Multiple <i>ADH</i> genes are associated with upper aerodigestive cancers

Alcohol is an important risk factor for upper aerodigestive cancers and is principally metabolized by alcohol dehydrogenase (ADH) enzymes. We have investigated six &lt;i&gt;ADH&lt;/i&gt; genetic variants in over 3,800 aerodigestive cancer cases and 5,200 controls from three individual studies. Gene variants rs1229984 (&lt;i&gt;ADH1B&lt;/i&gt;) and rs1573496 (&lt;i&gt;ADH7&lt;/i&gt;) were significantly protective against aerodigestive cancer in each individual study and overall (&lt;i&gt;P&lt;/i&gt; = 10&lt;sup&gt;−10&lt;/sup&gt; and 10&lt;sup&gt;−9&lt;/sup&gt;, respectively). These effects became more apparent with increasing alcohol consumption (&lt;i&gt;P&lt;/i&gt; for trend = 0.0002 and 0.065, respectively). Both gene effects were independent of each other, implying that multiple &lt;i&gt;ADH&lt;/i&gt; genes may be involved in upper aerodigestive cancer etiology

Three-dimensional reconstruction of protein networks provides insight into human genetic disease

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies