PROJECT SUMMARY
Although we can readily determine a patient's genotype, we often cannot accurately predict their risk for
disease or ascertain which of many variants of uncertain significance might underlie a pathology. Indeed,
medically relevant phenotypes may emerge from the combination of thousands of polymorphisms. Complicating
matters, the effects of genetic variants are not constant across individuals due to interactions with other variants
in the genome and the environment. This project aims to build a fundamental understanding of which genetic
variants give rise to complex traits and why.
To do so, we will exploit a unique model system in the budding yeast Saccharomyces cerevisiae, in which
we have already identified thousands of nucleotides that determine complex traits. These include regulatory
variants that likely influence gene expression and many synonymous variants that, although often regarded as
'silent,' make substantial contributions to phenotype. Reversing typical functional genomics paradigms, we will
examine the molecular consequences of known causal variants to identify the signatures that make them
important to complex traits. We will focus on ascertaining the predictive power of functional measurements (such
as nucleosome position, histone modification, gene expression level, and protein abundance) as a guide to the
application of these technologies to patient- and tissue-specific genomics. In addition to examining these
molecularly diverse linear contributors to phenotype, we will take advantage of a powerful genetic mapping panel
(which contains more individuals than segregating polymorphisms) to begin dissecting the functional basis of
gene ´ environment interactions and genetic background effects in complex traits.
To chart this atlas of functionally important genetic variation, we will undertake the following specific aims:
1. Define the molecular impact of functional synonymous variants
2. Identify signatures of functional regulatory variants
3. Build integrative genotype-to-molecule-to-phenotype maps
The inherent complexity of quantitative traits is a daunting problem that grows ever-more challenging with
the growing catalog of variants of uncertain significance in the patient population. Using model systems in which
the genotype-to-phenotype relationship can be comprehensively mapped is a powerful approach for
understanding and building predictive models of which variants are likely to be causal. Indeed, linking changes
in DNA both to their molecular consequences and their effects on cellular phenotypes is a central challenge in
genetics that promises to allow the functional classification of never-before-seen mutations. Our approach will
help to understand the fundamental structure of these relationships, with implications for genome reading and
writing in medicine and biotechnology.