The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

Kent A Shefchek
Nomi L Harris
Michael Gargano, The Jackson Laboratory
Nicolas Matentzoglu
Deepak Unni
Matthew Brush
Daniel Keith
Tom Conlin
Nicole Vasilevsky
Xingmin Aaron Zhang, The Jackson Laboratory
James P Balhoff
Larry Babb
Susan M. Bello, The Jackson Laboratory
Hannah Blau, The Jackson Laboratory
Yvonne Bradford
Seth Carbon
Leigh Carmody, The Jackson Laboratory
Lauren E Chan
Valentina Cipriani
Alayne Cuzick
Maria D Rocca
Nathan Dunn
Shahim Essaid
Petra Fey
Chris Grove
Jean-Phillipe Gourdine
Ada Hamosh
Midori Harris
Ingo Helbig
Maureen Hoatlin
Marcin Joachimiak
Simon Jupp
Kenneth B Lett
Suzanna E Lewis
Craig McNamara
Zoë M Pendlington
Clare Pilgrim
Tim Putman
Vida Ravanmehr, The Jackson Laboratory
Justin Reese
Erin Riggs
Sofia Robb
Paola Roncaglia
James Seager
Erik Segerdell
Morgan Similuk
Andrea L Storm
Courtney Thaxon
Anne Thessen
Julius O B Jacobsen
Julie A McMurry
Tudor Groza
Sebastian Köhler
Damian Smedley
Peter N Robinson, The Jackson Laboratory
Christopher J Mungall
Melissa A Haendel
Monica C Munoz-Torres
David Osumi-Sutherland


In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative ( integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.