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A Combination of CD28 (rs1980422) and IRF5 (rs10488631) Polymorphisms Is Associated with Seropositivity in Rheumatoid Arthritis: A Case Control Study.

Vernerova L, Spoutil F, Vlcek M, Krskova K, Penesova A, Meskova M, Marko A, Raslova K, Vohnout B, Rovensky J, Killinger Z, Jochmanova I, Lazurova I, Steiner G, Smolen J, Imrich R - PLoS ONE (2016)

Bottom Line: The risk variants of IRF5 and CD28 genes were found to be common determinants for seropositivity in RDA, while positivity of RF alone was associated with the CTLA4 risk variant in heterozygous form.The risk alleles in AFF3 gene together with the presence of ACPA were associated with higher clinical severity of RA.The association among multiple risk variants related to T cell receptor signalling with seropositivity may play an important role in distinct clinical phenotypes of RA.

View Article: PubMed Central - PubMed

Affiliation: Institute of Clinical and Translational Research, Biomedical Centre, Slovak Academy of Sciences, Bratislava, Slovakia.

ABSTRACT

Introduction: The aim of the study was to analyse genetic architecture of RA by utilizing multiparametric statistical methods such as linear discriminant analysis (LDA) and redundancy analysis (RDA).

Methods: A total of 1393 volunteers, 499 patients with RA and 894 healthy controls were included in the study. The presence of shared epitope (SE) in HLA-DRB1 and 11 SNPs (PTPN22 C/T (rs2476601), STAT4 G/T (rs7574865), CTLA4 A/G (rs3087243), TRAF1/C5 A/G (rs3761847), IRF5 T/C (rs10488631), TNFAIP3 C/T (rs5029937), AFF3 A/T (rs11676922), PADI4 C/T (rs2240340), CD28 T/C (rs1980422), CSK G/A (rs34933034) and FCGR3A A/C (rs396991), rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPA) and clinical status was analysed using the LDA and RDA.

Results: HLA-DRB1, PTPN22, STAT4, IRF5 and PADI4 significantly discriminated between RA patients and healthy controls in LDA. The correlation between RA diagnosis and the explanatory variables in the model was 0.328 (Trace = 0.107; F = 13.715; P = 0.0002). The risk variants of IRF5 and CD28 genes were found to be common determinants for seropositivity in RDA, while positivity of RF alone was associated with the CTLA4 risk variant in heterozygous form. The correlation between serologic status and genetic determinants on the 1st ordinal axis was 0.468, and 0.145 on the 2nd one (Trace = 0.179; F = 6.135; P = 0.001). The risk alleles in AFF3 gene together with the presence of ACPA were associated with higher clinical severity of RA.

Conclusions: The association among multiple risk variants related to T cell receptor signalling with seropositivity may play an important role in distinct clinical phenotypes of RA. Our study demonstrates that multiparametric analyses represent a powerful tool for investigation of mutual relationships of potential risk factors in complex diseases such as RA.

No MeSH data available.


Related in: MedlinePlus

The genetic discrimination of RA patients and controls.Linear discrimination analysis diagram shows that shared epitope and single nucleotide polymorphisms in PTPN22, STAT4, IRF5 and PADI4 genes significantly discriminated between RA patients and healthy controls. RA—RA patients; C—control group; SE (0,1,2)—number of SE coding allele in HLA-DRB1 gene (✧); IRF5 (CC, CT, TT)—genotypes in IRF5 gene (C risk allele) (◁); PADI4 (TT, CT, CC)–genotypes in PADI4 gene (T risk allele) (▽); PTPN22 (CC, CT, TT)–genotypes in PTPN22 gene (A risk allele) (△); STAT4 (GG, GT, TT)–genotypes in STAT4 gene (T risk allele) (☐). Diagram reading clue: Small circles represent individual cases. Large grey circles—centroids—represent subject groups (RA patients and controls). Symbols are genetic factors. Large bold symbols represent genotypes significantly influencing the distribution of subjects. Small empty symbols represent other genotypes of selected genes. The closer to the group centroid the gene symbol lies, the stronger is its impact on the classification of subjects to particular group.
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pone.0153316.g001: The genetic discrimination of RA patients and controls.Linear discrimination analysis diagram shows that shared epitope and single nucleotide polymorphisms in PTPN22, STAT4, IRF5 and PADI4 genes significantly discriminated between RA patients and healthy controls. RA—RA patients; C—control group; SE (0,1,2)—number of SE coding allele in HLA-DRB1 gene (✧); IRF5 (CC, CT, TT)—genotypes in IRF5 gene (C risk allele) (◁); PADI4 (TT, CT, CC)–genotypes in PADI4 gene (T risk allele) (▽); PTPN22 (CC, CT, TT)–genotypes in PTPN22 gene (A risk allele) (△); STAT4 (GG, GT, TT)–genotypes in STAT4 gene (T risk allele) (☐). Diagram reading clue: Small circles represent individual cases. Large grey circles—centroids—represent subject groups (RA patients and controls). Symbols are genetic factors. Large bold symbols represent genotypes significantly influencing the distribution of subjects. Small empty symbols represent other genotypes of selected genes. The closer to the group centroid the gene symbol lies, the stronger is its impact on the classification of subjects to particular group.

