Comparative Analysis of Plasma Proteomic Profiles in Healthy Adults and Individuals with Neurodegenerative Diseases
- PI
- Richards, Brent
- DAR ID
- 11181
- Initial Approval Date
- November 25, 2024
- Current DAR Status
- Closed
- PI Institution
- SIR MORTIMER B. DAVIS JEWISH GEN HOSP
- PI Institution Country
- Canada
- PI Institution Type
- Non-Profit
Requested Datasets (1)
Research Use Statement
Objectives:
(1) Identify disease-specific pQTL
(2) Identify age-specific pQTL
(3) Identify causal disease pQTL through Mendelian randomization & colocalization
Study design & analysis plan:
• Disease-specific analyses
We will use linear or logistic regression to ascertain the role of pQTLs on disease. We will investigate the associations of the 7,028 aptamers in each group separetely (i) 1,076 cognitively normal individuals, (ii) the 1,001 clinically diagnosed Alzheimer’s disease patients, and (iii) 1,030 individuals with other neurodegenerative diseases. Each linear model will be adjusted for age, sex, and disease-relevant confounders.
• Age-specific analyses
Again using a linear or logistic regression models, we will stratify our analysis by age, setting a threshold to distinguish individuals considered old and young. Thus allowing us to detect if there is an effect of age on specific pQTLs.
• Causal pQTL through Mendelian randomization & colocalization
A recent study in the UKB claimed a predictive and causal role for GFAP, NEFL, GDF15 and LTBP2 Alzheimer’s disease1. We will the R package TwoSampleMR2 to estimate the causal effect of pQTLs on disease outcomes. We will focus on cis-pQTLs since they are less bias to horizontal pleiotropy. For a single SNP, wald ratio will be calculated, and for multiple SNPs the inverse variance weighted (IVW) approach will be employed. We will select independent SNPs (r2 < 0.001), and perform the necessary harmonization steps to perform the MR analysis. Finally, to account for linkage disequilibrium (LD) bias, we will perform a colocalization analysis using both coloc3 and coloc.susie4.
(1) Identify disease-specific pQTL
(2) Identify age-specific pQTL
(3) Identify causal disease pQTL through Mendelian randomization & colocalization
Study design & analysis plan:
• Disease-specific analyses
We will use linear or logistic regression to ascertain the role of pQTLs on disease. We will investigate the associations of the 7,028 aptamers in each group separetely (i) 1,076 cognitively normal individuals, (ii) the 1,001 clinically diagnosed Alzheimer’s disease patients, and (iii) 1,030 individuals with other neurodegenerative diseases. Each linear model will be adjusted for age, sex, and disease-relevant confounders.
• Age-specific analyses
Again using a linear or logistic regression models, we will stratify our analysis by age, setting a threshold to distinguish individuals considered old and young. Thus allowing us to detect if there is an effect of age on specific pQTLs.
• Causal pQTL through Mendelian randomization & colocalization
A recent study in the UKB claimed a predictive and causal role for GFAP, NEFL, GDF15 and LTBP2 Alzheimer’s disease1. We will the R package TwoSampleMR2 to estimate the causal effect of pQTLs on disease outcomes. We will focus on cis-pQTLs since they are less bias to horizontal pleiotropy. For a single SNP, wald ratio will be calculated, and for multiple SNPs the inverse variance weighted (IVW) approach will be employed. We will select independent SNPs (r2 < 0.001), and perform the necessary harmonization steps to perform the MR analysis. Finally, to account for linkage disequilibrium (LD) bias, we will perform a colocalization analysis using both coloc3 and coloc.susie4.
Non-Technical Summary
The study aims to understand how specific genetic variations related to proteins impact neurological diseases, particularly Alzheimer's. Over 7,000 genetic proteins will be analyzed in three groups: healthy individuals, Alzheimer's patients, and those with other neurodegenerative diseases. The effect of age, on these genetic variations will be explored in the study. The study will replicate results from a recent proteomic study in the UKBiobank. Using advanced statistical methods to assess the causal role of these genetic markers in Alzheimer's. The stydu will prioritize certain genetic variations and use different statistical techniques to analyze their effects. To ensure accuracy, they'll also address potential biases in their analysis.
Reported Publications
Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.
No publications have been reported for this DAR.