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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

The proportion of naive CD4+ T cells that express CD25 (log scale) increases with age.The MS protective allele for the M2 SNP rs41295055:C > T associates with fewer CD4+ T cells expressing CD25 across all ages (p = 3.45 × 10−8), and is statistically preferred to the previously reported M1 SNP, rs2104286:T > C (p = 2.56 × 10−6; Δ BIC = 8.43). S and P are used to represent the (common) MS-susceptible and (rare) MS-protective alleles respectively at each SNP. These SNPs are in limited LD (r2 = 0.3).
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pgen.1005272.g004: The proportion of naive CD4+ T cells that express CD25 (log scale) increases with age.The MS protective allele for the M2 SNP rs41295055:C > T associates with fewer CD4+ T cells expressing CD25 across all ages (p = 3.45 × 10−8), and is statistically preferred to the previously reported M1 SNP, rs2104286:T > C (p = 2.56 × 10−6; Δ BIC = 8.43). S and P are used to represent the (common) MS-susceptible and (rare) MS-protective alleles respectively at each SNP. These SNPs are in limited LD (r2 = 0.3).

Mentions: Since we were unable to distinguish between the competing M1 and M2 models for MS using SNP-disease association data alone, we sought corroborating evidence to support SNPs in either model using CD25 flow cytometric expression data. The M2 SNP rs2104286 has been associated with the age-dependent proportion of naïve T cells that express CD25 on their cell surface and rs12722495 (within SNP group A, located in intron 1 of IL2RA) with the total expression of CD25 on memory T cells [19, 20]. rs12722495 has also been associated with IL2RA mRNA expression in T cells, both resting [19] and activated by 48 hour culture with anti-CD3+CD28 [21]. We selected a single tag SNP from each of the credible sets and examined which could best explain CD25 protein expression phenotypes using previously published data from 179 samples [19], plus an additional 30 samples. Both phenotypes were best explained by a single SNP: rs12722495 from group A again showed the strongest association with intensity of CD25 expression on memory T cells (p = 5.50 × 10−10, S5 Fig). For the proportion of naive CD4+ cells that express CD25, the M1 SNP rs41295055 from group D, whose association with CD25 expression was not previously tested, was preferred to the M2 SNP rs2104286 (p = 3.45 × 10−8versusp = 2.56 × 10−6, ΔBIC = 8.43, which is interpreted as “strong” evidence in favour of rs41295055 [24]; Fig 4), supporting the hypothesis that rs2104286 is not itself functional, but merely tags other functional variants which are causal for MS. The A and D SNP groups coincide with regions in IL2RA that contain DNase I sensitivity sites indicating open chromatin available to bind transcription factors under appropriate conditions (Fig 3). In addition, the existence of RNA-seq reads from resting and stimulated CD4+ T cells within intron 1, where group A SNPs lie (Fig 3), support the regulatory nature of this region [25].


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)

The proportion of naive CD4+ T cells that express CD25 (log scale) increases with age.The MS protective allele for the M2 SNP rs41295055:C > T associates with fewer CD4+ T cells expressing CD25 across all ages (p = 3.45 × 10−8), and is statistically preferred to the previously reported M1 SNP, rs2104286:T > C (p = 2.56 × 10−6; Δ BIC = 8.43). S and P are used to represent the (common) MS-susceptible and (rare) MS-protective alleles respectively at each SNP. These SNPs are in limited LD (r2 = 0.3).
© Copyright Policy
Related In: Results  -  Collection

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

pgen.1005272.g004: The proportion of naive CD4+ T cells that express CD25 (log scale) increases with age.The MS protective allele for the M2 SNP rs41295055:C > T associates with fewer CD4+ T cells expressing CD25 across all ages (p = 3.45 × 10−8), and is statistically preferred to the previously reported M1 SNP, rs2104286:T > C (p = 2.56 × 10−6; Δ BIC = 8.43). S and P are used to represent the (common) MS-susceptible and (rare) MS-protective alleles respectively at each SNP. These SNPs are in limited LD (r2 = 0.3).
Mentions: Since we were unable to distinguish between the competing M1 and M2 models for MS using SNP-disease association data alone, we sought corroborating evidence to support SNPs in either model using CD25 flow cytometric expression data. The M2 SNP rs2104286 has been associated with the age-dependent proportion of naïve T cells that express CD25 on their cell surface and rs12722495 (within SNP group A, located in intron 1 of IL2RA) with the total expression of CD25 on memory T cells [19, 20]. rs12722495 has also been associated with IL2RA mRNA expression in T cells, both resting [19] and activated by 48 hour culture with anti-CD3+CD28 [21]. We selected a single tag SNP from each of the credible sets and examined which could best explain CD25 protein expression phenotypes using previously published data from 179 samples [19], plus an additional 30 samples. Both phenotypes were best explained by a single SNP: rs12722495 from group A again showed the strongest association with intensity of CD25 expression on memory T cells (p = 5.50 × 10−10, S5 Fig). For the proportion of naive CD4+ cells that express CD25, the M1 SNP rs41295055 from group D, whose association with CD25 expression was not previously tested, was preferred to the M2 SNP rs2104286 (p = 3.45 × 10−8versusp = 2.56 × 10−6, ΔBIC = 8.43, which is interpreted as “strong” evidence in favour of rs41295055 [24]; Fig 4), supporting the hypothesis that rs2104286 is not itself functional, but merely tags other functional variants which are causal for MS. The A and D SNP groups coincide with regions in IL2RA that contain DNase I sensitivity sites indicating open chromatin available to bind transcription factors under appropriate conditions (Fig 3). In addition, the existence of RNA-seq reads from resting and stimulated CD4+ T cells within intron 1, where group A SNPs lie (Fig 3), support the regulatory nature of this region [25].

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