Matthew Aguirre

Matthew Aguirre

PhD candidate in Biomedical Informatics at Stanford University. Just one small analyst in a big data world.

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Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

publications

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

Published in Nature Communications, 2019

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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

A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population

Published in PLoS genetics, 2020

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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

Genetic determinants of interventricular septal anatomy and the risk of ventricular septal defects and hypertrophic cardiomyopathy

Published in medRxiv, 2021

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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

Bayesian model comparison for rare-variant association studies

Published in The American Journal of Human Genetics, 2021

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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

Integration of rare expression outlier-associated variants improves polygenic risk prediction

Published in The American Journal of Human Genetics, 2022

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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

Genetic Determinants of the Interventricular Septum Are Linked to Ventricular Septal Defects and Hypertrophic Cardiomyopathy

Published in Circulation: Genomic and Precision Medicine, 2023

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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

Oligogenic Architecture of Rare Noncoding Variants Distinguishes 4 Congenital Heart Disease Phenotypes

Published in Circulation: Genomic and Precision Medicine, 2023

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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

talks

A deep learning classifier for local ancestry inference

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.

A deep learning classifier for local ancestry inference

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.

Simulating realistic gene regulatory networks and expression data

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.

Simulating systemic effects of expression quantitative trait loci across gene regulatory networks

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).

Modeling quantitative trait locus effects through gene regulatory networks

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.

teaching

Math 1a: Introduction to Calculus

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.

BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology

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.

BIODS 360: Inclusive Mentoring in Data Science

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.

BIOMEDIN 217: Translational Bioinformatics

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.