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VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes.

Burren OS, Guo H, Wallace C - Bioinformatics (2014)

Bottom Line: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility.Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms.The approach is implemented in VSEAMS, a freely available software pipeline.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Genetics, JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Cambridge Biomedical Campus, Cambridge, CB2 0XY, UK and MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.

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Comparison of VSEAMS and permuted phenotype methods with differing sample size, for example, gene sets, where enrichment is present (IKZF3) and absent (YY1). (a) Shows difference in Z-scores between both methods with 10 000 simulations and a variable sample size, with an equal number of cases and controls. (b) Shows how the correlation between Z-scores over a variable number of permutations varies with respect to sample size. The coloured lines represent a locally estimated scatterplot smoothing (LOESS) fitted model for each sample size
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btu571-F5: Comparison of VSEAMS and permuted phenotype methods with differing sample size, for example, gene sets, where enrichment is present (IKZF3) and absent (YY1). (a) Shows difference in Z-scores between both methods with 10 000 simulations and a variable sample size, with an equal number of cases and controls. (b) Shows how the correlation between Z-scores over a variable number of permutations varies with respect to sample size. The coloured lines represent a locally estimated scatterplot smoothing (LOESS) fitted model for each sample size

Mentions: We picked two gene sets from the Cusanovich et al. (2014) dataset with similar test set SNP counts to examine the effect of sample size and gene set selection on VSEAMS performance, IKZF3 as an example where enrichment is present and YY1 where it is absent. Both sets exhibited similar behaviour. In general, we see that the number of permutations required for a stable correlation between permutation and VSEAMS Z-scores is independent of sample size and is mainly dependent on gene set, and for these gene sets, 5000 simulations seems sufficient to ensure VSEAMS is a good approximation for permutation. At sample sizes <10 with a fixed number of permutations, we observe a large difference between Z-scores generated using VSEAMS and permutation method (Fig. 5). Small sample sizes (<200) show reduced correlation even for large numbers of permutations.Fig. 5.


VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes.

Burren OS, Guo H, Wallace C - Bioinformatics (2014)

Comparison of VSEAMS and permuted phenotype methods with differing sample size, for example, gene sets, where enrichment is present (IKZF3) and absent (YY1). (a) Shows difference in Z-scores between both methods with 10 000 simulations and a variable sample size, with an equal number of cases and controls. (b) Shows how the correlation between Z-scores over a variable number of permutations varies with respect to sample size. The coloured lines represent a locally estimated scatterplot smoothing (LOESS) fitted model for each sample size
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4296156&req=5

btu571-F5: Comparison of VSEAMS and permuted phenotype methods with differing sample size, for example, gene sets, where enrichment is present (IKZF3) and absent (YY1). (a) Shows difference in Z-scores between both methods with 10 000 simulations and a variable sample size, with an equal number of cases and controls. (b) Shows how the correlation between Z-scores over a variable number of permutations varies with respect to sample size. The coloured lines represent a locally estimated scatterplot smoothing (LOESS) fitted model for each sample size
Mentions: We picked two gene sets from the Cusanovich et al. (2014) dataset with similar test set SNP counts to examine the effect of sample size and gene set selection on VSEAMS performance, IKZF3 as an example where enrichment is present and YY1 where it is absent. Both sets exhibited similar behaviour. In general, we see that the number of permutations required for a stable correlation between permutation and VSEAMS Z-scores is independent of sample size and is mainly dependent on gene set, and for these gene sets, 5000 simulations seems sufficient to ensure VSEAMS is a good approximation for permutation. At sample sizes <10 with a fixed number of permutations, we observe a large difference between Z-scores generated using VSEAMS and permutation method (Fig. 5). Small sample sizes (<200) show reduced correlation even for large numbers of permutations.Fig. 5.

Bottom Line: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility.Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms.The approach is implemented in VSEAMS, a freely available software pipeline.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Genetics, JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Cambridge Biomedical Campus, Cambridge, CB2 0XY, UK and MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.

Show MeSH
Related in: MedlinePlus