The goal of this NIH Pathway to Independence award is to provide Dr. Brittany Lasseigne with an extensive
training program to prepare her to be an effective independent investigator who uses computational genomics
to study complex human diseases. We propose a formal one-year training and mentoring program in
genomics, computer science, statistics, and career development to build on her 8+ years of hands-on training,
followed by a three-year structured and independent research program. Research will focus on the integration
of multidimensional genomic data sets in the context of complex human diseases. A critical barrier in genomic
research is the complexity of data integration: the ability to leverage overlapping and unique information
captured by different genomic assays would improve our understanding of data integration and generate
clinically relevant genomic signatures. To meet this need, we propose to integrate a combination of genomic
data we generated with public data to (1) infer genomic instability signatures from different data types, (2)
improve clinically relevant phenotype prediction by building multi-omics machine learning classifiers and
reducing phenotype heterogeneity, and (3) create a cloud-enabled R package and associated Shiny
application to accelerate future research. The proposed work will advance our understanding of data
integration, allow inference of genomic instabilities across data sets, and generate high performance classifiers
for assessing clinically relevant phenotypes in both cancer and psychiatric disease using frameworks that will
be broadly applicable across other complex diseases. It will also facilitate prioritization of experiments in future
studies by informing on the orthogonality of genomic assays, thereby allowing more efficient study designs to
capture as much information as possible within a given sample size or scope of experimentation. Collectively,
this additional training will allow Dr. Lasseigne to develop new multidimensional data integration approaches
and translational questions applicable across complex diseases when independent. Dr. Richard Myers
(HudsonAlpha) and Dr. Gregory Cooper (HudsonAlpha), leaders in applying genetics and genomics to
complex human diseases, and an Advisory Committee of additional experts including Dr. Barbara Wold
(Caltech), Dr. Eddy Yang (UAB), and Dr. Timothy Reddy (Duke), will provide mentoring throughout this award.
The mentored phase will take place at the HudsonAlpha Institute for Biotechnology, an ideal environment for
this training with extensive translational science collaborations, expert faculty and staff, and state-of-the art
computational and laboratory resources devoted to genomics. This combination will maximize Dr. Lasseigne's
training program, facilitating her transition to an independent, tenure-track investigator at a university with a
strong commitment to data-driven approaches to complex human disease research, i.e. strong genomics
research programs with clinical collaborators, ideally at, or affiliated with, an academic medical center.
