Generation of an Alzheimer’s Disease Polygenic Risk Score using a neuropathological confirmed cohort
- PI
- Piras, Ignazio
- DAR ID
- 11431
- Initial Approval Date
- November 14, 2024
- Current DAR Status
- Active
- PI Institution
- Translational Genomics Research Institute
- PI Institution Country
- United States
- PI Institution Type
- Non-Profit
Requested Datasets (1)
Research Use Statement
Objective of the proposed research
Generate an AD polygenic risk score (PRS) using neuropathologically confirmed AD cases and controls (ND). The PRS will be used to assess the risk of AD in other neurological and psychiatric disease and to identify potential risk factors in large population studies accounting for the genetic risk.
Study Design
We will select the neuropathologically confirmed AD and ND samples, and genotypes will be used to conduct a genome-wide association study (GWAS), which results will be used to estimate the PRS.
Analysis plan
VCF files will undergo quality controls with the following inclusion criteria: QUAL >30, DP >10, and GQ > 20. We will exclude variants with allelic balance for heterozygous <0.3 or >0.7. We will include only SNPs with genotype rate >95%, MAF>0.05, Hardy-Weinberg Equilibrium p > 1.0E-06, and sample genotype rate >95%. Sex mismatch will be conducted using the SNPs located in the X chromosome. Duplicated samples will be identified using the pi-hat metrics, removing samples with pi-hat >0.7. Outliers will be identified by the Identity By State statistics. The GWAS will be conducted using BOLT-LMM (PMID:29892013), adjusting for the top 10 PCs and for sex.
PRS will be estimated using the methods described by Marees et al. (PMID:29484742) and from the PR-Sice2 software (PMID:31307061). A wide range of neurological and psychiatric disease will be used as target datasets to assess the risk of AD. The PRS will be also used to investigate AD risk factors accounting for the genetic risk. For the method by Marees et al., we will select the significant SNPs (p<5.0E-08), and the number of alleles present in each individual in the target dataset will be counted and then multiplied by their corresponding GWAS p-value and finally summed across all SNPs for each individual's PRS. The method implemented in PR-Sice2 used a wide range of p-values optimizing the cutoff according to the accuracy prediction in the target phenotype.
This project will be conducted in collaboration with Drs. Eric Reiman Valentina Ghisays (Banner Alzheimer's Institute, Phoenix, AZ). Only the PI (Dr. Piras) will have access to the data.
Generate an AD polygenic risk score (PRS) using neuropathologically confirmed AD cases and controls (ND). The PRS will be used to assess the risk of AD in other neurological and psychiatric disease and to identify potential risk factors in large population studies accounting for the genetic risk.
Study Design
We will select the neuropathologically confirmed AD and ND samples, and genotypes will be used to conduct a genome-wide association study (GWAS), which results will be used to estimate the PRS.
Analysis plan
VCF files will undergo quality controls with the following inclusion criteria: QUAL >30, DP >10, and GQ > 20. We will exclude variants with allelic balance for heterozygous <0.3 or >0.7. We will include only SNPs with genotype rate >95%, MAF>0.05, Hardy-Weinberg Equilibrium p > 1.0E-06, and sample genotype rate >95%. Sex mismatch will be conducted using the SNPs located in the X chromosome. Duplicated samples will be identified using the pi-hat metrics, removing samples with pi-hat >0.7. Outliers will be identified by the Identity By State statistics. The GWAS will be conducted using BOLT-LMM (PMID:29892013), adjusting for the top 10 PCs and for sex.
PRS will be estimated using the methods described by Marees et al. (PMID:29484742) and from the PR-Sice2 software (PMID:31307061). A wide range of neurological and psychiatric disease will be used as target datasets to assess the risk of AD. The PRS will be also used to investigate AD risk factors accounting for the genetic risk. For the method by Marees et al., we will select the significant SNPs (p<5.0E-08), and the number of alleles present in each individual in the target dataset will be counted and then multiplied by their corresponding GWAS p-value and finally summed across all SNPs for each individual's PRS. The method implemented in PR-Sice2 used a wide range of p-values optimizing the cutoff according to the accuracy prediction in the target phenotype.
This project will be conducted in collaboration with Drs. Eric Reiman Valentina Ghisays (Banner Alzheimer's Institute, Phoenix, AZ). Only the PI (Dr. Piras) will have access to the data.
Non-Technical Summary
The research aims to develop a polygenic risk score (PRS) for Alzheimer's disease (AD) using neuropathologically confirmed AD cases and controls. This PRS will help assess AD risk in other neurological and psychiatric conditions and identify potential risk factors, considering genetic predisposition. The study will use Whole Genome Sequencing (WGS) and microarray data from AD and non-AD (ND) samples. A genome-wide association study (GWAS) will generate the necessary genetic data for PRS calculation. Quality control and filtering will be applied to ensure the accuracy of the genetic data. The PRS will be calculated using genome-wide significant SNPs and analyzed with PR-Sice2 software. The score will be applied to various neurological and psychiatric datasets to evaluate the risk of AD and explore potential risk factors.
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.