Research Project
10332468
Abstract In response to PAR-20-099 ?Harmonization of Alzheimer?s Disease and Related Dementias (AD/RD) Genetic, Epidemiologic, and Clinical Data to Enhance Therapeutic Target Discovery?, we have assembled a multidisciplinary team that includes experts in neuroimaging, neuropsychology, fluid biomarkers, neuropathology, and vascular contributions to ADRD to work in close partnership with the NIH and the Alzheimer?s Disease Sequencing Project (ADSP). Our ADSP Phenotype Harmonization Consortium, or ?ADSP-PHC?, seeks to work in coordination with existing ADSP workgroups and initiatives to (1) streamline access to endophenotype data, (2) provide high quality endophenotype harmonization across multiple research domains, and (3) provide comprehensive documentation of both data availability and harmonization procedures. This project includes two coordinating centers, three cores, and eight domain- specific harmonization teams led by world-renowned experts in their fields. While our efforts will focus on data access, documentation, and harmonization, we will work closely with other ADSP workgroups and other large-scale harmonization efforts to maximize the impact and align with NIH priorities. In particular, we will focus harmonization on ADRD-related endophenotypes, including cognitive scores derived from detailed neuropsychological assessments, measures of neuropathology measured both ex vivo (neuropathological assessment at autopsy) and in vivo (fluid biomarkers and positron emission tomography biomarkers), concomitant pathways of injury (vascular risk factors and vascular brain injury), and measures of neurodegeneration focusing on both white (diffusion-weighted MRI) and grey matter (T1-weighted MRI). The proposed harmonization effort will provide an unprecedented opportunity to disentangle the genetic architecture of individual biological contributors to ADRD risk and progression. The harmonized data, protocols, and educational tools developed by the ADSP-PHC will transform the ADRD landscape, accelerate discovery, and facilitate the application of emerging big data analytic approaches leveraging machine learning and artificial intelligence.
