Update netwas.rst

docs/netwas.rst

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 Tissue-specific networks provide a new means to generate hypotheses related to the molecular basis of human disease. We developed an approach, termed network-wide association study (NetWAS). In NetWAS, the statistical associations from a standard GWAS guide the analysis of functional networks. This reprioritization method is driven by discovery and does not depend on prior disease knowledge. NetWAS, in conjunction with tissue-specific networks, effectively reprioritizes statistical associations from distinct GWAS to identify disease-associated genes, and tissue-specific NetWAS better identifies genes associated with hypertension than either GWAS or tissue-naive NetWAS.
 
 
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+NetWAS trains a support vector machine classifier using nominally significant (P < 0.01) genes as positive examples and 10,000 randomly selected non-significant (P ≥ 0.01) genes as negatives. The classifier is constructed using a tissue network relevant to a disease (e.g. kidney for hypertension), where the features of the classifier are the edge weights of the labeled examples to all the genes in the network. Genes are re-ranked using their distance from the hyperplane, which represent a network-based prioritization of a GWAS, termed NetWAS.
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+To calculate per-gene P values for a GWAS, we suggest the versatile gene-based association study (VEGAS) system.
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+We have performed and evaluated NetWAS on six GWAS: C-reactive protein levels (lnCRP), type 2 diabetes (T2D), body mass index (BMI), hypertension (ht), alzheimer's (adni) and advanced age-related macular degeneration (advanced AMD). 
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