Genome-wide assessment of genetic risk for systemic lupus erythematosus and disease severity

Chen, Lingyan; Wang, Yongfei; Liu, Lu; Bielowka, Adrianna; Ahmed, Rahell; Zhang, Huoru; Tombleson, Phil; Roberts, Amy L.; Odhams, Christopher A.; Graham, Deborah S. Cunninghame; Zhang, Xuejun; Yang, Wu; Vyse, Timothy J.; Morris, David L.

doi:10.1093/hmg/ddaa030

Cited by 69 publications

(64 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genetic information may also be used to predict how severe the disease will be and identify patients that have an increased risk for certain organ manifestations. The genetic information can be leveraged by the use of polygenetic risk scores that measures the cumulative effects of a large number of individual SLE risk variants 99‐101 . Such risk scores can identify patients at increased risk for renal disorders, cardiovascular events and decreased survival 99,100 .…”

Section: Discussionmentioning

confidence: 99%

Immunogenetics in systemic lupus erythematosus: Transitioning from genetic associations to cellular effects

Lundtoft

Rönnblom

2020

Scand J Immunol

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Immunogenetics in systemic lupus erythematosus: Transitioning from genetic associations to cellular effects

Lundtoft

Rönnblom

2020

Scand J Immunol

View full text Add to dashboard Cite

show abstract

“…In addition to comorbidities with other IMIDs, there is great variability in the disease course followed by patients with SSc, since their treatment and prognosis in the long term is very heterogeneous. 20 Chen et al 17 developed a GRS based on a GWAS analysing patients with SLE with and without renal involvement, but this SLE nephritis-specific GRS did not outperform the SLE severity predictions achieved with a SLE GRS. Following a similar strategy, we generated two additional GRS based on the GWAS comparisons between clinical and serological subtypes in patients with SSc.…”

Section: Discussionmentioning

confidence: 99%

“…This lack of significant contribution of sex to the GRS model was also reported previously in SLE. 17 Therefore, these counterintuitive results for a known SSc risk factor 19 were likely due to the selection of a sex-matched control population (online supplemental table 1), which would rule out the relevance of this parameter. The immune cell types included in the multivariate GRS were also concordant with the known aetiopathogenesis of the disease.…”

Section: Discussionmentioning

confidence: 99%

Genomic Risk Score impact on susceptibility to systemic sclerosis

et al. 2020

View full text Add to dashboard Cite

ObjectivesGenomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those individuals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time.MethodsAllelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model.ResultsThe best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren’s syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC<0.5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693.ConclusionsGRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.

show abstract

“…In this gene–environment interaction study of SLE risk, the largest conducted to date and the first, to our knowledge, to employ GWAS results rather than to investigate individual candidate polymorphisms, we created an updated, weighted GRS based on existing GRS studies of SLE (28,29), and tested for interactions between SLE genetic factors and smoking status in relation to their potential influence on SLE risk. We found that having a high wGRS and being a current or recent smoker were each individually strongly associated with SLE risk: each standard deviation increase in the wGRS more than doubled the risk of SLE, while current smokers and those who had recently quit smoking had an increased SLE risk of ~50% compared to never and more distant past smokers.…”

Section: Discussionmentioning

confidence: 99%

Interactions Between Genome‐Wide Genetic Factors and Smoking Influencing Risk of Systemic Lupus Erythematosus

Cui

Raychaudhuri

Karlson

et al. 2020

Arthritis & Rheumatology

View full text Add to dashboard Cite

Objective To identify interactions between genetic factors and current or recent smoking in relation to risk of developing systemic lupus erythematosus (SLE). Methods For the study, 673 patients with SLE (diagnosed according to the American College of Rheumatology 1997 updated classification criteria) were matched by age, sex, and race (first 3 genetic principal components) to 3,272 control subjects without a history of connective tissue disease. Smoking status was classified as current smoking/having recently quit smoking within 4 years before diagnosis (or matched index date for controls) versus distant past/never smoking. In total, 86 single‐nucleotide polymorphisms and 10 classic HLA alleles previously associated with SLE were included in a weighted genetic risk score (wGRS), with scores dichotomized as either low or high based on the median value in control subjects (low wGRS being defined as less than or equal to the control median; high wGRS being defined as greater than the control median). Conditional logistic regression models were used to estimate both the risk of SLE and risk of anti–double‐stranded DNA autoantibody–positive (dsDNA+) SLE. Additive interactions were assessed using the attributable proportion (AP) due to interaction, and multiplicative interactions were assessed using a chi‐square test (with 1 degree of freedom) for the wGRS and for individual risk alleles. Separate repeated analyses were carried out among subjects of European ancestry only. Results The mean ± SD age of the SLE patients at the time of diagnosis was 36.4 ± 15.3 years. Among the 673 SLE patients included, 92.3% were female and 59.3% were dsDNA+. Ethnic distributions were as follows: 75.6% of European ancestry, 4.5% of Asian ancestry, 11.7% of African ancestry, and 8.2% classified as other ancestry. A high wGRS (odds ratio [OR] 2.0, P = 1.0 × 10−51 versus low wGRS) and a status of current/recent smoking (OR 1.5, P = 0.0003 versus distant past/never smoking) were strongly associated with SLE risk, with significant additive interaction (AP 0.33, P = 0.0012), and associations with the risk of anti‐dsDNA+ SLE were even stronger. No significant multiplicative interactions with the total wGRS (P = 0.58) or with the HLA‐only wGRS (P = 0.06) were found. Findings were similar in analyses restricted to only subjects of European ancestry. Conclusion The strong additive interaction between an updated SLE genetic risk score and current/recent smoking suggests that smoking may influence specific genes in the pathogenesis of SLE.

show abstract

Genome-wide assessment of genetic risk for systemic lupus erythematosus and disease severity

Cited by 69 publications

References 44 publications

Immunogenetics in systemic lupus erythematosus: Transitioning from genetic associations to cellular effects

Immunogenetics in systemic lupus erythematosus: Transitioning from genetic associations to cellular effects

Genomic Risk Score impact on susceptibility to systemic sclerosis

Interactions Between Genome‐Wide Genetic Factors and Smoking Influencing Risk of Systemic Lupus Erythematosus

Contact Info

Product

Resources

About