Journal
AMERICAN JOURNAL OF HUMAN GENETICS
Volume 81, Issue 2, Pages 397-404Publisher
UNIV CHICAGO PRESS
DOI: 10.1086/519794
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Funding
- NCI NIH HHS [R25 CA112355, R25T CA112355, R01 CA88164, R01 CA088164] Funding Source: Medline
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Genomewide association Studies (GWAs) initially investigate hundreds of thousands of single-nucleotide polymorphisms (SNPs), and the most promising SNPs are further evaluated with additional subjects, for replication or a joint analysis. Deciding which SNPs merit follow-up is one of the most crucial aspects of these studies. We present here an approach for selecting the most-promising SNPs that incorporates into a hierarchical model both conventional results and other existing information about the SNPs. The model is developed for general use, its potential value is shown by application, and toots are provided for undertaking hierarchical modeling. By quantitatively harnessing all available information in GWAs, hierarchical modeling may more clearly distinguish true causal variants from noise.
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