Mentions: All genes analysed in the study were in Hardy-Weinberg equilibrium. The presence of SE coding alleles in HLA-DRB1 gene was significantly higher in RA patients compared to controls (Table 2). The most frequent SE alleles in patient group were DRB1*01:01 (13.4%), DRB1*04:01 (11.9%), DRB1*04:04 (3.8%) (data not shown). Among 11 studied SNPs, risk allele and genotype frequencies of PTPN22, STAT4, IRF5 and PADI4 genes were significantly higher in RA patients compared to controls (Table 2). LDA model was used to classify our cohort based on the genotype frequencies of studied SNPs. A major discriminant function was generated based on the variances seen in the data using LDA. This discriminant function was assigned to 1st ordinal axis of LDA diagram and the centroids for diagnosis were plotted in this matrix together with the significant genetic determinants (Fig 1). Based on these discriminant functions, the model predicted to which of the study group individual subject belongs with an overall accuracy of 10.7%. The correlation coefficient between RA diagnosis and explanatory variables in the model was 0.328 (Trace = 0.107; F = 13.715; P = 0.0002). These genetic determinants were selected as significant in the following order representing their importance in the model: SE 0 > IRF5 TT > SE 1 + SE 2 > STAT4 GG > PADI4 CC + PADI4 TT > PTPN22 CC.


A Combination of CD28 (rs1980422) and IRF5 (rs10488631) Polymorphisms Is Associated with Seropositivity in Rheumatoid Arthritis: A Case Control Study.

Vernerova L, Spoutil F, Vlcek M, Krskova K, Penesova A, Meskova M, Marko A, Raslova K, Vohnout B, Rovensky J, Killinger Z, Jochmanova I, Lazurova I, Steiner G, Smolen J, Imrich R - PLoS ONE (2016)

The genetic discrimination of RA patients and controls.Linear discrimination analysis diagram shows that shared epitope and single nucleotide polymorphisms in PTPN22, STAT4, IRF5 and PADI4 genes significantly discriminated between RA patients and healthy controls. RA—RA patients; C—control group; SE (0,1,2)—number of SE coding allele in HLA-DRB1 gene (✧); IRF5 (CC, CT, TT)—genotypes in IRF5 gene (C risk allele) (◁); PADI4 (TT, CT, CC)–genotypes in PADI4 gene (T risk allele) (▽); PTPN22 (CC, CT, TT)–genotypes in PTPN22 gene (A risk allele) (△); STAT4 (GG, GT, TT)–genotypes in STAT4 gene (T risk allele) (☐). Diagram reading clue: Small circles represent individual cases. Large grey circles—centroids—represent subject groups (RA patients and controls). Symbols are genetic factors. Large bold symbols represent genotypes significantly influencing the distribution of subjects. Small empty symbols represent other genotypes of selected genes. The closer to the group centroid the gene symbol lies, the stronger is its impact on the classification of subjects to particular group.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836711&req=5

