Multi-omics approaches for gene discovery in Alzheimer's Disease.

Alzheimer?s Disease (AD) is a complex, heterogeneous disorder, and risk to AD is influenced partly by genetics. Understanding the genetic mechanisms that play a role in disease is important as it can lead to a better understanding of the underlying molecular mechanisms, and can identify new gene targets for therapeutic development. We propose gene centric approaches that leverage diverse omics datasets developed specifically for AD (such as AMP-AD), but also more general resources such as GTEx, PsychENCODE, ENCODE, and Roadmap Epigenomics. We will develop quantile tools for transcriptome-wide association studies (TWAS), which are generalizations of TWAS to more complex and heterogenous scenarios where the linear assumptions in standard TWAS are likely to fail. We will also develop gene-based tests using data from WGS by jointly analyzing coding and regulatory variation in predicted regulatory elements likely to affect the expression of a gene under consideration. We will implement these analytical tools into software packages to be made freely available to the community. We will also apply them to some of the largest existing genetic datasets for AD, both GWAS and WGS, and will make the results available to the community on a specially designed web portal.