Alzheimers disease (AD) is the most common late-onset neurodegenerative disease. It is known to have strong genetic influences. Over the last decade using advanced high-throughput genotyping/sequencing technology, researchers have identified nearly 30 AD susceptibility genes/loci. However, these loci account for only a portion of AD heritability. Furthermore, almost all of them have been identified for the risk of developing AD, where disease status is the primary outcome of interest. In our paper (Li et al. AJHG 2002) we highlighted the hypothesis of genetic predisposition for age-at-onset (AAO) of AD, estimating the heritability for AAO of AD to be ~42% and reporting linkage to new genetic quantitative trait loci for AD AAO. Recently, two genome wide association studies for AAO of AD confirmed findings in the APOE gene, but otherwise, there was little overlap. Overall, much remains to be discovered about genetic factors for AAO of AD. If AAO genes can be identified, they will contribute new knowledge about the genetic modifiers of AD, provide excellent intervention targets for delaying the onset of AD, and improve our ability to predict AAO in an individual. The increase in large genome- wide genetic datasets for AD, provides an excellent resource for the comprehensive investigation of the genetic basis of AAO of AD through a well-conceived statistical analysis plan and methods development. Our central hypothesis is that genetic variants exist that regulate the variation of AAO and/or one's age-associated risk. To test this hypothesis, we will use datasets from the Alzheimer's Disease Genetics Consortium (ADGC) and Alzheimer's Disease Sequencing Project (ADSP). These two large datasets will allow us to search for common and rare variants for AAO of AD. Our analysis strategy is unique from previous studies, because we will not only analyze AAO as a quantitative trait in a case-only design as used in the past, but we will also treat AAO as a censored trait by applying novel survival-analysis methodology. The survival-analysis approach will include unaffected subjects whose AAO was censored at the age of enrollment, and allow us to identify genetic variants that are associated with AD risk in an age-dependent manner. To uncover novel genes tied to AAO and develop predictive models of AAO of AD, we propose the following aims: (1) Conduct discovery analysis in the ADGC dataset to identify common and/or rare variants associated with AAO of AD; (2) Replicate AAO findings in the independent ADSP dataset and additional cohorts; (3) Develop a suite of analysis programs to support the proposed analysis in Aims 1 and 2 and accommodate the different data structures included in the Discovery and Replication datasets; and (4) Develop a polygenic risk score model to predict AAO of AD. This proposal promises to provide targets with the potential to delay onset of AD and to advance the future development of personalized medicine utilizing individual genetic burden to predict AD AAO. Furthermore, it will provide innovative tools for data analysis and risk modeling to the genetic research community.
This project will identify genes that influence age-at-onset (AAO) of Alzheimer's disease (AD), the most common late-onset neurodegenerative disease. These genes will provide excellent targets for therapies with the potential to delay onset of AD. In addition, we will develop predictive models that will allow identification of individuals most in need of early interventions.
| Sims, Rebecca (see original citation for additional authors) (2017) Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease. Nat Genet 49:1373-1384 |
