Quantification of the two mechanisms of yeast SH3 interaction change: 1) interaction rewiring, and 2) protein change.

<p>Rows including the species name contain the interaction rate for the SH3 interaction change denoted by 1) above, while the row immediately below illustrates the rate for the second type of interaction change. Rates before and after the backslash were calculated respectively by using <i>S. cerevisiae</i> and the closest species to the species in question from the gene derived phylogenetic tree as the reference species. Divergence time is taken with respect to the last common ancestor in millions of years.</p

Binding cluster formation and existence of a significant correlation between interaction conservation and both binding cluster size and the number of binding site clusters.

<p>A) Global protein in-degree decomposition into amino acid resolution binding demonstrates the formation of binding site clusters. A segment of the <i>S. cerevisiae</i> protein Srv2p is shown demonstrating binding cluster formation B) A strong and significant correlation between interaction conservation and binding cluster size exists (ρ = 0.192, p-value = 4.67×10⁻⁶). C) The correlation between interaction conservation and the number of clusters is found to have an even stronger correlation with the number of clusters found on a protein for the SH3 interaction network (ρ = 0.461, p-value = 5.85×10⁻¹²).</p

Rates of interaction change.

<p>Divergence distances are taken with respect to the last common ancestor. A) The canonical phylogeny based on protein sequences of common genes found in all 23 species (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002411#s3" target="_blank">Materials and Methods</a>). B) Rates of SH3 interaction change calculated such that no branch is shared in the canonical phylogenetic tree versus divergence in millions of years. C) The number of SH3 interaction changes with respect to <i>S. cerevisiae</i> versus divergence in millions of years. Saturation is reached within ∼200 million years of divergence.</p

Pathway-based discovery of genetic interactions in breast cancer

<div><p>Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.</p></div

An example between pathway interaction identified from the BPC3 cohort.

<p>(A) Interaction between Acute Myeloid Leukemia (AML) gene set and Steroid hormone biosynthesis (SHB) gene set. White and yellow nodes represent the SNPs mapped to genes in the corresponding pathways and their color shows the significance of a univariate test in the same breast cancer cohort (white: not significant; yellow: marginally significant, 10⁻⁴ < p < 0.05). Red lines indicate the risk associated SNP-SNP interactions between SNPs mapped to the corresponding pathways. (B) Null distribution of the SNP-SNP interaction density between the AML and SHB based on 200,000 SNP permutations. The arrow indicates the observed SNP-SNP interaction density in the BPC3 cohort. (C) Distribution of the significance of pairwise SNP-SNP interactions (-log₁₀ p-value) tested individually for SNP pairs supporting the AML-SHB interaction. The most significant SNP-SNP interaction results in an <i>FDR</i> = 0.94 after multiple hypothesis correction, suggesting that there is not sufficient power to detect SNP-SNP interactions between these pathways in this cohort.</p

Summary of discoveries across five breast cancer cohorts.

<p>Summary of discoveries across five breast cancer cohorts.</p

Pathway-level genetic interaction models.

<p>(A) Between-pathway interaction and between-pathway model. Two biological pathways share a common function necessary for maintaining a healthy state. Genetic variants in individual pathways do not result in a phenotype, but joint mutations in both pathways in the same individual results in disease. Between-pathway interactions clustering between two complementary pathways and appear are referred to as an instance of the between-pathway model (BPM). (B) Within-pathway interaction and within-pathway model. A single pathway supports a function for maintaining a healthy state. A single genetic variant does not result in a phenotype, but joint mutations in the same pathway results in the loss of function and a disease state. Within-pathway interactions clustered within the single pathway are called a within-pathway model (WPM). (C) Overview of the framework for discovering pathway-level genetic interactions from GWAS breast cancer data, leveraging the BridGE method [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006973#pgen.1006973.ref023" target="_blank">23</a>].</p

Consensus interactions with the glutathione conjugation pathway.

<p>(A) Gene interaction degree (fold enrichment) of all glutathione conjugation genes in the three cohorts that support a PATH interaction for the glutathione conjugation pathway (LAT517, CHN799 and JPN517). (B) Between pathway interactions associated with glutathione conjugation that are significant (<i>FDR</i> ≤ 0.25) in both LAT517 and CHN799 datasets. The red edges indicate they are all associated with increased risk of breast cancer. (C) Detailed statistics for the between pathway interactions shown in (B).</p

Network view of the between-pathway interactions (BPM) from the consensus analysis.

<p>All BPMs satisfying a geometric mean p ≤ 5.0 × 10⁻⁴ threshold from consensus analysis are plotted. Red edges indicate interactions associated with increased breast cancer risk while green edges indicate interactions associated with decreased risk. Node size is proportional to the number of BPMs connected to each pathway. Several of the highly connected pathways are labeled by numbers, and their corresponding pathway names are listed. The complete information for these pathways can be found in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006973#pgen.1006973.s005" target="_blank">S5 Table</a>.</p

Consensus summary of pathway-level interactions discovered from the 6 GWAS breast cancer cohorts.

<p>(A) Network view of the most significant between-pathway interactions (BPM) (geometric mean p ≤ 5.0 × 10⁻⁵) that are supported by at least two cohorts. The supporting cohorts are indicated by the edge labels. (B) List of all within-pathway interactions (WPM) and hub pathways (PATH) that are most significant (geometric mean p ≤ 5.0 × 10⁻³) and supported by at least two cohorts.</p