There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Bernabe Bustos |
| Institution: | Northwestern University |
| Department: | Neurology |
| Country: | |
| Proposed Analysis: | The first objective is to perform an exome-wide burden analysis of single nucleotide variants in the NIAGADS AD cases vs controls dataset. We will count all alleles per individual across frequencies (AF< 5%, 1%, 0.1%, singleton, ultra-rare variants), functional annotations (Protein altering variants, nonsynonymous, loss-of-function, synonymous and noncoding) and damaging predictions (CADD scores >12.37=damaging). We will also stratify cases according to age of onset (early onset, late onset). Then we will run a logistic regression modeling the number of alleles per individual against disease status, including correction for relevant covariates, such as age, sex, population structure (PCA) and sequencing coverage if applicable. We will correct associations for multiple testing using the Bonferroni method to detect significant results. The second objective is to perform a gene-set burden analysis of the most enriched variants from the previous goal, using gene-list from highly constrained genes according to gnomAD (pLI>0.9), the Molecular Signatures database Hallmark and C2 curated gene-sets, and highly expressed genes from 54 specific GTEx tissues, in order to identify molecular pathways, biological processes and tissue-specific expression patterns enriched. Here we will use the SKAT-O software to perform the variant enrichment on each gene-set with the same covariates used on the first objective. We will use 10,000 permutations and a family-wise error rate (FWER< 0.05) as correction for multiple testing to select the most enriched gene-sets and tissues. The third objective will be to run a gene-wise burden test and perform a protein-protein interaction network along with enrichment in brain single-cell expression data, in order to prioritize significant candidate genes. Here we will map variants to single genes and use SKAT-O in a similar way to the previous objective. Then we will take all genes with uncorrected P< 0.05 and run a protein-protein interaction network with WebgestaltR, using the network-topology analysis and random walk algorithm, and Gene-Ontology enrichment of the resulting network using BIOGRID. We will use STRING in order to get network interaction significance. |
| Additional Investigators |
