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Abstract Details
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Activity Number:
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482
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300511 |
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Title:
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Efficient Association Tests for Gene/SNP Sets/Networks
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Author(s):
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Xihong Lin*+ and Tianxi Cai and Raymond James Carroll
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Companies:
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Harvard School of Public Health and Harvard University and Texas A & M University
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Address:
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655 Huntington Ave, Harvard School of Public Health, Boston, MA, 02115, USA
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Keywords:
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Abstract:
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We develop an efficient test for studying the association of disease phenotypes and gene/SNP sets or gene networks. This class of tests accounts for the fact that genes within a gene set/network are often correlated and the signals are likely to be sparse. The proposed test effectively dampens the effects of the noisy variables. We study the theoretical properties of the test and show that they are more powerful than the traditional multivariate tests especially when the gene set is large. We apply the proposed methods to genome-wide association studies to study the effects of genomic features on disease phenotypes.
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