Publications

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.

Previous studies have emphasized ethnically heterogeneous human leukocyte antigen (HLA) classical allele associations to rheumatoid arthritis (RA) risk. We fine-mapped RA risk alleles within the major histocompatibility complex (MHC) in 2782 seropositive RA cases and 4315 controls of Asian descent. We applied imputation to determine genotypes for eight class I and II HLA genes to Asian populations for the first time using a newly constructed pan-Asian reference panel. First, we empirically measured high imputation accuracy in Asian samples. Then we observed the most significant association in HLA-DRβ1 at amino acid position 13, located outside the classical shared epitope (Pomnibus = 6.9 × 10(-135)). The individual residues at position 13 have relative effects that are consistent with published effects in European populations (His > Phe > Arg > Tyr ≅ Gly > Ser)–but the observed effects in Asians are generally smaller. Applying stepwise conditional analysis, we identified additional independent associations at positions 57 (conditional Pomnibus = 2.2 × 10(-33)) and 74 (conditional Pomnibus = 1.1 × 10(-8)). Outside of HLA-DRβ1, we observed independent effects for amino acid polymorphisms within HLA-B (Asp9, conditional P = 3.8 × 10(-6)) and HLA-DPβ1 (Phe9, conditional P = 3.0 × 10(-5)) concordant with European populations. Our trans-ethnic HLA fine-mapping study reveals that (i) a common set of amino acid residues confer shared effects in European and Asian populations and (ii) these same effects can explain ethnically heterogeneous classical allelic associations (e.g. HLA-DRB1*09:01) due to allele frequency differences between populations. Our study illustrates the value of high-resolution imputation for fine-mapping causal variants in the MHC.

Psoriasis vulgaris (PsV) risk is strongly associated with variation within the major histocompatibility complex (MHC) region, but its genetic architecture has yet to be fully elucidated. Here, we conducted a large-scale fine-mapping study of PsV risk in the MHC region in 9,247 PsV-affected individuals and 13,589 controls of European descent by imputing class I and II human leukocyte antigen (HLA) genes from SNP genotype data. In addition, we imputed sequence variants for MICA, an MHC HLA-like gene that has been associated with PsV, to evaluate association at that locus as well. We observed that HLA-C(∗)06:02 demonstrated the lowest p value for overall PsV risk (p = 1.7 × 10(-364)). Stepwise analysis revealed multiple HLA-C(∗)06:02-independent risk variants in both class I and class II HLA genes for PsV susceptibility (HLA-C(∗)12:03, HLA-B amino acid positions 67 and 9, HLA-A amino acid position 95, and HLA-DQα1 amino acid position 53; p < 5.0 × 10(-8)), but no apparent risk conferred by MICA. We further evaluated risk of two major clinical subtypes of PsV, psoriatic arthritis (PsA; n = 3,038) and cutaneous psoriasis (PsC; n = 3,098). We found that risk heterogeneity between PsA and PsC might be driven by HLA-B amino acid position 45 (Pomnibus = 2.2 × 10(-11)), indicating that different genetic factors underlie the overall risk of PsV and the risk of specific PsV subphenotypes. Our study illustrates the value of high-resolution HLA and MICA imputation for fine mapping causal variants in the MHC.

MOTIVATION: As disease loci are rapidly discovered, an emerging challenge is to identify common pathways and biological functionality across loci. Such pathways might point to potential disease mechanisms. One strategy is to look for functionally related or interacting genes across genetic loci. Previously, we defined a statistical strategy, Gene Relationships Across Implicated Loci (GRAIL), to identify whether pair-wise gene relationships defined using PubMed text similarity are enriched across loci. Here, we have implemented VIZ-GRAIL, a software tool to display those relationships and to depict the underlying biological patterns.

RESULTS: Our tool can seamlessly interact with the GRAIL web site to obtain the results of analyses and create easy to read visual displays. To most clearly display results, VIZ-GRAIL arranges genes and genetic loci to minimize intersecting pair-wise gene connections. VIZ-GRAIL can be easily applied to other types of functional connections, beyond those from GRAIL. This method should help investigators appreciate the presence of potentially important common functions across loci.

AVAILABILITY: The GRAIL algorithm is implemented online at http://www.broadinstitute.org/mpg/grail/grail.php. VIZ-GRAIL source-code is at http://www.broadinstitute.org/mpg/grail/vizgrail.html.

Advances in genotyping and sequencing technologies have revolutionized the genetics of complex disease by locating rare and common variants that influence an individual's risk for diseases, such as diabetes, cancers, and psychiatric disorders. However, to capitalize on these data for prevention and therapies requires the identification of causal alleles and a mechanistic understanding for how these variants contribute to the disease. After discussing the strategies currently used to map variants for complex diseases, this Primer explores how variants may be prioritized for follow-up functional studies and the challenges and approaches for assessing the contributions of rare and common variants to disease phenotypes.

PURPOSE OF REVIEW: To review the recently discovered genetic risk loci in rheumatoid arthritis (RA), the pathways they implicate, and the genetic architecture of RA.

RECENT FINDINGS: Since 2008 investigators have identified many common genetic variants that confer disease risk through single nucleotide polymorphism genotyping studies; the list of variants will no doubt continue to expand at a rapid rate as genotyping technologies evolve and case-control sample collections continue to grow. In aggregate, these variants implicate pathways leading to NF-kappaB (nuclear factor kappa-light-chain-enhancer of activated B cells) activation, the interluekin-2 signaling pathway, and T-cell activation.

SUMMARY: Although the effect of any individual variant is modest and even in aggregate considerably less than that of the major histocompatability complex, discovery of recent risk variants suggests immunological processes that are involved in disease pathogenesis.