主 题: Statistical approaches for predicting the functional effect of
报告人: Prof. Iuliana Ionita-Laza (Columbia University)
时 间: 2016-06-30 12:30-13:30
地 点: tyc234cc 太阳成集团镜春园全斋 29
Over the past few years, large scale genomics projects such as
the ENCODE and Roadmap Epigenomics have produced genome-wide data on a
large number of biochemical assays for a diverse set of human cell types
and tissues. Such data can play a critical role in identifying
putatively causal variants among the abundant natural variation that
occurs at a locus of interest. I will discuss challenges in using these
data for predicting functional effects of variants, and discuss recent
work on unsupervised approaches to integrate these diverse sets of
annotations into a single predictor of functional importance. I will
demonstrate the usefulness of such scores in the context of complex
disease genetics. In the second part, I will discuss some quantile
regression methods to identify eQTLs (expression quantitative trait
loci) using data across many tissues from the GTEx project, a major
effort to study the effect of genetic variants on gene expression in
multiple tissues.
