Enrichment of lymphoma and CLL risk SNPs in DNase-hypersensitive sites of lymphoblastoid cell lines.

<p>(A-I) These histograms represent the distribution of how many random loci overlap a specific annotation. The blue represents the mean of the empirical null distribution while the red line represents the real number of loci from the lymphoma and CLL GWAS that overlap the DNase hypersensitive site in the specified cell line: (A) GM19238 (B) GM19240 (C) GM12864 (D) GM12865 (E) GM06990 (F) GM19239 (G) GM18507 (H) GM12892 (I) GM12891. (J) Th0 (K) CD20+ (L) Summary of distribution of tissue of origin for cell lines in which lymphoma and CLL risk SNPs are either enriched (p<0.0004) in DNase hypersensitive sites or not enriched.</p

Overlap of lymphoma risk SNPs with regulatory regions in GM12878.

<p>The histograms represent the distribution of how many random loci overlap a specific annotation. The blue represents the mean of the empirical null distribution while the red line represents the real number of loci from the lymphoma and CLL GWAS that overlap the specific regulatory annotation. A, Overlap of SNPs with DNase hypersensitivity regions in GM12878. B, Overlap of SNPs with active promoters and strong enhancers as annotated by ChromHMM in GM12878. C, Overlap of SNPs with active promoters and strong enhancers as annotated by Segway in GM12878.</p

UES algorithm visualization.

<p>This represents the generalized workflow to determine the SNP enrichment in an ENCODE track. A full description and details of the algorithm can be found in the Materials and Methods.</p

DataSheet_1_Automated clustering reveals CD4+ T cell subset imbalances in rheumatoid arthritis.docx

BackgroundDespite the report of an imbalance between CD4+ T helper (Th) cell subsets in rheumatoid arthritis (RA), patient stratification for precision medicine has been hindered by the discovery of ever more Th cell subsets, as well as contradictory association results.ObjectivesTo capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results.MethodsUnstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intra-nuclear transcription factors). First, conventional biaxial gating and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Second, unsupervised clustering was performed with FlowSOM and analysed using mixed-effects modelling of Associations of Single Cells (MASC).ResultsConventional analytical techniques fail to identify classical Th subset imbalance, while unsupervised automated clustering, by allowing for unusual marker combinations, identified an imbalance between pro- and anti-inflammatory subsets. For example, a pro-inflammatory Th1-like (IL-2+ T-bet+) subset and an unconventional but pro-inflammatory IL-17+ T-bet+ subset were significantly enriched in RA (odds ratio=5.7, p=2.2 x 10-3; odds ratio=9.7, p=1.5x10-3, respectively). In contrast, a FoxP3+ IL-2+ HLA-DR+ Treg-like subset was reduced in RA (odds ratio=0.1, p=7.7x10-7).ConclusionTaking an unbiased approach to large dataset analysis using automated clustering algorithms captures non-canonical CD4+ T cell subset imbalances in RA blood.</p

Table_2_Automated clustering reveals CD4+ T cell subset imbalances in rheumatoid arthritis.xlsx

BackgroundDespite the report of an imbalance between CD4+ T helper (Th) cell subsets in rheumatoid arthritis (RA), patient stratification for precision medicine has been hindered by the discovery of ever more Th cell subsets, as well as contradictory association results.ObjectivesTo capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results.MethodsUnstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intra-nuclear transcription factors). First, conventional biaxial gating and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Second, unsupervised clustering was performed with FlowSOM and analysed using mixed-effects modelling of Associations of Single Cells (MASC).ResultsConventional analytical techniques fail to identify classical Th subset imbalance, while unsupervised automated clustering, by allowing for unusual marker combinations, identified an imbalance between pro- and anti-inflammatory subsets. For example, a pro-inflammatory Th1-like (IL-2+ T-bet+) subset and an unconventional but pro-inflammatory IL-17+ T-bet+ subset were significantly enriched in RA (odds ratio=5.7, p=2.2 x 10-3; odds ratio=9.7, p=1.5x10-3, respectively). In contrast, a FoxP3+ IL-2+ HLA-DR+ Treg-like subset was reduced in RA (odds ratio=0.1, p=7.7x10-7).ConclusionTaking an unbiased approach to large dataset analysis using automated clustering algorithms captures non-canonical CD4+ T cell subset imbalances in RA blood.</p

Table_1_Automated clustering reveals CD4+ T cell subset imbalances in rheumatoid arthritis.xlsx

