Published in Nature, 2017
Taru Tukiainen, Alexandra-Chloe Villani, Angela Yen, Manuel Rivas, Jamie Marshall, Rahul Satija, Matt Aguirre, Laura Gauthier, Mark Fleharty, Andrew Kirby, et. al., "Landscape of X chromosome inactivation across human tissues." Nature, 2017. https://doi.org/10.1038/nature24265
Published in Bioinformatics, 2018
Gregory McInnes, Yosuke Tanigawa, Chris DeBoever, Adam Lavertu, Julia Olivieri, Matthew Aguirre, Manuel Rivas, "Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics." Bioinformatics, 2018. https://doi.org/10.1093/bioinformatics/bty999
Published in Molecular psychiatry, 2019
Douglas Ruderfer, Colin Walsh, Matthew Aguirre, Yosuke Tanigawa, Jessica Ribeiro, Joseph Franklin, Manuel Rivas, "Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide." Molecular psychiatry, 2019. https://doi.org/10.1038/s41380-018-0326-8
Published in The American Journal of Human Genetics, 2019
Matthew Aguirre, Manuel Rivas, James Priest, "Phenome-wide Burden of Copy-Number Variation in the UK Biobank." The American Journal of Human Genetics, 2019. https://www.sciencedirect.com/science/article/pii/S0002929719302642
Published in Nature Communications, 2019
Yosuke Tanigawa, Jiehan Li, Johanne Justesen, Heiko Horn, Matthew Aguirre, Christopher DeBoever, Chris Chang, Balasubramanian Narasimhan, Kasper Lage, Trevor Hastie, et. al., "Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology." Nature Communications, 2019. https://doi.org/10.1038/s41467-019-11953-9
Published in The American Journal of Human Genetics, 2020
Christopher DeBoever, Yosuke Tanigawa, Matthew Aguirre, Greg McInnes, Adam Lavertu, Manuel Rivas, "Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases." The American Journal of Human Genetics, 2020. https://www.sciencedirect.com/science/article/pii/S0002929720300835
Published in PLoS genetics, 2020
Junyang Qian, Yosuke Tanigawa, Wenfei Du, Matthew Aguirre, Chris Chang, Robert Tibshirani, Manuel Rivas, Trevor Hastie, "A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank." PLoS genetics, 2020. https://doi.org/10.1371/journal.pgen.1009141
Published in arXiv preprint arXiv:2011.02081, 2020
Matthew Aguirre, Jan Sokol, Guhan Venkataraman, Alexander Ioannidis, "A deep learning classifier for local ancestry inference." arXiv preprint arXiv:2011.02081, 2020. https://arxiv.org/abs/2011.02081
Published in PLoS genetics, 2020
Catherine Tcheandjieu, Matthew Aguirre, Stefan Gustafsson, Priyanka Saha, Praneetha Potiny, Melissa Haendel, Erik Ingelsson, Manuel Rivas, James Priest, "A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population." PLoS genetics, 2020. https://doi.org/10.1371/journal.pgen.1008802
Published in Nature Genetics, 2021
Nasa Sinnott-Armstrong, Yosuke Tanigawa, David Amar, Nina Mars, Christian Benner, Matthew Aguirre, Guhan Venkataraman, Michael Wainberg, Hanna Ollila, Tuomo Kiiskinen, et. al., "Genetics of 35 blood and urine biomarkers in the UK Biobank." Nature Genetics, 2021. https://doi.org/10.1038/s41588-020-00757-z
Published in European Journal of Human Genetics, 2021
Matthew Aguirre, Yosuke Tanigawa, Guhan Venkataraman, Rob Tibshirani, Trevor Hastie, Manuel Rivas, "Polygenic risk modeling with latent trait-related genetic components." European Journal of Human Genetics, 2021. https://doi.org/10.1038/s41431-021-00813-0
Published in medRxiv, 2021
Mengyao Yu, Andrew Harper, Matthew Aguirre, Maurren Pittman, Dulguun Amgalan, Christopher Grace, Anuj Goel, Martin Farrall, Ke Xiao, Jesse Engreitz, et. al., "Genetic determinants of interventricular septal anatomy and the risk of ventricular septal defects and hypertrophic cardiomyopathy." medRxiv, 2021. https://doi.org/10.1101/2021.04.19.21255650
Published in The American Journal of Human Genetics, 2021
Guhan Venkataraman, Christopher DeBoever, Yosuke Tanigawa, Matthew Aguirre, Alexander Ioannidis, Hakhamanesh Mostafavi, Chris Spencer, Timothy Poterba, Carlos Bustamante, Mark Daly, Matti Pirinen, Manuel Rivas, "Bayesian model comparison for rare-variant association studies." The American Journal of Human Genetics, 2021. https://www.sciencedirect.com/science/article/pii/S0002929721004171
Published in The American Journal of Human Genetics, 2022
