The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.

Five years of GWAS discovery

A new model for an etiology of rheumatoid arthritis : smoking may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified by citrullination.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/art.21575

A new model for an etiology of rheumatoid arthritis: smoking may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified by citrullination.

An editorial in this issue explains that the degree of biological interaction between risk factors is measured as the deviation from additivity by the corresponding disease rates and not for example as deviation from multiplicativity. It is the purpose of this article to describe how a logistic regression model, or a Cox regression model, can be defined in order to produce the output that is needed for assessment of biological interaction. We will also demonstrate how common software can be programmed to deliver this output. Finally, we show how this output can be used as input in an Excel sheet that is set up to calculate the measures of biological interaction to be used for the assessment.

Calculating measures of biological interaction.

A SNP haplotype in the third intron of STAT4 was associated with susceptibility to both rheumatoid arthritis and systemic lupus erythematosus. The minor alleles of the haplotype-defining SNPs were present in 27% of chromosomes of patients with established rheumatoid arthritis, as compared with 22% of those of controls (for the SNP rs7574865, P = 2.81×10 −7 ; odds ratio for having the risk allele in chromosomes of patients vs. those of controls, 1.32). The association was replicated in Swedish patients with recent-onset rheumatoid arthritis (P = 0.02) and matched controls. The haplotype marked by rs7574865 was strongly associated with lupus, being present on 31% of chromosomes of case patients and 22% of those of controls (P = 1.87×10 −9 ; odds ratio for having the risk allele in chromosomes of patients vs. those of controls, 1.55). Homozygosity of the risk allele, as compared with absence of the allele, was associated with a more than doubled risk for lupus and a 60% increased risk for rheumatoid arthritis. CONCLUSIONS

https://www.nejm.org/doi/pdf/10.1056/NEJMoa073003

STAT4 and the Risk of Rheumatoid Arthritis and Systemic Lupus Erythematosus

Epigenetic mechanisms integrate genetic and environmental causes of disease, but comprehensive genome-wide analyses of epigenetic modifications have not yet demonstrated robust association with common diseases. Using Illumina HumanMethylation450 arrays on 354 anti-citrullinated protein antibody-associated rheumatoid arthritis cases and 337 controls, we identified two clusters within the major histocompatibility complex (MHC) region whose differential methylation potentially mediates genetic risk for rheumatoid arthritis. To reduce confounding factors that have hampered previous epigenome-wide studies, we corrected for cellular heterogeneity by estimating and adjusting for cell-type proportions in our blood-derived DNA samples and used mediation analysis to filter out associations likely to be a consequence of disease. Four CpGs also showed an association between genotype and variance of methylation. The associations for both clusters replicated at least one CpG (P < 0.01), with the rest showing suggestive association, in monocyte cell fractions in an independent cohort of 12 cases and 12 controls. Thus, DNA methylation is a potential mediator of genetic risk.

/pdf/epigenome-wide-association-data-implicate-dna-methylation-as-3sn5oxmq7v.pdf

Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis

Objective
The main genetic risk factor for rheumatoid arthritis (RA) is the shared epitope (SE) of HLA–DR, while smoking is an important environmental risk factor. We studied a potential gene–environment interaction between SE genes and smoking in the etiology of the 2 major subgroups of RA: rheumatoid factor (RF)–seropositive and RF-seronegative disease.

Methods
A population-based case–control study involving incident cases of RF-seropositive and RF-seronegative RA (858 cases and 1,048 controls) was performed in Sweden. Cases and controls were classified according to their cigarette smoking status and HLA–DRB1 genotypes. The relative risk of developing RA was calculated for different gene/smoking combinations and was compared with the relative risk in never smokers without SE genes.

Results
The relative risk of RF-seropositive RA was 2.8 (95% confidence interval [95% CI] 1.6–4.8) in never smokers with SE genes, 2.4 (95% CI 1.3–4.6) in current smokers without SE genes, and 7.5 (95% CI 4.2–13.1) in current smokers with SE genes. Smokers carrying double SE genes displayed a relative risk of RF-seropositive RA of 15.7 (95% CI 7.2–34.2). The interaction between smoking and SE genes was significant, as measured by the attributable proportion due to interaction, which was 0.4 (95% CI 0.2–0.7) for smoking and any SE, and 0.6 (95% CI 0.4–0.9) for smoking and a double SE. Neither smoking nor SE genes nor the combination of these factors increased the risk of developing RF-seronegative RA.

Conclusion
The disease risk of RF-seropositive RA associated with one of the classic genetic risk factors for immune-mediated diseases (the SE of HLA–DR) is strongly influenced by the presence of an environmental factor (smoking) in the population at risk.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/art.20553

Camilla Silva

Papers

A gene-environment interaction between smoking and shared epitope genes in HLA-DR provides a high risk of seropositive rheumatoid arthritis