科学家利用机器学习将哺乳动物的增强子遗传变异与复杂的表型联系起来
2023.05.11美国卡内基梅隆大学Andreas R. Pfenning等研究人员合作利用机器学习将哺乳动物的增强子遗传变异与复杂的表型联系起来。这一研究成果发表在2023年4月28日出版的国际学术期刊《科学》上。
研究人员开发了Tissue-Aware Conservation Inference Toolkit（TACIT），利用在特定组织上训练的机器学习模型的预测，将候选增强子与物种的表型联系起来。应用TACIT将运动皮层和小清蛋白阳性的神经元增强子与神经系统表型联系起来，研究人员发现了几十个增强子与表型的联系，包括与大脑大小相关的增强子，它们与小头畸形或大头畸形的基因相互影响。TACIT提供了一个基础，可用于识别与任何具有对齐基因组的大类群物种中任何趋同演化表型的演化相关增强子。
据了解，物种之间的蛋白质编码差异往往不能解释表型的多样性，这表明调节基因表达的基因组元件，如增强子的参与。识别增强子和表型之间的联系是具有挑战性的，因为增强子的活动可以是组织依赖性的，而且尽管序列保守性低，但功能保守。
附：英文原文
Title: Relating enhancer genetic variation across mammals to complex phenotypes using machine learning
Author: Irene M. Kaplow, Alyssa J. Lawler, Daniel E. Schffer, Chaitanya Srinivasan, Heather H. Sestili, Morgan E. Wirthlin, BaDoi N. Phan, Kavya Prasad, Ashley R. Brown, Xiaomeng Zhang, Kathleen Foley, Diane P. Genereux, Zoonomia Consortium**, Elinor K. Karlsson, Kerstin Lindblad-Toh, Wynn K. Meyer, Andreas R. Pfenning, Gregory Andrews, Joel C. Armstrong, Matteo Bianchi, Bruce W. Birren, Kevin R. Bredemeyer, Ana M. Breit, Matthew J. Christmas, Hiram Clawson, Joana Damas, Federica Di Palma, Mark Diekhans, Michael X. Dong, Eduardo Eizirik, Kaili Fan, Cornelia Fanter, Nicole M. Foley, Karin Forsberg-Nilsson, Carlos J. Garcia, John Gatesy, Steven Gazal, Diane P. Genereux, Linda Goodman, Jenna Grimshaw, Michaela K. Halsey, Andrew J. Harris, Glenn Hickey, Michael Hiller, Allyson G. Hindle, Robert M. Hubley, Graham M. Hughes, Jeremy Johnson, David Juan, Irene M. Kaplow, Elinor K. Karlsson, Kathleen C. Keough, Bogdan Kirilenko, Klaus-Peter Koepfli, Jennifer M. Korstian, Amanda Kowalczyk, Sergey V. Kozyrev, Alyssa J. Lawler, Colleen Lawless, Thomas Lehmann, Danielle L. Levesque, Harris A. Lewin, Xue Li, Abigail Lind, Kerstin Lindblad-Toh, Ava Mackay-Smith, Voichita D. Marinescu, Tomas Marques-Bonet, Victor C. Mason, Jennifer R. S. Meadows, Wynn K. Meyer, Jill E. Moore, Lucas R. Moreira, Diana D. Moreno-Santillan, Kathleen M. Morrill, Gerard Muntané, William J. Murphy, Arcadi Navarro, Martin Nweeia, Sylvia Ortmann, Austin Osmanski, Benedict Paten, Nicole S. Paulat, Andreas R. Pfenning, BaDoi N. Phan, Katherine S. Pollard, Henry E. Pratt, David A. Ray, Steven K. Reilly, Jeb R. Rosen, Irina Ruf, Louise Ryan, Oliver A. Ryder, Pardis C. Sabeti, Daniel E. Schffer, Aitor Serres, Beth Shapiro, Arian F. A. Smit, Mark Springer, Chaitanya Srinivasan, Cynthia Steiner, Jessica M. Storer, Kevin A. M. Sullivan, Patrick F. Sullivan, Elisabeth Sundstrm, Megan A. Supple, Ross Swofford, Joy-El Talbot, Emma Teeling, Jason Turner-Maier, Alejandro Valenzuela, Franziska Wagner, Ola Wallerman, Chao Wang, Juehan Wang, Zhiping Weng, Aryn P. Wilder, Morgan E. Wirthlin, James R. Xue, Xiaomeng Zhang
Issue&Volume: 2023-04-28
Abstract: Protein-coding differences between species often fail to explain phenotypic persity, 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.
DOI: abm7993
