211702 Statistical Issues and Challenges in Analyzing High-throughput 'Omics Data

Tuesday, November 10, 2009: 5:00 PM

Xihong Lin , Harvard School of Public Health, Boston, MA
  
With the advance of biotechnology, massive "omics" data, such as genomic and proteomic data, become rapidly available. An increasing challenge is how to analyze such high-throughput "omics" data, interpret the results, make the findings reproducible. We discuss several statistical issues in analysis of high-dimensional "omics" data in population based omics studies. We present statistical methods for analysis of several types of "omics" data, including incorporation of biological structures in analysis of data from genome-wide association studies and analysis of genome-wide DNA methylation data.

Learning Objectives:
To discuss statistical challenges in the analysis of genomics data

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am Professor in Biostatistics and have been working on statistical methods for analyzing high-dimensional omics data in the last few years. My work is supported by the NIH R37 grant in this area.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

See more of: Special Annual Statistical Methodology Session: Challenges in Genomics Studies
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