Additional file 1 of Alzheimer’s disease-associated complement gene variants influence plasma complement protein levels
Creators: name: Veteleanu, Aurora
name: Stevenson-Hoare, Joshua
name: Keat, Samuel
name: Daskoulidou, Nikoleta
name: Zetterberg, Henrik
name: Heslegrave, Amanda
name: Escott-Price, Valentina
name: Williams, Julie
name: Sims, Rebecca
name: Zelek, Wioleta M.
name: Carpanini, Sarah M.
name: Morgan, Bryan Paul
Publisher: name: figshare
Year: 2023
Subjects: subject: Biochemistry
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Developmental Biology
subject: Inorganic Chemistry
subject: FOS: Chemical sciences
subject: Hematology
subject: Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Table S1. Antibodies, proteins, and sample dilutions used in ELISA. All antibodies and standard proteins were produced in-house unless otherwise specified for each analyte. Comptech: Complement Technology, Inc. Abbreviations: mAb = monoclonal antibody, CV = coefficient of variation. Table S2. Pearson correlation scores between complement proteins and AT(N) biomarkers in control (A) and case samples (B). The top right triangle shows Pearson r correlation scores, while the bottom left triangle shows p values (italics). Table S3. Medians, Inter-Quartile Ranges (IQR) and Wilcoxon Rank Sum (Mann Whitney U) Test p-values for comparing distributions of plasma protein levels in early-onset Alzheimer’s disease (EOAD) vs. late-onset Alzheimer’s disease (LOAD) (* = p&lt;0.05; ** = p&lt;0.01; *** = p&lt;0.001). Table S4. ROC-AUC statistics of stepAIC informed GLMs predicting disease status from plasma protein levels, adjusted for age and sex. Each stepAIC informed GLM model (Model), AUC values (AUC), Akaike Information Criterion (AIC), 95% confidence intervals (generated from 2000 bootstrap replicates) (CI), Z scores (Z), standard error (SE) and p-values (p-value) are given for each GLM and resultant ROC curve for predicting EOAD and LOAD status against controls. Table S5. Receiver-operator characteristic area under the curve (ROC-AUC) statistics for three major confounders (age, sex and APOE status), plus each protein biomarker, in generalised linear models (GLMs) to predict AD status and results of DeLong tests, comparing GLMs containing the three confounders and GLMs containing the three confounders plus each protein biomarker. Results are for Controls vs EOAD, Controls vs LOAD and EOAD vs LOAD, and each column contains the GLMs (Model), AUC of the resultant ROC curves, 95% confidence intervals (generated from 2000 bootstrap replicates) for the ROC-AUC (CI), Z-scores for the ROC-AUC (Z), standard error for the ROC-AUC (SE), p-values for the ROC-AUC (p-value) and confidence intervals, Z-scores and p-values from the results of the DeLong test (CI Delong, Z Delong, p-value Delong). Table S6. ROC-AUC statistics of GLMs predicting disease status from plasma protein levels, unadjusted for age and sex. For each protein, AUC values (AUC), 95% confidence intervals (from 2000 bootstrap replicates) (CI), Z scores (Z), standard error (SE) and p-values (p-value) are given for predicting EOAD and LOAD status vs controls, and EOAD vs LOAD. Table S7. ROC-AUC statistics of GLMs predicting disease status from plasma protein levels, adjusted for age and sex. For each protein, AUC values (AUC), 95% confidence intervals (from 2000 bootstrap replicates) (CI), Z scores (Z), standard error (SE) and p-values (p-value) are given for predicting EOAD and LOAD status vs controls, and EOAD vs LOAD. Table S8. Results of Generalised Linear Models (GLMs) estimating protein levels from Age, Sex and an interaction term between phenotype (Control vs EOAD, Control vs LOAD, Control vs AD) and certain genotypes (SNP rsIDs). Estimate values for each element (Estimate), the standard error (Std Error), the t-value statistic (t-value), significance thresholds (Pr(&gt;|t|)) and formula used to create each GLM. Figure S1. Forest plot of regression coefficients (estimate values) of the generalised linear models (A = early-onset Alzheimer’s disease (EOAD), B = late-onset Alzheimer’s disease (LOAD)), showing the direction and strength of effect for ATN biomarkers and complement proteins measured for each AD group vs. controls. Proteins with negative coefficients are shown in green and those with a positive coefficient are shown in red. Proteins shown in grey were not statistically significant at the Bonferroni corrected p-value significance thresholds (* = p&lt;0.005; ** = p&lt;0.001; *** = p&lt;0.0001). Error bars indicate the standard error for each regression coefficient. Figure S2. Density plots showing distributions of plasma protein levels (each protein labelled below the X-axes) for each of the groups (red = controls, green = early-onset Alzheimer’s disease (EOAD), blue = late-onset Alzheimer’s disease (LOAD)). Figure S3. Scatter plots showing distributions of plasma protein levels on the Y axes (each protein labelled on the Y-axes) vs age at inclusion on the X-axes for each of the groups (red = controls, green = early-onset Alzheimer’s disease (EOAD), blue = late-onset Alzheimer’s disease (LOAD)).