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Joint Research Seminar (24 Sep 2014)

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Joint Research Seminar

Genomics Strategic Research Theme
Centre for Genomic Sciences

A hidden Markov random field based
Bayesian method for the detection of
long-range chromosomal interactions in
Hi-C Data

Dr Yun Li

Genetics and Biostatistics
University of North Carolina, Chapel Hill


24 September 2014 (Wed)
10 – 11 am


Seminar Room 7-03
7/F, HKJC Building for Interdisciplinary Research

5 Sassoon Road, Pokfulam, HK

 
Abstract:

Advances in chromosome conformation capture and next-generation sequencing technologies are enabling genome-wide investigation of dynamic chromatin interactions. For example, Hi-C experiments generate genome-wide contact frequencies between pairs of loci by sequencing DNA segments ligated from loci in close spatial proximity. One essential task in such studies is peak calling, that is, the identification of non-random interactions between loci from the two-dimensional contact frequency matrix. Successful fulfilment of this task has many important implications including identifying long-range interactions that assist in interpreting a sizable fraction of the results from genome-wide association studies (GWAS). The task – distinguishing biologically meaningful chromatin interactions from massive numbers of random interactions - poses great challenges both statistically and computationally. Modelbased methods to address this challenge are still lacking. In particular, no statistical model exists that takes the underlying dependency structure into consideration. We propose a hidden Markov random field (HMRF) based Bayesian method to rigorously model interaction probabilities in the two-dimensional space based on the contact frequency matrix. By borrowing information from neighboring loci pairs, our method demonstrates superior reproducibility and statistical power in both simulations and real data.
 
 
About the Speaker: 

Dr Yun Li is an Assistant Professor in the Departments of Genetics and Biostatistics, University of North Carolina at Chapel Hill. Dr Li’s research focus is on the development of statistical methods and their application to the genetic dissection of complex diseases and traits. Dr Li has developed a genotype imputation method (implemented in software MaCH) that has become standard in the analysis of genome-wide association scans. Dr Li has worked on genomewide scans for genetic variants underlying several metabolic, auto-immune and cardiovascular diseases and related quantitative traits. Dr Li has been actively involved in a number of next-generation sequencing (NGS) based studies including the 1000 Genomes Project (Project Leader on calling SNP genotypes from low-coverage pilot).
 

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