PROJECT SUMMARY/ABSTRACT
Alzheimer's disease (AD) is the most common form of dementia and the sixth leading cause of death in the
United States. Currently no treatments are available that prevent or slow the disease. Genetics is thought to
account for up to 70% of risk for developing AD. Apolipoprotein E (APOE) is the greatest genetic risk factor
with inheritance of the e4 allele of APOE (APOEe4) contributing approximately 15-50% of late-onset AD (LOAD)
genetic risk. More than twenty other genes have been consistently associated with AD through next generation
sequencing and genome-wide association studies (GWAS). Therefore in most cases, AD is likely caused by
interactions between combinations of genetic factors. However, for many genes, the causative risk variants
have not been identified and the mechanisms by which these genes contribute to AD are not known. This
knowledge gap makes it extremely difficult to predict risk for and develop new strategies for treating AD. To
bridge this gap, we propose a systems genetics approach in mice to identify combinations of genetic
factors that modulate risk for AD. In particular we will focus on identifying genetic interactors of APOEe4. We
aim to identify genetic factors that modify APOE-dependent processes most relevant to AD including lipid and
amyloid clearance, APP processing, synaptic maintenance, vascular health and immune cell activation.
Previous attempts to model AD in mice have utilized only a tiny fraction of the available genetic diversity and
we believe this is one of the main reasons why mouse models have failed to recapitulate key aspects of human
AD contributing to the lack of success in clinical trials. At The Jackson Laboratory (JAX), we have access to
mouse strains that capture as much genetic diversity as is present in the human population and the
expertise to maximize their potential. Our approach incorporates four classical inbred strains (C57BL/6J
(B6), WSB/EiJ, CAST/Ei and NZO/HILtJ) and a recently developed panel of recombinant inbred lines (The
Collaborative Cross, CC). We will use a combination of cutting edge genetic, genomic and computational
methods to formulate and validate predictions about how specific AD-relevant genes interact to affect AD-
related phenotypes. In Aim 1, we will determine the extent by which diverse genetic contexts modulate
APOEe4-dependent processes. To achieve this we have crossed APOEe4, APPswe and PS1de9 from C57BL/6J to
WSB/EiJ, CAST/EiJ and NZO/HILtJ. Our data show these strains provide variation in AD-relevant outcomes
including cognitive ability, immune cell activation, body composition and metabolism. In Aim 2, we will
determine how known LOAD genes modify the effects of APOEe4. We have identified 10 CC lines that together
harbor an allelic series for at least 12 GWAS genes such as TREM2, ABCA7, BIN1, CLU, PICALM and CD33.
We will determine specific variants that, in combination with APOEe4, affect AD phenotypes. In Aim 3 we will
validate specific combinations of variants in different genetic contexts to more precisely define the role of
genetic interactors of APOEe4 in AD.