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Francois Aguet on Muck Rack

Francois Aguet

As seen in: Nature, PLOS, Genome Biology, Science Magazine, BioRxiv, Cell Press, The American Journal of Human Genetics, Texas Medical Center
Computational Biologist | Head of computational biology @PredictaBiosci | Previously group leader @illumina AI lab and @broadinstitute
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Articles

Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies

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By Xiaowei Hu, Daniel Araújo, Chachrit Khunsriraksakul, Lida Wang, Quan Sun, Jia Wen, Lingbo Zhou, Leslie Lange, Ethan Lange, Stephen Montgomery, Francois Aguet, Kristin G. Ardlie, Tuuli Lappalainen, Christopher R. Gignoux, Esteban G. Burchard, Kent Taylor, Xiuqing Guo, Jerome I. Rotter, Stephen S. Rich, Peter Jon Durda, Russell P. Tracy, Yongmei Liu, W. Craig Johnson, George Papanicolaou, Minoli A. Perera, Michael Cho, Dajiang J Liu, Laura M. Raffield, Yun Li Verified, Heather Wheeler, Hae Kyung Im, Ani W Manichaikul, Lynette Ekunwe, Elaine Cornell
| The American Journal of Human Genetics Verified
Keywords transcriptome prediction models transcriptome-wide association study functional annotation multi-ancestry peripheral blood mononuclear cells PBMCs RNA-seq omnibus approach Introduction Although large-scale genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex human diseases or traits, the inference from identified genetic variants to candidate genes remains challenging.
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github.com/broadinstitute/warp/tensorqtl_cis_permutations

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By Francois Aguet
| zenodo.org
The Warp Analysis Research Pipelines (WARP) repository is a collection of cloud-optimized pipelines for processing biological data from the Broad Institute Data Sciences Platform and collaborators. WARP provides robust, standardized data analysis for the Broad Institute Genomics Platform and large consortia like the Human Cell Atlas and the BRAIN Initiative. WARP pipelines are rigorously scientifically validated, high scale, reproducible and open source, released under the BSD 3-Clause license.
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Functional dissection of complex trait variants at single-nucleotide resolution - Nature

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By Layla Siraj, Rodrigo Castro, Hannah Dewey, Susan Kales, John C. Butts, Thanh Nguyen, Masahiro Kanai, Kousuke Mouri, Qingbo Wang, Petko Fiziev, Kristin Tsuo, Zachary R McCaw, Sager J. Gosai, Francois Aguet, Ran Ji Cui, Irfahan Kassam, Jeremy F McRae, Christopher M. Vockley, Caleb A Lareau, Sergey A. Abramov, Jeff Vierstra, Yukinori Okada, Alexander Gusev, Thouis R Jones, Eric S. Lander, Pardis Sabeti, Hilary Finucane, Steven Reilly, Jacob C. Ulirsch, Ryan Tewhey, Daniel Berenzy, Alexandr Boystov
| Nature Verified
Abstract Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics1,2,3,4,5. Most of these variants individually have weak effects6 and lie in non-coding gene-regulatory elements7,8,9,10, for which we lack a complete understanding of how single-nucleotide alterations modulate transcriptional processes to affect human phenotypes5,11,12,13,14,15.
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