Craig Smail, Nicole Ferraro, Qin Hui, Matthew Durrant, Matthew Aguirre, Yosuke Tanigawa, Marissa Keever-Keigher, Abhiram Rao, Johanne Justesen, Xin Li, Michael Gloudemans, Themistocles Assimes, Charles Kooperberg, Alexander Reiner, Jie Huang, Christopher O'Donnell, Yan Sun, Manuel Rivas, Stephen Montgomery, "Integration of rare expression outlier-associated variants improves polygenic risk prediction." The American Journal of Human Genetics, 2022. https://www.sciencedirect.com/science/article/pii/S000292972200163X
Published in Circulation: Genomic and Precision Medicine, 2023
Mengyao Yu, Andrew R Harper, Matthew Aguirre, Maureen Pittman, Catherine Tcheandjieu, Dulguun Amgalan, Christopher Grace, Anuj Goel, Martin Farrall, Ke Xiao, Jesse Engreitz, Katherine S Pollard, Hugh Watkins, James R Priest, "Genetic Determinants of the Interventricular Septum Are Linked to Ventricular Septal Defects and Hypertrophic Cardiomyopathy" Circulation: Genomic and Precision Medicine, 2023. https://www.ahajournals.org/doi/abs/10.1161/CIRCGEN.122.003708
Published in Circulation: Genomic and Precision Medicine, 2023
Mengyao Yu, Matthew Aguirre, Meiwen Jia, Ketrin Gjoni, Aldo Cordova-Palomera, Chad Munger, Dulguun Amgalan, X Rosa Ma, Alexandre Pereira, Catherine Tcheandjieu, Christine Seidman, Jonathan Seidman, Martin Tristani-Firouzi, Wendy Chung, Elizabeth Goldmuntz, Deepak Srivastava, Ruth JF Loos, Nathalie Chami, Heather Cordell, Martina Dreßen, Bertram Mueller-Myhsok, Harald Lahm, Markus Krane, Katherine S Pollard, Jesse M Engreitz, Sarah A Gagliano Taliun, Bruce D Gelb, James R Priest, "Oligogenic Architecture of Rare Noncoding Variants Distinguishes 4 Congenital Heart Disease Phenotypes" Circulation: Genomic and Precision Medicine, 2023. https://www.ahajournals.org/doi/abs/10.1161/CIRCGEN.122.003968
Published in bioRxiv, 2023
Marianne K DeGorter, Pagé C Goddard, Emre Karakoc, Soumya Kundu, Stephanie M Yan, Daniel Nachun, Nathan Abell, Matthew Aguirre, Tommy Carstensen, Ziwei Chen, Matthew Durrant, Vikranth R Dwaracherla, Karen Feng, Michael J Gloudemans, Naiomi Hunter, Mohana PS Moorthy, Cristina Pomilla, Kameron B Rodrigues, Courtney J Smith, Kevin S Smith, Rachel A Ungar, Brunilda Balliu, Jacques Fellay, Paul Flicek, Paul J McLaren, Brenna Henn, Rajiv C McCoy, Lauren Sugden, Anshul Kundaje, Manjinder S Sandhu, Deepti Gurdasani, Stephen B Montgomery, "Transcriptomics and chromatin accessibility in multiple African population samples." bioRxiv, 2023. https://doi.org/10.1101%2F2023.11.04.564839
Published in PLoS ONE, 2024
Merle Behr, Karl Kumbier, Aldo Cordova-Palomera, Matthew Aguirre, Omer Ronen, Chengzhong Ye, Euan Ashley, Atul J Butte, Rima Arnaout, Ben Brown, James Priest, Bin Yu, "Learning epistatic polygenic phenotypes with Boolean interactions." PLoS ONE, 2024. https://doi.org/10.1371/journal.pone.0298906
Published in bioRxiv, 2024
Matthew Aguirre, Jeffrey P Spence, Guy Sella, Jonathan K. Pritchard, "Gene regulatory network structure informs the distribution of perturbation effects." bioRxiv, 2024. https://www.biorxiv.org/content/10.1101/2024.07.04.602130
Published:
Preliminary results from project to call copy-number variants in ~500,000 participants of the UK Biobank study. Now published in the American Journal of Human Genetics.
Published:
Talk (by application) on project to call copy-number variants in ~500,000 participants of the UK Biobank study. Now published in the American Journal of Human Genetics.
Published:
Research presentation describing the genomic landscape and medical consequences of copy-number variants called from array genotypes in ~500,000 participants of the UK Biobank study. Now published in the American Journal of Human Genetics.
Published:
Preliminary results on efforts towards a joint model of polygenic risk for multiple phenotypes, with focus on common complex diseases. Now published in the European Journal of Human Genetics.
Published:
Preliminary results towards developing a deep convolutional neural network to predict local ancestry tracts in whole-genome sequence data. Selected as a reviewer’s choice abstract (scored by reviewers in top 10% of all poster abstracts), and presented as a Poster Talk. Now posted as an arXiv preprint.
