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Nature
Bioinformatics AI
Sanan Venkatesh, Roman Kosoy, Zhenyi Wu, Marios Anyfantakis, Christian Dillard, Prashant N. M., David Burstein, Deepika Mathur, Chris Chatzinakos, Bukola Ajanaku, Fotis Tsetsos, Biao Zeng, Sonali Gupta, Rachel Bercovitch, Aram Hong, Clara Casey, Marcela Alvia, Zhiping Shao, Stathis Argyriou, Karen Therrien, Athan Z. Li, Chenfeng He, Chirag Gupta, Christian Porras, Colleen A. McClung, Collin Spencer, Daifeng Wang, David A. Bennett, Fotios Tsetsos, Gennadi Ryan, Hui Yang, Jaroslav Bendl, Jennifer Monteiro Fortes, Jerome J. Choi, Kalpana H. Arachchilage, Lars J. Jensen, Lisa L. Barnes, Logan C. Dumitrescu, Lyra Sheu, Madeline R. Scott, Marios Anyfantakis, Maxim Signaevsky, Mikaela Koutrouli, Milos Pjanic, Monika Ahirwar, Nicolas Y. Masse, Noah Cohen Kalafut, Pavan K. Auluck, Pavel Katsel, Pengfei Dong, Pramod B. Chandrashekar, Sanan Venkatesh, Saniya Khullar, Sarah R. Murphy, Sayali A. Alatkar, Seon Kinrot, Steven Finkbeiner, Steven P. Kleopoulos, Tereza Clarence, Timothy J. Hohman, Ting Jin, Vahram Haroutunian, Vivek G. Ramaswamy, Xiang Huang, Xinyi Wang, Zhenyi Wu, Tim Bigdeli, Pavan Auluck, David A. Bennett, Stefano Marenco, Vahram Haroutunian, Kiran Girdhar, Jaroslav Bendl, Donghoon Lee, John F. Fullard, Gabriel E. Hoffman, Georgios Voloudakis, Panos Roussos
2026-09-23
TL;DR - This study uses single-nucleus transcriptomics across diverse populations to map genetic risk for human brain disorders to specific cell types. It matters because cell-type-level analysis reveals gene–trait associations and conserved mechanisms that bulk-tissue studies may miss.
- Integrates transcriptome-wide association analysis with single-nucleus data.
- Examines how disorder-associated genetic variation affects particular brain cell types.
- Includes diverse populations, supporting broader investigation of shared and population-specific effects.
- Identifies previously hidden associations and conserved cell-type-specific mechanisms.
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