Thesis defense: Abhishek Sarkar "Interpreting the role of non-coding variation in human disease"

Speaker: Abhishek Sarkar , MIT CSAIL

Date: Wednesday, March 22, 2017

Time: 9:30 AM to 10:30 AM Note: all times are in the Eastern Time Zone

Public: Yes

Location: 32-G449 (Kiva)

Event Type:

Room Description:

Host: Manolis Kellis , MIT CSAIL

Contact: Geraldine McGowan, 617-253-3497, gmcgowan@csail.mit.edu

Relevant URL:

Speaker URL: None

Speaker Photo:
None

Reminders to:

Reminder Subject: TALK: Interpreting the role of non-coding variation in human disease

Abstract:

One of the fundamental goals of human genetics is to identify the
genetic causes of human disease to ultimately design novel
therapeutics. However, two challenges have become readily apparent.
First, the majority of genomic regions associated with disease do not
implicate protein-altering variants but might instead alter gene
regulation, making interpretation and validation more difficult.
Second, the genomic regions associated with disease explain a fraction
of the variance of associated phenotypes, suggesting human diseases
are highly polygenic and that many additional regions remain to be
discovered and characterized.

Here, we address these challenges by using functional annotation of
the human genome spanning diverse data types: epigenomic profiles,
gene regulatory circuitry, and biological pathways. We first develop a
method to simultaneously select relevant genomic regions not yet
associated with disease as well as select relevant functional
annotations enriched in those regions. We use this framework to
characterize specific genetic variants in the selected regions, the
gene regulatory elements they reside in, the cellular contexts in
which those elements are active, their upstream regulators, their
downstream target genes, and the biological pathways they disrupt
across eight common diseases.

We then investigate why predicted regulatory elements are enriched in
disease-associated variants by framing the problem as Bayesian
inference of hyperparameters in a structured sparse regression model.
We propose an active learning method to efficiently explore the
hyperparameter space and avoid exponential scaling in the dimension of
the hyperparameters. We show in simulation that our method can
distinguish between possible explanations of the observed enrichments,
and we characterize potential biases in the estimates.

Together, our results can help guide the development of new models of
disease and gene regulation and discovery of biologically meaningful,
but currently undetectable regulatory loci underlying a number of
common diseases.

Research Areas:

Impact Areas:

This event is not part of a series.

Created by Geraldine McGowan Email at Friday, March 17, 2017 at 1:23 PM.