Relating enhancer genetic variation across mammals to complex phenotypes using machine learning
Authors: Kaplow Irene M. , Lawler Alyssa J. , Schäffer Daniel E. , Srinivasan Chaitanya , Sestili Heather H. , Wirthlin Morgan E. , Phan BaDoi N. , Prasad Kavya , Brown Ashley R. , Zhang Xiaomeng , Foley Kathleen , Genereux Diane P. ,   Zoonomia Consortium**, Karlsson Elinor K. , Lindblad-Toh Kerstin , Meyer Wynn K. , Pfenning Andreas R. , Andrews Gregory , Armstrong Joel C. , Bianchi Matteo , Birren Bruce W. , Bredemeyer Kevin R. , Breit Ana M. , Christmas Matthew J. , Clawson Hiram , Damas Joana , Di Palma Federica , Diekhans Mark , Dong Michael X. , Eizirik Eduardo , Fan Kaili , Fanter Cornelia , Foley Nicole M. , Forsberg-Nilsson Karin , Garcia Carlos J. , Gatesy John , Gazal Steven , Genereux Diane P. , Goodman Linda , Grimshaw Jenna , Halsey Michaela K. , Harris Andrew J. , Hickey Glenn , Hiller Michael , Hindle Allyson G. , Hubley Robert M. , Hughes Graham M. , Johnson Jeremy , Juan David , Kaplow Irene M. , Karlsson Elinor K. , Keough Kathleen C. , Kirilenko Bogdan , Koepfli Klaus-Peter , Korstian Jennifer M. , Kowalczyk Amanda , Kozyrev Sergey V. , Lawler Alyssa J. , Lawless Colleen , Lehmann Thomas , Levesque Danielle L. , Lewin Harris A. , Li Xue , Lind Abigail , Lindblad-Toh Kerstin , Mackay-Smith Ava , Marinescu Voichita D. , Marques-Bonet Tomas , Mason Victor C. , Meadows Jennifer R. S. , Meyer Wynn K. , Moore Jill E. , Moreira Lucas R. , Moreno-Santillan Diana D. , Morrill Kathleen M. , Muntané Gerard , Murphy William J. , Navarro Arcadi , Nweeia Martin , Ortmann Sylvia , Osmanski Austin , Paten Benedict , Paulat Nicole S. , Pfenning Andreas R. , Phan BaDoi N. , Pollard Katherine S. , Pratt Henry E. , Ray David A. , Reilly Steven K. , Rosen Jeb R. , Ruf Irina , Ryan Louise , Ryder Oliver A. , Sabeti Pardis C. , Schäffer Daniel E. , Serres Aitor , Shapiro Beth , Smit Arian F. A. , Springer Mark , Srinivasan Chaitanya , Steiner Cynthia , Storer Jessica M. , Sullivan Kevin A. M. , Sullivan Patrick F. , Sundström Elisabeth , Supple Megan A. , Swofford Ross , Talbot Joy-El , Teeling Emma , Turner-Maier Jason , Valenzuela Alejandro , Wagner Franziska , Wallerman Ola , Wang Chao , Wang Juehan , Weng Zhiping , Wilder Aryn P. , Wirthlin Morgan E. , Xue James R. , Zhang Xiaomeng 
Published: April 2023

Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challenging because enhancer activity can be tissue-dependent and functionally conserved despite low sequence conservation. We developed the Tissue-Aware Conservation Inference Toolkit (TACIT) to associate candidate enhancers with species’ phenotypes using predictions from machine learning models trained on specific tissues. Applying TACIT to associate motor cortex and parvalbumin-positive interneuron enhancers with neurological phenotypes revealed dozens of enhancer–phenotype associations, including brain size–associated enhancers that interact with genes implicated in microcephaly or macrocephaly. TACIT provides a foundation for identifying enhancers associated with the evolution of any convergently evolved phenotype in any large group of species with aligned genomes.