utility in 'personalized medicine' applications that involve genetic diagnostics of single nucleotide polymorphisms (SNPs). However, to date there have been few reports that objectively evaluate the assay completion rate, call rate and accuracy of APEX. We have further developed robust assay design, chemistry and analysis methodologies, and have sought to determine how effective APEX is in comparison to leading 'gold-standard' genotyping platforms. Our methods have been tested against industry-leading technologies in two blinded experiments based on Coriell DNA samples and SNP genotype data from the International HapMap Project. RESULTS: In the first experiment, we genotyped 50 SNPs across the entire 270 HapMap Coriell DNA sample set. For each Coriell sample, DNA template was amplified in a total of 7 multiplex PCRs prior to genotyping. We obtained good results for 41 of the SNPs, with 99.8% genotype concordance with HapMap data, at an automated call rate of 94.9% (not including the 9 failed SNPs). In the second experiment, involving modifications to the initial DNA amplification so that a single 50-plex PCR could be achieved, genotyping of the same 50 SNPs across each of 49 randomly chosen Coriell DNA samples allowed extremely robust 50-plex genotyping from as little as 5 ng of DNA, with 100% assay completion rate, 100% call rate and >99.9% accuracy. CONCLUSION: We have shown our methods to be effective for robust multiplex SNP genotyping using APEX, with 100% call rate and >99.9% accuracy. We believe that such methodology may be useful in future point-of-care clinical diagnostic applications where accuracy and call rate are both paramount.

MACGT: multi-dimensional automated clustering genotyping tool for analysis of microarray-based mini-sequencing data.

SUMMARY: Multi-dimensional Automated Clustering Genotyping Tool (MACGT) is a Java application that clusters complex multi-dimensional vector data derived from single nucleotide polymorphism (SNP) genotyping experiments using mini-sequencing based microarray chemistries such as arrayed primer extension (APEX). Spot intensity output files from microarray experiments across multiple samples are imported into MACGT. The datasets can include four channels of intensity data for each spot, replica spots for each SNP probe and multiple probe types (APEX and allele-specific APEX probes) on both DNA strands for each SNP. MACGT automatically clusters these multi-dimensionality datasets for each SNP across multiple samples. Incorporation of additional array datasets from known samples that have previously validated SNP genotype calls allows unknown samples to be automatically assigned a genotype based on the clustering, along with numerical measures of confidence for each genotype call. Calling accuracy by MACGT exceeds 98% when applied to genotyping data from APEX microarrays, and can be increased to >99.5% by applying thresholds to the confidence measures.

SNP Chart: an integrated platform for visualization and interpretation of microarray genotyping data.

UNLABELLED: SNP Chart is a Java application for the visualization and interpretation of microarray genotyping data primarily derived from arrayed primer extension-based chemistries. Spot intensity output files from microarray analysis tools are imported into SNP Chart, together with a multi-channel TIFF image of the original array experiment and a list of the actual single nucleotide polymorphisms (SNPs) being tested. Data from different and/or replicate probes that interrogate the same SNP, but that are scattered across the array grid, can be reassembled into a single chart format, specific for the SNP. This allows a quick and very effective 'visualization'/'quality control' of the data from multiple probes for the same SNP that can be easily interpreted and manually scored as a genotype. AVAILABILITY: http://www.snpchart.ca.