Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes
This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex. One of the most important problems in biology and medicine is to identify the genetic mutations that affect human traits such as blood pressure, longevity, and onset of disease. Currently, large scientific teams are examining the genomes of thousands of people in an attempt to find mutations present only in individuals with certain traits. Until now, mutations have been largely examined in isolation, without regard to how they work together inside the cell. However, large pathway maps are now available which describe in detail the network of genes and proteins that underlies cell function. Here we show how to take advantage of these pathway maps to better identify relevant mutations and to show how these mutations work mechanistically. This basic approach of combining genetic information with known maps of the cell will have wide-ranging applications in understanding and treating disease.