pone.0153316.g001: The genetic discrimination of RA patients and controls.Linear discrimination analysis diagram shows that shared epitope and single nucleotide polymorphisms in PTPN22, STAT4, IRF5 and PADI4 genes significantly discriminated between RA patients and healthy controls. RA—RA patients; C—control group; SE (0,1,2)—number of SE coding allele in HLA-DRB1 gene (✧); IRF5 (CC, CT, TT)—genotypes in IRF5 gene (C risk allele) (◁); PADI4 (TT, CT, CC)–genotypes in PADI4 gene (T risk allele) (▽); PTPN22 (CC, CT, TT)–genotypes in PTPN22 gene (A risk allele) (△); STAT4 (GG, GT, TT)–genotypes in STAT4 gene (T risk allele) (☐). Diagram reading clue: Small circles represent individual cases. Large grey circles—centroids—represent subject groups (RA patients and controls). Symbols are genetic factors. Large bold symbols represent genotypes significantly influencing the distribution of subjects. Small empty symbols represent other genotypes of selected genes. The closer to the group centroid the gene symbol lies, the stronger is its impact on the classification of subjects to particular group.
Mentions: All genes analysed in the study were in Hardy-Weinberg equilibrium. The presence of SE coding alleles in HLA-DRB1 gene was significantly higher in RA patients compared to controls (Table 2). The most frequent SE alleles in patient group were DRB1*01:01 (13.4%), DRB1*04:01 (11.9%), DRB1*04:04 (3.8%) (data not shown). Among 11 studied SNPs, risk allele and genotype frequencies of PTPN22, STAT4, IRF5 and PADI4 genes were significantly higher in RA patients compared to controls (Table 2). LDA model was used to classify our cohort based on the genotype frequencies of studied SNPs. A major discriminant function was generated based on the variances seen in the data using LDA. This discriminant function was assigned to 1st ordinal axis of LDA diagram and the centroids for diagnosis were plotted in this matrix together with the significant genetic determinants (Fig 1). Based on these discriminant functions, the model predicted to which of the study group individual subject belongs with an overall accuracy of 10.7%. The correlation coefficient between RA diagnosis and explanatory variables in the model was 0.328 (Trace = 0.107; F = 13.715; P = 0.0002). These genetic determinants were selected as significant in the following order representing their importance in the model: SE 0 > IRF5 TT > SE 1 + SE 2 > STAT4 GG > PADI4 CC + PADI4 TT > PTPN22 CC.

Bottom Line: The risk variants of IRF5 and CD28 genes were found to be common determinants for seropositivity in RDA, while positivity of RF alone was associated with the CTLA4 risk variant in heterozygous form.The risk alleles in AFF3 gene together with the presence of ACPA were associated with higher clinical severity of RA.The association among multiple risk variants related to T cell receptor signalling with seropositivity may play an important role in distinct clinical phenotypes of RA.

View Article: PubMed Central - PubMed

Affiliation: Institute of Clinical and Translational Research, Biomedical Centre, Slovak Academy of Sciences, Bratislava, Slovakia.

ABSTRACT

Introduction: The aim of the study was to analyse genetic architecture of RA by utilizing multiparametric statistical methods such as linear discriminant analysis (LDA) and redundancy analysis (RDA).

Methods: A total of 1393 volunteers, 499 patients with RA and 894 healthy controls were included in the study. The presence of shared epitope (SE) in HLA-DRB1 and 11 SNPs (PTPN22 C/T (rs2476601), STAT4 G/T (rs7574865), CTLA4 A/G (rs3087243), TRAF1/C5 A/G (rs3761847), IRF5 T/C (rs10488631), TNFAIP3 C/T (rs5029937), AFF3 A/T (rs11676922), PADI4 C/T (rs2240340), CD28 T/C (rs1980422), CSK G/A (rs34933034) and FCGR3A A/C (rs396991), rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPA) and clinical status was analysed using the LDA and RDA.

Results: HLA-DRB1, PTPN22, STAT4, IRF5 and PADI4 significantly discriminated between RA patients and healthy controls in LDA. The correlation between RA diagnosis and the explanatory variables in the model was 0.328 (Trace = 0.107; F = 13.715; P = 0.0002). The risk variants of IRF5 and CD28 genes were found to be common determinants for seropositivity in RDA, while positivity of RF alone was associated with the CTLA4 risk variant in heterozygous form. The correlation between serologic status and genetic determinants on the 1st ordinal axis was 0.468, and 0.145 on the 2nd one (Trace = 0.179; F = 6.135; P = 0.001). The risk alleles in AFF3 gene together with the presence of ACPA were associated with higher clinical severity of RA.

Conclusions: The association among multiple risk variants related to T cell receptor signalling with seropositivity may play an important role in distinct clinical phenotypes of RA. Our study demonstrates that multiparametric analyses represent a powerful tool for investigation of mutual relationships of potential risk factors in complex diseases such as RA.

No MeSH data available.


Related in: MedlinePlus