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Abstract Details
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Activity Number:
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147
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Type:
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Invited
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300562 |
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Title:
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Statistical Issues in Risk Modeling of Complex Diseases Using High-Throughput Data
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Author(s):
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Heping Zhang*+
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Companies:
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Yale University
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Address:
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60 College Street, New Haven, CT, ,
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Keywords:
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Abstract:
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Genes and environmental factors are believed to underlie the etiology of complex diseases, and to study the risk of complex diseases, high throughput data such as common and rare genetic variants have been generated. Before risk modeling, it is necessary to develop and assess the efficiency of statistical methods and models. Although many real data sets have been collected, they are not ideal for this developmental and evaluation purpose because the true answer is unknown. Thus, simulation data must be generated, must resemble the real data, and must be computationally feasible. In this talk, I will first discuss strategies to generate such data, and then present statistical methods that can be used to identify common and/or rare variants for complex diseases.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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