Limits...
Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping.

Wallace C, Cutler AJ, Pontikos N, Pekalski ML, Burren OS, Cooper JD, García AR, Ferreira RC, Guo H, Walker NM, Smyth DJ, Rich SS, Onengut-Gumuscu S, Sawcer SJ, Ban M, Richardson S, Todd JA, Wicker LS - PLoS Genet. (2015)

Bottom Line: In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813.The results support a shared causal variant for T1D and MS.Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

View Article: PubMed Central - PubMed

Affiliation: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom; MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, United Kingdom.

ABSTRACT
Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 ≃ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

No MeSH data available.


Related in: MedlinePlus

Six sets of SNPs can best explain the association of T1D and MS in the chromosome 10p15 region.LD: a heatmap indicating the r2 between SNPs. Assoc: MPPI for MS and T1D the SNPs in a group, with total MPPI across a SNP group, gMPPI, indicated by the height of the shaded rectangle (see Table 5 for numerical details). SNP groups are labelled by the letters A-F for reference. SNPs in this track are ordered by SNP group for ease of visualisation. Genes: SNPs are mapped back to physical position and shown in relation to genes in the region. RNAseq: read counts in two pooled replicates of resting (“rest1” and “rest2”) and anti-CD3/CD28 stimulated (“stim1” and “stim2”) CD4+ T cells; y axes were truncated to allow visualization of intronic read counts. Note the different limits for resting and stimulated cells, which show greater transcription of all protein coding genes in the region. DNase: DNase hypersensitivity measured in CD4 cells by the Roadmap consortium. Replicate 1 (“rest1”) is RO_01689; replicate 2 (“rest2”) is RO_01736; y axes were truncated again to improve visualization.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4481316&req=5

pgen.1005272.g003: Six sets of SNPs can best explain the association of T1D and MS in the chromosome 10p15 region.LD: a heatmap indicating the r2 between SNPs. Assoc: MPPI for MS and T1D the SNPs in a group, with total MPPI across a SNP group, gMPPI, indicated by the height of the shaded rectangle (see Table 5 for numerical details). SNP groups are labelled by the letters A-F for reference. SNPs in this track are ordered by SNP group for ease of visualisation. Genes: SNPs are mapped back to physical position and shown in relation to genes in the region. RNAseq: read counts in two pooled replicates of resting (“rest1” and “rest2”) and anti-CD3/CD28 stimulated (“stim1” and “stim2”) CD4+ T cells; y axes were truncated to allow visualization of intronic read counts. Note the different limits for resting and stimulated cells, which show greater transcription of all protein coding genes in the region. DNase: DNase hypersensitivity measured in CD4 cells by the Roadmap consortium. Replicate 1 (“rest1”) is RO_01689; replicate 2 (“rest2”) is RO_01736; y axes were truncated again to improve visualization.

Mentions: We selected all high confidence SNP groups for more detailed exploration (Fig 3). The T1D signals are located in (1) intron 1 of IL2RA—SNP group A, (2) intergenic between IL2RA and RBM17—C, (3) 5’ of RBM17—E, and (4) 5’ of RBM17 to intron 2 of PFKFB3—F. Under the model M1 for MS, SNP group A was also associated with MS, with the same alleles protective for both (Table 5) whilst the second M1 signal (SNP group D) physically overlapped, but was not in LD with, SNPs from group C. Under the model M2, the sole-MS associated SNP (B) is located in intron 1 of IL2RA, neighbouring the T1D-associated SNP group A, but there was only weak LD between A and B (r2 = 0.3).


Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping.

Wallace C, Cutler AJ, Pontikos N, Pekalski ML, Burren OS, Cooper JD, García AR, Ferreira RC, Guo H, Walker NM, Smyth DJ, Rich SS, Onengut-Gumuscu S, Sawcer SJ, Ban M, Richardson S, Todd JA, Wicker LS - PLoS Genet. (2015)

Six sets of SNPs can best explain the association of T1D and MS in the chromosome 10p15 region.LD: a heatmap indicating the r2 between SNPs. Assoc: MPPI for MS and T1D the SNPs in a group, with total MPPI across a SNP group, gMPPI, indicated by the height of the shaded rectangle (see Table 5 for numerical details). SNP groups are labelled by the letters A-F for reference. SNPs in this track are ordered by SNP group for ease of visualisation. Genes: SNPs are mapped back to physical position and shown in relation to genes in the region. RNAseq: read counts in two pooled replicates of resting (“rest1” and “rest2”) and anti-CD3/CD28 stimulated (“stim1” and “stim2”) CD4+ T cells; y axes were truncated to allow visualization of intronic read counts. Note the different limits for resting and stimulated cells, which show greater transcription of all protein coding genes in the region. DNase: DNase hypersensitivity measured in CD4 cells by the Roadmap consortium. Replicate 1 (“rest1”) is RO_01689; replicate 2 (“rest2”) is RO_01736; y axes were truncated again to improve visualization.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481316&req=5

pgen.1005272.g003: Six sets of SNPs can best explain the association of T1D and MS in the chromosome 10p15 region.LD: a heatmap indicating the r2 between SNPs. Assoc: MPPI for MS and T1D the SNPs in a group, with total MPPI across a SNP group, gMPPI, indicated by the height of the shaded rectangle (see Table 5 for numerical details). SNP groups are labelled by the letters A-F for reference. SNPs in this track are ordered by SNP group for ease of visualisation. Genes: SNPs are mapped back to physical position and shown in relation to genes in the region. RNAseq: read counts in two pooled replicates of resting (“rest1” and “rest2”) and anti-CD3/CD28 stimulated (“stim1” and “stim2”) CD4+ T cells; y axes were truncated to allow visualization of intronic read counts. Note the different limits for resting and stimulated cells, which show greater transcription of all protein coding genes in the region. DNase: DNase hypersensitivity measured in CD4 cells by the Roadmap consortium. Replicate 1 (“rest1”) is RO_01689; replicate 2 (“rest2”) is RO_01736; y axes were truncated again to improve visualization.
Mentions: We selected all high confidence SNP groups for more detailed exploration (Fig 3). The T1D signals are located in (1) intron 1 of IL2RA—SNP group A, (2) intergenic between IL2RA and RBM17—C, (3) 5’ of RBM17—E, and (4) 5’ of RBM17 to intron 2 of PFKFB3—F. Under the model M1 for MS, SNP group A was also associated with MS, with the same alleles protective for both (Table 5) whilst the second M1 signal (SNP group D) physically overlapped, but was not in LD with, SNPs from group C. Under the model M2, the sole-MS associated SNP (B) is located in intron 1 of IL2RA, neighbouring the T1D-associated SNP group A, but there was only weak LD between A and B (r2 = 0.3).

Bottom Line: In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813.The results support a shared causal variant for T1D and MS.Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

View Article: PubMed Central - PubMed

Affiliation: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom; MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, United Kingdom.

ABSTRACT
Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 ≃ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

No MeSH data available.


Related in: MedlinePlus