Genetic Analyses Using Data from MiGA and related studies

The purpose of our study is to identify novel genetic factors associated with age related neurodegeneration. This includes identifying genetic factors associated with the risk of these conditions, as well as genetic risk factors associated with age-at-onset (AAO) for these conditions. The findings from our analyses have the potential for identification of new therapeutic targets for Alzheimer's Disease and other age related neurodegenerative disease. The findings from our analyses also have the potential for identification of genetic and phenotypic biomarkers that will be beneficial for subsetting patients in new ways. Using the data we have requested we will be identifying genes driving neurodegenerative diseases by identifying dysregulated genes in cases through using total and allele specific gene expression profiles.

Genotypes and RNA-seq reads will be used to generate allele specific expression (ASE). RNA-seq counts and ASE from controls will be used to model the variance of both total and ASE gene expression. Total gene expression vs ASE specifically from cases will be used to identify dysregulated genes in single individuals. These will then be compared to pathway and known disease-associated genes. Case/control status, genotype, and RNA-seq data will be all be evaluated together through quantitative trait loci (QTL) analyses, and additional statistical association analyses.

All data will remain anonymized and securely stored, and only those listed on our application and their staff will have access to these data. We will not share any of the individual level data outside of Genentech nor beyond the researchers on our application. We will adhere to all data use agreement stipulations through the NIAGADS. We have a secure computational environment called Rosalind within Genentech where we will use these data. We have IT security staff that constantly monitor all our research computing, assuring safety and privacy of all of our stored data. We will not collaborate with researchers at other institutions.

Genetic variation and gene expression data allows us to understand more of the genetic contribution to risk and protection from diseases such as Alzheimer’s and dementia. This information also allows us to identify important biological contributors to disease for developing effective treatment strategies, and identifying groups of individuals that would benefit most from new treatments. Our exploration of this relationship between genotype, disease traits, gene expression, and outcomes, through these datasets will allow us to pursue important new findings for disease treatment.