JLIN: A java based linkage disequilibrium plotter

BACKGROUND: A great deal of effort and expense are being expended internationally in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Techniques such as Linkage Disequilibrium mapping are being increasingly used to examine and compare markers across increasingly large datasets. Visualisation techniques are becoming essential to analyse the ever-growing volume of data and results available with any given analysis. RESULTS: JLIN (Java LINkage disequilibrium plotter) is a software package designed for customisable, intuitive visualisation of Linkage Disequilibrium (LD) across all common computing platforms. Customisation allows the user to choose particular visualisations, statistical measures and measurement ranges. JLIN also allows the user to export images of the LD visualisation in several common document formats. CONCLUSION: JLIN allows the user to visually compare and contrast the results of a range of statistical measures on the input dataset(s). These measures include the commonly used D' and r(2 )statistics and empirical p-values. JLIN has a number of unique and novel features that improve on existing LD visualisation tools

Genome-wide linkage and association mapping of disease genes with the GAW14 simulated datasets

We combined the results of whole-genome linkage and association analyses to determine which markers were most strongly associated with Kofendrerd Personality Disorder. Using replicate 1 from the Genetic Analysis Workshop 14 Aipotu, Karangar, Danacaa, and New York City simulated populations, we determined that several markers showed significant linkage and association with disease status. We used both SNP and microsatellite markers to determine patterns and chromosomal regions of markers. Three consistently associated markers were C01R0050, C03R0280, and C10R0882. Using generalized linear mixed models, we modelled the effect of the three predefined phenotypic categories on disease status and concluded that the phenotypes defining the "anxiety-related" category best predicted the outcome

The effect of missing data on linkage disequilibrium mapping and haplotype association analysis in the GAW14 simulated datasets

We used our newly developed linkage disequilibrium (LD) plotting software, JLIN, to plot linkage disequilibrium between pairs of single-nucleotide polymorphisms (SNPs) for three chromosomes of the Genetic Analysis Workshop 14 Aipotu simulated population to assess the effect of missing data on LD calculations. Our haplotype analysis program, SIMHAP, was used to assess the effect of missing data on haplotype-phenotype association. Genotype data was removed at random, at levels of 1%, 5%, and 10%, and the LD calculations and haplotype association results for these levels of missingness were compared to those for the complete dataset. It was concluded that ignoring individuals with missing data substantially affects the number of regions of LD detected which, in turn, could affect tagging SNPs chosen to generate haplotypes

SimHap GUI: An intuitive graphical user interface for genetic association analysis

<p>Abstract</p> <p>Background</p> <p>Researchers wishing to conduct genetic association analysis involving single nucleotide polymorphisms (SNPs) or haplotypes are often confronted with the lack of user-friendly graphical analysis tools, requiring sophisticated statistical and informatics expertise to perform relatively straightforward tasks. Tools, such as the <it>SimHap </it>package for the R statistics language, provide the necessary statistical operations to conduct sophisticated genetic analysis, but lacks a graphical user interface that allows anyone but a professional statistician to effectively utilise the tool.</p> <p>Results</p> <p>We have developed SimHap GUI, a cross-platform integrated graphical analysis tool for conducting epidemiological, single SNP and haplotype-based association analysis. SimHap GUI features a novel workflow interface that guides the user through each logical step of the analysis process, making it accessible to both novice and advanced users. This tool provides a seamless interface to the <it>SimHap </it>R package, while providing enhanced functionality such as sophisticated data checking, automated data conversion, and real-time estimations of haplotype simulation progress.</p> <p>Conclusion</p> <p>SimHap GUI provides a novel, easy-to-use, cross-platform solution for conducting a range of genetic and non-genetic association analyses. This provides a free alternative to commercial statistics packages that is specifically designed for genetic association analysis.</p

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/