Statistical approaches for predicting the functional effect of
2016-06-30 10:00 ~ 11:00
报告人：Prof. Iuliana Ionita-Laza, Columbia University
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.