Project Summary/Abstract Alzheimer's disease (AD) is a highly heritable, common and fatal neurodegenerative disease among older people. There have been significant advances in genomic studies of risk factors, volumetric variations of the human brain and AD, however, there is a lack of systemic analysis of these traits together. Single trait analysis is not only less powerful than multiple trait analysis in searching for disease associated variants but also misses the opportunity to identify novel pleiotropic variants and to understand the biological mechanism among them. Since risk factors and brain function and structure all contribute to AD, an AD polygenic risk score incorporating genetic components obtained from risk factors and brain imaging data may substantially improve its. But how to incorporate this information has not been studied. Furthermore, searching for rare variants contributing to AD is extremely challenged and requires a large sample size. Novel analysis approaches are necessary to improve the identification of AD associated rare variants. Our NHGRI funded project R01 HG011052 entitled “Statistical analysis of large genomic data sets” is developing statistical methods and software for estimating causal effects among traits and searching for pleiotropic variants, as well as testing rare variant associations by incorporating linkage evidence and natural selection. We believe these novel methods can contribute AD research and move the field forward. In this project we propose two specific aims using the methods and software we developed in HG011052. In Aim 1, we will construct a genetic network, identify genetic variants with and without pleiotropy effects and construct a composite polygenic risk score for AD. In Aim 2, we will identify rare genetic variants associated with AD by incorporating family information and natural selection. Our proposed work focuses on understanding the mechanism of AD and the project HG011052 is the foundation of this administrative supplement.