Zihuai He, PhD
Assistant Professor (Research) of Neurology and of Medicine
Quantitative Sciences Unit & Biomedical Informatics Research
Stanford University

Abstract:

Understanding the functional consequences of genetic variants is a challenging problem, especially for variants in non-coding regions. The noncoding genome covers ~98% of the human genome and includes elements that regulate when, where, and to what degree protein-coding genes are transcribed. We will talk about a combination of new methodologies for the analysis of noncoding variants, integrating whole genome sequencing, epigenetic technologies and experimental approaches. First, we propose a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell type/tissue specific epigenetic annotations to predict functional consequences of non-coding genetic variants. Second, we propose a scan statistic framework, GenoScan, to simultaneously detect the existence, and estimate the locations of the association signal at genome-wide scale. Last, we will discuss their application to integrative analysis of complex trait genetics.