Characterizing complex structural variation in Alzheimer's Disease

We will run multiple structural variation (SV) callers, including but not limited to Absinthe, Canvas, Manta, MELT, and PanGenie, on the entire Alzheimer’s Disease Sequencing Program (ADSP) WGS data set. We will combine these data into a single coherent SV call set which we will genotype on the entire ADSP WGS data set. We will use these data, together with SNV and indel calls, to generate a phased integrated SNV/indel/SV genotype panel for ADSP. We will then use this to impute the presence of SVs in data from the Alzheimer’s Disease Genetics Consortium (ADGC) genotyping on samples not included in ADSP and also in external phenotyped population sequencing projects including but not necessarily limited to UK Biobank. The resulting directly genotyped and imputed SVs will be used in association studies with AD status and related phenotypes.

We will perform this work in collaboration with Badri Vardarajan at Columbia University.

We will collaborate with Gerard Schellenberg, Li-San Wang, Wan-Ping Lee and Yuk Yee (Fanny) Leung in GCAD at University of Pennsylvania to share individual SV callsets we have generated distinct from theirs and build a joint SV callset.

NYGC will contract the services of Wayne Clarke from Outlier Informatics for this project. Dr. Clarke will be responsible for the cloud implementation of NYGC's SV calling pipelines and for optimizing and running data analysis on AWS and GCP as well as locally on NYGC hardware. In this capacity Dr. Clarke will use NYGC accounts and compute infrastructure to access NIAGADS data in the cloud or downloaded to NYGC's on prem compute infrastructure.

Structural variation refers to alterations in DNA that change the copy number or ordering of large blocks of DNA. Historically, these have been difficult to detect accurately and thus their impact on disease risk has often been ignored. We will apply the latest methods to discover and characterize structural variants in Alzheimer’s Disease (AD) cases and controls, and then use genetic association techniques to determine whether these events are likely to be correlated with increased or decreased risk of developing AD or any of the specific features of AD. Identifying additional genetic risk factors may improve diagnosis of AD and may increase our understanding of the biological mechanisms leading to AD and possible therapeutics.