To identify novel genetic risk factors for rheumatoid arthritis (RA), we conducted a genome-wide association study (GWAS) meta-analysis of 5,539 autoantibody positive RA cases and 20,169 controls of European descent, followed by replication in an independent set of 6,768 RA cases and 8,806 controls. Of 34 SNPs selected for replication, 7 novel RA risk alleles were identified at genome-wide significance (P<5×10−8) in analysis of all 41,282 samples. The associated SNPs are near genes of known immune function, including IL6ST, SPRED2, RBPJ, CCR6, IRF5, and PXK. We also refined the risk alleles at two established RA risk loci (IL2RA and CCL21) and confirmed the association at AFF3. These new associations bring the total number of confirmed RA risk loci to 31 among individuals of European ancestry. An additional 11 SNPs replicated at P<0.05, many of which are validated autoimmune risk alleles, suggesting that most represent bona fide RA risk alleles.

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to play an important role in genetic susceptibility to common disease. To address this we undertook a large direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed ~19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated ~50% of all common CNVs larger than 500bp. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell-lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease, IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis, and type 1 diabetes, and TSPAN8 for type 2 diabetes, though in each case the locus had previously been identified in SNP-based studies, reflecting our observation that the majority of common CNVs which are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs which can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.

To discover novel RA risk loci, we systematically examined 370 SNPs from 179 independent loci with p<0.001 in a published meta-analysis of RA GWAS of 3,393 cases and 12,462 controls1. We used GRAIL2, a computational method that applies statistical text mining to PubMed abstracts, to score these 179 loci for functional relationships to genes in 16 established RA disease loci1,3-11. We identified 22 loci with a significant degree of functional connectivity. We genotyped 22 representative SNPs in an independent set of 7,957 cases and 11,958 matched controls. Three validate convincingly: CD2/CD58 (rs11586238, p=1×10−6 replication, p=1×10−9 overall), and CD28 (rs1980422, p=5×10−6 replication, p=1×10−9 overall), PRDM1 (rs548234, p=1×10−5 replication, p=2×10−8 overall). An additional four replicate (p<0.0023): TAGAP (rs394581, p=0.0002 replication, p=4×10−7 overall), PTPRC (rs10919563, p=0.0003 replication, p=7×10−7 overall), TRAF6/RAG1 (rs540386, p=0.0008 replication, p=4×10−6 overall), and FCGR2A (rs12746613, p=0.0022 replication, p=2×10−5 overall). Many of these loci are also associated to other immunologic diseases.

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.

ObjectivesTo investigate a shared genetic aetiology for skin involvement in psoriasis and psoriatic arthritis (PsA) by genotyping single-nucleotide polymorphisms (SNPs), reported to be associated in genome-wide association studies of psoriasis, in patients with PsA.MethodsSNPs with reported evidence for association with psoriasis were genotyped in a PsA case and control collection from the UK and Ireland. Genotype and allele frequencies were compared between PsA cases and controls using the Armitage test for trend.ResultsSeven SNPs mapping to the IL1RN, TNIP1, TNFAIP3, TSC1, IL23A, SMARCA4 and RNF114 genes were successfully genotyped. The IL23A and TNIP1 genes showed convincing evidence for association (rs2066808, p = 9.1×10−7; rs17728338, p = 3.5×10−5, respectively) whilst SNPs mapping to the TNFAIP3, TSC1 and RNF114 genes showed nominal evidence for association (rs610604, p = 0.03; rs1076160, p = 0.03; rs495337, p = 0.0025). No evidence for association with IL1RN or SMARCA4 was found but the power to detect association was low.ConclusionsSNPs mapping to previously reported psoriasis loci show evidence for association to PSA, thus supporting the hypothesis that the genetic aetiology of skin involvement is the same in both PsA and psoriasis.