Probabilistic Methods for Human Genetics and Disease Circuitry.

Speaker: Yue Li

Date: Wednesday, February 22, 2017

Time: 10:00 AM to 12:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: 32-G449

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Host: Manolis Kellis, CSAIL, MIT

Contact: Geraldine McGowan, 617-253-3497,

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Reminder Subject: TALK: Probabilistic methods for human genetics and disease circuitry

In the first part of the talk, I will discuss a Bayesian model named RiVIERA to jointly infer disease-associated and functional risk variants across multiple related human complex diseases (Li and Kellis, 2016a, b). RiVIERA leverages epigenomic annotations to infer tissue-specific regulatory variants in the context of linkage disequilibrium. I applied RiVIERA to 26 distinct human complex diseases to dissect their regulatory genetics.

In the second part of the talk, I will also talk about modeling large-scale electronic health records (EHR) using a novel Bayesian model named PheMAP. The main goal of PheMAP is to provide clinical recommendations such as further lab tests and diagnoses conditioned only on a subset of the EHR entries of an unseen patient. The model-based learning involves inference of potentially missing EHR entries by jointly modeling the data distribution and the potentially non-missing at random mechanisms. Applying PheMAP to real EHR data, we identified many meaningful disease modules.

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Created by Geraldine McGowan Email at Friday, February 17, 2017 at 5:34 PM.