Completion of the human genome project has resulted in rapid advances in technology as well as a deluge of
genomic data. This tremendous accomplishment has sparked an incredible international community effort to
catalog human genetic variation and to relate this variation to human phenotypes with the ultimate payoff of
more personalized medicine. While the latest technologies provide unprecedented ability to conduct genome-
wide association studies (GWAS) to identify individual susceptibility loci for a given human disease, GWAS are
underpowered (due to the multiple hypotheses testing problem) to screen for the multiple gene-gene
interactions conferring susceptibility in humans. To overcome this limitation, in this application, we propose a
novel, cross-species (yeast-to-human) comparative systems genetics strategy to identify gene-gene and
pathway-pathway interactions underlying human breast cancer susceptibility. Specifically, we hypothesize
that cellular sensitivity to DNA damage can be used as an intermediate phenotype for breast cancer
susceptibility, and that genes and pathways that synergize with defects in the DNA damage response pathway
will also synergize to produce breast cancer susceptibility in humans. In Aim 1, we will leverage existing and
emerging data from our genome-wide screens in yeast (R01 CA 129604-01A1 Phenotype-based approach to
find gene interactions underlying breast cancer risk; PI: Paulovich) to identify gene-gene interactions likely to
underlie susceptibility for breast cancer in humans. Putative human orthologs of interacting yeast genes will be
identified using sophisticated data analysis tools. An integrative genomics analysis will then be used to further
prioritize gene pairs with high probability of contributing to breast cancer susceptibility based on genomics
datasets and networks derived from human breast cancers. Although model organisms can be genetically
altered and their environments manipulated to test predictions about contributions of specific gene variants to
risk, there are limitations of single or multiple gene knockout or mutant approaches as the sole means to test
predictions; hence in Aim 2 we will test the functional significance in human mammary epithelial cells (HMEC)
of synthetic or synergistic gene-gene interactions discovered in yeast and prioritized in Aim 1. In Aim 3, gene-
gene interactions functionally verified in HMEC will be tested for association with breast cancer susceptibility
using existing and emerging GWAS datasets on human breast cancer. The premise of using sensitivity to DNA
damage as an intermediate phenotype is reasonable given the abundance of evidence that defects in this
pathway cause germline predisposition to breast cancer. This work will complement and extend the current
GWAS studies since significant increases in risk may only be apparent when variant alleles are considered in
combination (epistasis), and hence these risk alleles will be frequently missed in GWA studies.
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