BackgroundDespite the report of an imbalance between CD4+ T helper (Th) cell subsets in rheumatoid arthritis (RA), patient stratification for precision medicine has been hindered by the discovery of ever more Th cell subsets, as well as contradictory association results.ObjectivesTo capture previously reported Th imbalance in RA with deep immunophenotyping techniques; to compare hypothesis-free unsupervised automated clustering with hypothesis-driven conventional biaxial gating and explore if Th cell heterogeneity accounts for conflicting association results.MethodsUnstimulated and stimulated peripheral blood mononuclear cells from 10 patients with RA and 10 controls were immunophenotyped with a 37-marker panel by mass cytometry (chemokine receptors, intra-cellular cytokines, intra-nuclear transcription factors). First, conventional biaxial gating and standard definitions of Th cell subsets were applied to compare subset frequencies between cases and controls. Second, unsupervised clustering was performed with FlowSOM and analysed using mixed-effects modelling of Associations of Single Cells (MASC).ResultsConventional analytical techniques fail to identify classical Th subset imbalance, while unsupervised automated clustering, by allowing for unusual marker combinations, identified an imbalance between pro- and anti-inflammatory subsets. For example, a pro-inflammatory Th1-like (IL-2+ T-bet+) subset and an unconventional but pro-inflammatory IL-17+ T-bet+ subset were significantly enriched in RA (odds ratio=5.7, p=2.2 x 10-3; odds ratio=9.7, p=1.5x10-3, respectively). In contrast, a FoxP3+ IL-2+ HLA-DR+ Treg-like subset was reduced in RA (odds ratio=0.1, p=7.7x10-7).ConclusionTaking an unbiased approach to large dataset analysis using automated clustering algorithms captures non-canonical CD4+ T cell subset imbalances in RA blood.</p

Haplotype risk analysis of WTCCC type 1 diabetes data.

<p>We assessed the risk of haplotypes spanning <i>HLA-DRB1</i>, <i>HLA-DQA1</i> and <i>HLA-DQB1</i>, and compared these to the published risk estimates from an independent study <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0064683#pone.0064683-Cucca1" target="_blank">[20]</a>. The published odds ratios were based on transmission/non-transmission of alleles from familial data, while our odds ratios were estimated from case/control data. We used the same classification scheme by dividing haplotypes into three risk groups. The odds ratios are computed with respect to the DRB1*01-DQA1*0101-DQB1*0501 haplotype.</p

Hierarchical clustering of 45 MI/CAD GWAS SNPs and specific cell types for a histone modification mark (H3K27ac).

<p>We mapped 45 MI/CAD GWAS SNPs, as well as SNPs in high linkage disequilibrium (<i>r</i>²≥0.8), to H3K27ac in different cell types. Hierarchical clustering was based on the presence or absence of a SNP residing in H3K27ac in different cell types and was performed using the heatmap function in R (R Project for Statistical Computing). We observed unique patterns between the different GWAS loci and cell types. For example, 12 of the 45 GWAS loci were expressed in more than 80% of the cell types, whereas 13 of the 45 GWAS loci were expressed in less than 20%. Red color indicates a lead SNP or tag SNPs (linkage disequilibrium value of <i>r</i>²≥0.8) residing in H3K27ac in different cell types (See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.s009" target="_blank">S9</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.s010" target="_blank">S10</a> Figs for H3K9ac and H3K4me3, respectively). MI, myocardial infarction; CAD, coronary artery disease; GWAS, genome-wide association study; SNP, single nucleotide polymorphism.</p

Significant enrichment of 45 MI/CAD-associated SNPs in specific cell types detected by enrichment analysis.

<p>We examined whether 45 MI/CAD-associated loci were enriched in regions of inferred strong enhancer chromatin states [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref027" target="_blank">27</a>] in specific cell types using NIH Roadmap data and two mammalian conservation algorithms, GERP and SiPhy-omega, implemented in HaploReg v2 [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref026" target="_blank">26</a>]. We observed significant enrichment of MI/CAD-associated SNPs in specific cell types, including adipose nuclei, spleen and brain tissue. MI, myocardial infarction; CAD, coronary artery disease; SNP, single nucleotide polymorphism.</p

Overview of the SNP2HLA imputation procedure.

<p>The reference panel (top) contains SNPs in the MHC, classical HLA alleles at the class I and class II loci, and amino acid sequences corresponding to the 4-digit HLA types at each locus. For a data set with genotyped SNPs across the MHC (bottom), we use the reference panel to impute classical alleles and their corresponding amino acid polymorphisms.</p