Predicting The Risk Of Rheumatoid Arthritis

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1 Predicting The Risk Of Rheumatoid Arthritis Modelling Genetic And Environmental Risk Factors Ian Scott Arthritis Research UK Clinical Research Fellow Declaration Of Interests: No Competing Interests

2 Describe our risk prediction model for RA Combines genetic and environment risk factors Classifies individuals risk of developing RA Outline its mathematical basis Describe genetic/environmental components Show its predictive value in large datasets UKRAG Consortium WTCCC Summary

3 Mathematical Basis Of Prediction Model Modelling performed within the R package REGENT Combine relative risks (RRs) for each risk factor Generate individuals from the population Randomly with different combinations of risk factors RR confidence intervals (CIs) simulated for each individual Average individual identified with RR of 1 Individuals placed into 4 RA risk categories based around this average individual s 95% CIs Reduced, Average, Elevated, High Crouch et al, European Journal Human Genetics, 2012

4 Example Of Risk Categorisation Using 3 SNPs Elevated risk Average risk Average risk individual RR of 1 95% CI Low risk individual Upper 95% CI of <0.85 Reduced Risk Elevated risk individual Lower 95% CI >1.15

5 Non-HLA Genetic Component of Model 10 Most Significant Non-HLA SNPs From Meta-Analysis SNP ID Meta-Analysis (5539 cases, controls) UKRAG Dataset (1758 cases, 1527 controls) MAF RR p-value MAF RR P-value rs x x rs x x rs x rs x rs x rs x rs x rs x rs x rs x Stahl et al, Nature Genetics, 2010

6 HLA Genetic Component Of Model Shared Epitope Shared Epitope (SE) alleles defined as being DRB1*0401,*0404,*0405,*0101,*0102,*1402,*1001 Risks for SE derived from existing cohorts EIRA, CACORA,UKRAG, Nurses Health Study Random effects model estimates risks 1 SE allele OR 3.27 (95% CI ) 2 SE alleles OR 10.1 (95% CI )

7 Environmental Component Smoking As An Independent Risk One model uses smoking as an independent risk factor from the SE Odds Ratios obtained from the 2010 smoking meta-analysis OR for RF+ RA in current smokers 1.64 (95% CI ) OR for RF+ RA in ex-smokers 1.55 (95% CI ) Sugiyama et al, Ann Rheum Dis, 2010

Environmental Component Smoking-Shared Epitope Interaction Summary risks estimated using a random effects model from the EIRA and CACORA studies SE Copies Risk in Never Smokers OR (95% CI) Risk In

8 Environmental Component Smoking-Shared Epitope Interaction Summary risks estimated using a random effects model from the EIRA and CACORA studies SE Copies Risk in Never Smokers OR (95% CI) Risk In Ever Smokers OR (95% CI) Odds Ratios For RA With Different Combinations Of SE and Smoking (ref) 1.4 ( ) ( ) 6.1 ( ) ( ) 32.7 ( ) 0 SE 1 SE Never Smoker 2 SE Ever Smoker

9 4 Different Prediction Models 4 prediction models tested in the UKRAG dataset SNP SNP and SE SNP, SE and smoking as independent risk factors SNP, SE-smoking accounting for the gene-environment interaction

10 Results For The 4 Models In UKRAG Analysis for ACPA Positive RA Risk Category 10 SNPs 10 SNPS and SE SNP/SE/Smoking SNP/SE-Smoking % Control % RA % Control % RA % Control % RA % Control % RA Reduced Average Elevated High

11 Risk Differences Between The 4 Models SE accounts for most prediction The Smoking-SE interaction discriminates better for high risk cases vs. controls % Individuals With RR More Than % Controls % ACPA+RA SNP-SE SNP-SE-Smoking

12 Receiver Operating Characteristic Curves 3 Models Evaluating APCA-Positive RA Risk ROC Curves Area Under The Curve 10 SNP model SNP 10 SNP and SE 10 SNP, SE and smoking 10 SNP/SE model SNP/SE-smoking model 0.78

13 Prediction Of Lifetime RA Risk Lifetime risk of RF-positive RA in the USA 2.4% for women 1.1% for men Using the 10 SNP and SE model in UKRAG controls 8% have a RR >3 and a lifetime risk (if female) of >7% 1% have a RR >6 and a lifetime risk (if female) of >14% The highest risk individual is male and has a RR of 10.6 His lifetime risk is 11.6% Therefore a relatively small group of people are at a high risk of RA Crowson et al, Arth Rheum, 2012

14 WTCCC Results 10 SNP-SE Model 60 Risk Categorisation ROC Curves SNP- AUC Reduced Average Increased Controls ACPA Positive RA 10 SNP and SE- AUC 0.78

15 The Addition Of More Non-HLA SNPs To The Prediction Model 10 SNP Model 31 SNP Model The addition of more low risk SNPs Increases the size of the average risk group Decreases the model s predictive ability

16 Our prediction model discriminates sero-positive RA from controls Unlike other prediction models estimates precision of individuals risks using CIs Only a minority have significantly increased RA risk Most risk is from the shared epitope Further research needed Conclusions to better define RA risks to identify risk factors with large odds ratios and narrow CIs to improve the models accuracy

17 Acknowledgements Supervisors Cathryn Lewis Sophia Steer Andrew Cope SGU Dan Crouch Graham Goddard Rachael Tan Dataset Providers UKRAG Consortium WTCCC Funder Arthritis Research UK

How will we prevent rheumatoid arthritis? Kevin Deane, MD/PhD Division of Rheumatology University of Colorado

How will we prevent rheumatoid arthritis? Kevin Deane, MD/PhD Division of Rheumatology University of Colorado Outline 1) Brief overview of natural history of RA and how current understanding of disease