Published:
Preliminary results towards developing a deep convolutional neural network to predict local ancestry tracts in whole-genome sequence data. Presented as a poster at the “Learning Meaningful Representations of Life” (LMRL) workshop. Now posted as an arXiv preprint.
Published:
Preliminary results towards developing a deep convolutional neural network to predict local ancestry tracts in whole-genome sequence data. Presented as a poster at the “National Library of Medicine Training Informatics Conference” (NLM-ITC) hosted by the University of Washington – more information about the training conference can be found here. This project has been posted as an arXiv preprint.
Published:
Preliminary description of methods to simulate gene regulatory networks (GRNs) with realistic structure, function, and expression data, with early results with implications for GRN structure inference tasks and the statistical basis of complex trait heritability. Given as a talk to Microsoft Research via the “Microsoft Research PhD Fellowship Research Showcase”, which highlights research conducted by the Microsoft Research PhD Fellows.
Published:
Preliminary description of methods to simulate gene regulatory networks (GRNs) with realistic structure, function, and expression data, with early results to connect the statistical genetic basis of complex traits in context of GRNs. Presented as a poster at the “Population, Evolutionary, and Quantitative Genetics Conference” (PEQG 2022), hosted by the Genetics Society of America (GSA).
Published:
Preliminary description of methods to simulate gene regulatory networks (GRNs) with realistic structure, function, and expression data, with early results to connect the statistical genetic basis of complex traits in context of GRNs. Presented as a poster at the “National Library of Medicine Training Informatics Conference” (NLM-ITC) hosted by the University of Buffalo – more information about the training conference can be found here.
Published:
I attended the first short program of the Computational Genomics Summer Institute (CGSI) and gave a chalk talk describing a technique to simulate gene regulatory networks (GRNs) with realistic structure, function, and expression data.
Published:
Preliminary results on a simulation study to interrelate the structure and function of gene regulatory networks. Characterized the impact of network summary statistics on the distribution of effects from genetic and experimental perturbations.
Published:
Preliminary results on a simulation study to interrelate the structure and function of gene regulatory networks. Characterized the impact of network summary statistics on the distribution of effects from genetic and experimental perturbations.
Published:
Results on a modeling study to interrelate the structure and function of gene regulatory networks. Characterized the impact of network summary statistics on the distribution of effects from genetic and experimental perturbations, and discussed implications for inference tasks on observational and interventional data sources.
Published:
Results on a modeling study to interrelate the structure and function of gene regulatory networks. Characterized the impact of network summary statistics on the distribution of effects from genetic and experimental perturbations, and discussed implications for inference tasks on observational and interventional data sources.
Published:
Results on a modeling study to interrelate the structure and function of gene regulatory networks. Characterized the impact of network summary statistics on the distribution of effects from genetic and experimental perturbations, and discussed implications for inference tasks on observational and interventional data sources.
Published:
Results on a modeling study to interrelate the structure and function of gene regulatory networks. Characterized the impact of network summary statistics on the distribution of effects from genetic and experimental perturbations, and discussed implications for inference tasks on observational and interventional data sources.
Published:
Results on a modeling study to interrelate the structure and function of gene regulatory networks. Characterized the impact of network summary statistics on the distribution of effects from genetic and experimental perturbations, and discussed implications for inference tasks on observational and interventional data sources.
Published:
Results on a modeling study to interrelate the structure and function of gene regulatory networks. Characterized the impact of network summary statistics on the distribution of effects from genetic and experimental perturbations, and discussed implications for inference tasks on observational and interventional data sources.
Undergraduate course, Harvard University, Department of Mathematics, 2014
I was an undergraduate course assistant for Math 1a: Introduction to Calculus, with several members of the math department at Harvard. I held course office hours, graded homework, and led workshops in pre-calculus. I was on course staff from Fall 2014 to Spring 2017.
Graduate course, Stanford University, Department of Biomedical Data Science, 2020
I was a course grader for BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology, with Professor Russ Altman. I graded assignments and coordinated course logistics and feedback with the teaching team. This course is part of the core curriculum in Biomedical Informatics at Stanford. I was on course staff in Fall 2020 and Fall 2021.
Outreach, Stanford University, Department of Biomedical Data Science, 2021
I was a graduate student mentor for the Inclusive Mentoring in Data Science Program under supervision from Professor Chiara Sabatti. I held weekly mentee meetings and developed personalized data science curriculums for undergraduate and community college students from backgrounds currently underrepresented in data science. As part of the deparment course offering, I also participated in mentor meetings and seminars designed to strengthen teaching and mentorship skills. I was a program mentor in Winter 2021 (IMDS’ inaugural year), Winter 2022, and Winter 2023.
Graduate course, Stanford University, Department of Biomedical Data Science, 2021
I was a graduate teaching assistant for BIOMEDIN 217: Translational Bioinformatics with Professor Dennis Wall. I held office hours, graded coursework, and facilitated course discussion. This course was part of the core curriculum in Biomedical Informatics at Stanford. I was on course staff in Winter 2021 and Winter 2022.