Fine mapping seronegative and seropositive rheumatoid arthritis to shared and distinct HLA alleles by adjusting for the effects of heterogeneity

Abstract

Despite progress in defining human leukocyte antigen (HLA) alleles for anti-citrullinated-protein-autoantibody-positive (ACPA(+)) rheumatoid arthritis (RA), identifying HLA alleles for ACPA-negative (ACPA(-)) RA has been challenging because of clinical heterogeneity within clinical cohorts. We imputed 8,961 classical HLA alleles, amino acids, and SNPs from Immunochip data in a discovery set of 2,406 ACPA(-) RA case and 13,930 control individuals. We developed a statistical approach to identify and adjust for clinical heterogeneity within ACPA(-) RA and observed independent associations for serine and leucine at position 11 in HLA-DRβ1 (p = 1.4 × 10(-13), odds ratio [OR] = 1.30) and for aspartate at position 9 in HLA-B (p = 2.7 × 10(-12), OR = 1.39) within the peptide binding grooves. These amino acid positions induced associations at HLA-DRB1(∗)03 (encoding serine at 11) and HLA-B(∗)08 (encoding aspartate at 9). We validated these findings in an independent set of 427 ACPA(-) case subjects, carefully phenotyped with a highly sensitive ACPA assay, and 1,691 control subjects (HLA-DRβ1 Ser11+Leu11: p = 5.8 × 10(-4), OR = 1.28; HLA-B Asp9: p = 2.6 × 10(-3), OR = 1.34). Although both amino acid sites drove risk of ACPA(+) and ACPA(-) disease, the effects of individual residues at HLA-DRβ1 position 11 were distinct (p < 2.9 × 10(-107)). We also identified an association with ACPA(+) RA at HLA-A position 77 (p = 2.7 × 10(-8), OR = 0.85) in 7,279 ACPA(+) RA case and 15,870 control subjects. These results contribute to mounting evidence that ACPA(+) and ACPA(-) RA are genetically distinct and potentially have separate autoantigens contributing to pathogenesis. We expect that our approach might have broad applications in analyzing clinical conditions with heterogeneity at both major histocompatibility complex (MHC) and non-MHC regions.

Figure 1

Association Results within the MHC to ACPA⁻ RA (A) We observed the most significant association at position 11 of HLA-DRβ1 (encoded by HLA-DRB1), where Ser and Leu conferred risk (red diamond). We also observed an independent association at SNP rs9266669, which was statistically indistinguishable from HLA-B Asp9 (green diamond). The dark-red and dark-green squares denote the statistical significance of the two positions in a joint analysis including both discovery and replication data. (B) Conditioning on HLA-DRβ1 Ser11+Leu11, we found that the association at rs9266669 remained the most significant. (C) Conditioning on HLA-B Asp9, we found that the association at HLA-DRβ1 Ser11+Leu11 remained the most significant. (D) Conditioning on both HLA-DRβ1 Ser11+Leu11 and HLA-B Asp9, we did not observe any more statistically significant association within MHC (p > 0.0007).

Figure 2

3D Models of Amino Acid Positions Identified in This Study Key amino acid positions are highlighted as spheres. We used Protein Data Bank entries 3pdo (HLA-DR), 2bvp (HLA-B), and 1x7q (HLA-A) with UCSF Chimera to prepare the figure. See Figure S5 for all known associated positions.

Figure 3

Distinct Effect Sizes of Amino Acid Residues at HLA-DRβ1 Position 11 for ACPA⁻ and ACPA⁺ RA For each residue, we show the univariate OR (OR with respect to the other residues as a reference) and the 95% confidence interval. Effect sizes were distinct between the two disease subsets (p < 2.9 × 10⁻¹⁰⁷).