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: | Steve Horvath |
| Institution: | University of California Los Angeles |
| Department: | Human Genetics |
| Country: | |
| Proposed Analysis: | Molecular causes and clinical consequences of epigenetic age acceleration The PI recently developed a highly accurate candidate biomarker of aging based on DNA methylation (DNAm) levels (Horvath 2013, PMID: 24138928). This epigenetic clock can be used to measure the age of human cells, tissues, and organs. By contrasting the resulting measure of DNAm age with chronological age, one can measure age acceleration effects in various tissues such as blood or brain. For example, a subject exhibits negative age acceleration in blood tissue if the DNAm age is younger than expected based on chronological age. We have recently developed more powerful measures of epigenetic age acceleration and would like to evaluate their utility in the ADNI data set. The preciouse ADNI data will lend themselves for elucidating the causes and consequences of epigenetic age acceleration at multiple levels: genetic determinants, neuropathology, and clinical biomarkres. Based on both published and unpublished preliminary data, we expect that genome wide association studies (GWAS) will able to find single nucleotide polymorphisms (SNPs) that are significantly associated with age acceleration effects (measured by the epigenetic clock). Further, our preliminary data suggest that epigenetic age acceleration might relate to cognitive functioning traits including mild cognitive impairment and even Alzheimer's disease. Surprisingly, our preliminary data suggest that age acceleration in blood might correlate with brain volume changes. We would like to validate these preliminary findings using the precious ADNI data set. Aims Aim 1: Carry out a GWAS study of age acceleration, i.e. a quantitative trait locus (QTL) analysis of age acceleration (based on DNA methylation age) using all samples for which DNA methylation and SNP data are available. Aim 2: Relate the significant SNPs (identified in Aim 1) to Alzheimer's disease status and various measures of cognitive functioning in these subjects. Hypothesis: SNPs that relate to epigenetic age acceleration also relate to AD status or cognitive functioning traits. Aim 3: Relate epigenetic age acceleration to AD status, mild cognitive impairment, various measures of neurocognitive functioning and disease progression (memory loss, amyloid beta, neurodetection detected by the rise of CSF tau species and and synaptic dysfunction, measured via FDG-PET. Hypothesis: Epigenetic age acceleration relates to various measures of cognitive functioning, e.g. Rationale: Using several small data sets, we have not yet found a significant relationship between epigenetic age acceleration and AD status. However, these previous studies were under powered and therefore inconclusive. Hopefully, the additional data will allow us to detect an effect. Aim 4: To evaluate the relationship between age acceleration (measured using the epigenetic clock) and brain atrophy and neuron loss measured with MRI (most notably in hippocampus, caudate nucleus, and medial temporal lobe). Standard clinical characteristics: Age at Death (years), gender, ethnicity/race, educational level Phenotypic data; Amyloid beta imaging detected in CSF and PET amyloid imaging Neurodegeneration detected by rise of CSF tau species and synaptic dysfunction, measured via FDG-PET Brain atrophy and neuron loss measured with MRI (most notably in hippocampus, caudate nucleus, and medial temporal lobe) Cognitive assessments including measures of memory loss Any variables related to general cognitive decline measured by cognitive assessment DNA methylation data We plan to analyze all available DNA methylation data measured on the Illumina platform. It might be best to send us the raw idat files so that we can carry out the normalization. Further, information on batches and possible Illumina chips. Genome wide association studies and candidate SNP studies with PLINK To carry out the genomewide association analysis, we will use the PLINK software (Purcell et al 2007, http://pngu.mgh.harvard.edu/~purcell/plink/) We can carry out the SNP imputation ourselves. We would like to obtain GWAS results for several million SNPs (imputed based on the 1000 genomes as a reference panel). MRI and fMRI and DTI data: We don't need access to raw data. Rather, we would prefer access to higher level processed/normalized data. |
| Additional Investigators |
