This project focused a new study type to understand the basis of complex genetic traits, a functional genome-wide association study (fGWAS). Most experimental designs, relying solely on linear models and genetic information to predict phenotypes, fail to recover the full range of predictability of a trait. By combining extensive well-controlled cellular data with novel integrative computational models, this team sought to find a large chunk of the missing heritability of multiple complex traits. With these contributions, the team then worked to capture the broad-sense heritability that is missed by linear models that rely solely on genotype and markers acting individually.
This new study type focused on making advances along two fronts by measuring and integrating fine-grained cellular measurements into genotype-phenotype models:
(1) Integrative models that use cellular measurements to prioritize particular genetic variants and interactions, leading to more effective multiple hypothesis controls and better predictions
(2) Cellular measurements, interpreted as biomarkers, will be used directly to improve prediction of phenotypes
Key milestones: (1) developing novel computational methods that link genotype to phenotype using functional information in Functional Genome Wide Association Studies, and (2) characterizing natural human genetic variation using new computational methods.
David Gifford, CSAIL
Tommi Jaakkola, CSAIL
Halima Bensmail, QCRI
Reda Rawi, QCRI