Ben W. Tripp

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.

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.

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. Contact: ddaley@mrl.ubc.ca

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.

Hybrid Spot Segmentation in Four-Channel Microarray Genotyping Image Data

In this paper we present a novel hybrid algorithm for spot segmentation in four-channel genotyping microarray images. A new four-dimensional clustering approach for fully-automated spot segmentation is proposed, along with a new iterative method to automatically identify the number of clusters in a single-spot area. A spatial shape detection step is simultaneously applied, which assists a nonlinear diffusion filtering step in detecting the connected objects, while a spot masking step prevents various noise types from misleading the spot extraction algorithm. The developed analysis system successfully handles various four-channel as well as traditional two-channel microarray image data obtained from different sources. The platform is tested on 34 microarray data sets. The segmentation algorithm accurately segments donut-shaped spots, as well as irregular spot shapes, spots with intensity variations and different noise types effectively. The data extracted from the samples is fed to genotyping algorithms with the results achieving high accuracy rates of up to 99.8% with a call rate of 90.8%

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.

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.