Decoding disease: from genomes to networks to phenotypes.

TitleDecoding disease: from genomes to networks to phenotypes.
Publication TypeJournal Article
Year of Publication2021
AuthorsWong, AK, Sealfon, RSG, Theesfeld, CL, Troyanskaya, OG
JournalNat Rev Genet
Date Published2021 Dec
KeywordsEpigenomics, Gene Expression, Gene Regulatory Networks, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Humans, Machine Learning, Models, Genetic, Mutation, Phenotype

<p>Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.</p>

Alternate JournalNat Rev Genet
PubMed ID34341555
PubMed Central ID6786975
Grant ListU24 DK100845 / DK / NIDDK NIH HHS / United States
UG3 DK114907 / DK / NIDDK NIH HHS / United States
U2C DK114886 / DK / NIDDK NIH HHS / United States
UH3 TR002158 / TR / NCATS NIH HHS / United States