PT  - JOURNAL ARTICLE
AU  - Irene M. Kaplow
AU  - Alyssa J. Lawler
AU  - Daniel E. Schäffer
AU  - Chaitanya Srinivasan
AU  - Morgan E. Wirthlin
AU  - BaDoi N. Phan
AU  - Xiaomeng Zhang
AU  - Kathleen Foley
AU  - Kavya Prasad
AU  - Ashley R. Brown
AU  - Zoonomia Consortium
AU  - Wynn K. Meyer
AU  - Andreas R. Pfenning
TI  - Relating enhancer genetic variation across mammals to complex phenotypes using machine learning
AID  - 10.1101/2022.08.26.505436
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.08.26.505436
4099  - http://biorxiv.org/content/early/2022/08/26/2022.08.26.505436.short
4100  - http://biorxiv.org/content/early/2022/08/26/2022.08.26.505436.full
AB  - Protein-coding differences between mammals often fail to explain phenotypic diversity, suggesting involvement of enhancers, often rapidly evolving regions that regulate gene expression. Identifying associations between enhancers and phenotypes is challenging because enhancer activity is context-dependent and may be conserved without much sequence conservation. We developed TACIT (Tissue-Aware Conservation Inference Toolkit) to associate open chromatin regions (OCRs) with phenotypes using predictions in hundreds of mammalian genomes from machine learning models trained to learn tissue-specific regulatory codes. Applying TACIT for motor cortex and parvalbumin-positive interneurons to neurological phenotypes revealed dozens of new OCR-phenotype associations. Many associated OCRs were near relevant genes, including brain size-associated OCRs near genes mutated in microcephaly or macrocephaly. Our work creates a forward genomics foundation for identifying candidate enhancers associated with phenotype evolution.One Sentence Summary A new machine learning-based approach associates enhancers with the evolution of brain size and behavior across mammals.Competing Interest StatementThe authors have declared no competing interest.