Non-HLA RA susceptibility SNP allele frequencies and their association with seropositive RA in WTCCC and UKRAGG.

<p>SNPs are ordered by significance (most significant by <i>P</i>_GWAS listed first); all alleles attained genome-wide significance in the published meta-analysis; Ca = Cases; Co = Controls; MAF = Minor Allele Frequency;</p>a<p> = MAF in controls.</p

Risk categorisation of RA and controls by each prediction model.

<p>The y-axis on each graph refers to the proportion of cases/controls in each risk category; cont = controls; sero+ = seropositive RA; ACPA+ = ACPA-positive RA.</p

Clinical characteristics of WTCCC/UKRAGG cases and controls included in modelling.

<p>Data are number (%) unless otherwise stated. The following data are missing from WTCCC: gender in 2 cases and 169 controls; RF status in 5 cases; ACPA status in 290 cases; age of onset missing/inaccurate in 63 cases; erosive status in 96 cases; smoking status in 76 male cases, 204 female cases and 3 female controls. The following data are missing from UKRAGG: gender in 14 controls; RF status in 60 cases; ACPA status in 844 cases; age of onset missing/inaccurate in 93 cases; erosive status in 1,432 cases; nodular status in 378 cases; smoking status in 226 male cases, 513 female cases, 274 male controls and 284 female controls.</p>a<p> = % of males that are ever smokers;</p>b<p> = % of females that are ever smokers.</p

Prediction model generated risk profiles for ACPA-positive RA and controls.

<p>Panel A = WTCCC; Panel B = UKRAGG; the upper set of lines for each model refer to RA cases; the lower set of lines refer to controls; OR = odds ratio.</p

Kaplan-Meier curves: RA age of onset stratified by HLA model risk categorisation and smoking status.

<p>Panel A = WTCCC Curves Stratified By Risk Categorisation; Panel B = UKRAGG Curves Stratified By Risk Categorisation; Panel C = WTCCC Curves Stratified By Risk Categorisation and Ever-Smoking Status; Panel D = UKRAGG Curves Stratified By Risk Categorisation and Ever-Smoking Status; Δ = change in onset age; Δ_m = maximum change in onset age across strata.</p

Number of individuals evaluated in each prediction model.

<p>Number of individuals evaluated in each prediction model.</p

Average χ² statistics for LT versus other approaches in simulated data.

<p>For each statistic we display average results across 1,000,000 simulations, for various effect sizes <i>γ</i>. All statistics are χ²(1 dof). Logistic regression with an interaction term (G+GxE) values been converted from χ²(2 dof) to the equivalent χ²(1 dof) value. At an effect size of 0 all statistics give the expected value under the null. OR LBMI is the odds ratio computed from cases with BMI = 24. OR HBMI is the odds ratio for cases with BMI = 35.</p

Summary statistics across all datasets.

<p>The sum of each of the test statistics across all of the SNPs in each of the diseases. LTPub vs LogR is the % increase of LTPub compared to LogR. It has a median value of 16%. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p

Power calculations for LogR, G+GxE, and LT approaches in simulated data.

<p>For each statistic we display power to attain P<5<b>×</b>10⁻⁸ based on 1,000,000 simulations of 3000 cases and 3000 controls, for various effect sizes <i>γ</i>. The increase in power (ratio of y-axis values) for LT versus LogR is 22.8% for <i>γ</i> = 0.1, and 23.0% when computing average power across all values of <i>γ</i>. For γ = 0 the power was 5.0% for all statistics when the P-value threshold is 0.05. G+GxE performs worse due to an extra degree of freedom.</p

Inferred covariates and effect sizes on the liability scale.

<p>LT model is the liability threshold model for each disease with parameters estimated using the LTPub method. For diseases with multiple covariates, models with all covariates and each covariate separately are given. %Variance Explained is the fraction of variance explained on the liability scale in the study data for each of the covariates in each of the diseases when all covariates are used in the model, and is specific to the distribution of covariates in each particular study. BMI30 is a binary variable, which is 1 if an individual's BMI is greater than 30 and 0 otherwise. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p