Brian D. Athey, Ph.D. is the Michael A. Savageau Collegiate Professor and Founding Chair of the Department of Computational Medicine and Bioinformatics (DCMB) at the University of Michigan (UM) Medical School (UMMS); where he also is a Professor of Psychiatry. He also serves as an AI advisor to the EVP/Dean of Michigan Medicine, encompassing the MM Hospitals and Clinical Network and the U-M Medical School.

He recently served as elected chair of the UM Medical School Endowment for the Basic Sciences (EBS), coordinating the 10 academic Basic Science departments at UMMS. He has served as Michigan Medicine Chief Information Officer (CIO); and the Medical School Academic Informatics lead (Assoc. Dean equivalent). In addition, he served as co-founder and co-director of the UM-wide Michigan Institute for Data Science (MIDAS) from 2015-2018. Brian is a founding member of the Peking University Health Sciences Center (PUHSC) - UM Joint Institute (JI) faculty; and he served as its first co-Director of its Biomedical Informatics Core. Dr. Athey served as lead life and medical sciences consultant to the Chinese University Hong Kong-Shenzhen (CUHK-SZ) from 2015-2020. Brian is an elected fellow of the American College of Medical Informatics (FACMI) and to the American Association for the Advancement of Science (FAAAS). He was awarded the Peace Fellowship from the Federation of American Scientists (FAS.org) in 2005, for his work with DARPA to combat biological terrorism.

Dr. Athey has more than 30 years of experience in trans-disciplinary team science-based leadership experience as overall Principal Investigator of national biomedical informatics and computational sciences centers and consortia. These include the National Library of Medicine (NLM) Visible Human Project, the DARPA Telepathology and Virtual Solider Projects, and the NIH National Center for Integrative Biomedical Informatics (NCIBI). He also served as Director of the Michigan Center for Biological Information, the first State of Michigan wide software and data resource. He was a founding Associate Director of the UM Michigan Institute for Clinical and Health Research (MICHR), host of the UM Clinical and Translational Sciences Award (CTSA), and a founding member of the UM Depression Center, the first of its kind in the US and Canada (which now number over 50).

Trained as a biophysicist, as a graduate student Athey proposed the double helical crossed-linker model for chromatin, now considered by most to be its correct structural motif. On chromatin, he performed the first successful and published cryo-electron microscopy experiment—now a standard technique worldwide. Athey is an expert in biomedical optics, and he shared a laboratory for 10 years with the inventor of off-axis holography, the late Professor Emmet Leith, a recipient of the US National Medal of Science. During that time, he performed the first real-time live cell 2-photon confocal imaging experiment, done in the laboratory of Nobelist Professor Gérard Mourou.

Athey currently has 1 MS student, 2 PhD students, 1 very gifted high school student, 1 senior research scientist, 1 clinical professor, and 2 full professors and one expert lab technician in his research group. He is currently an active researcher in using AI to develop new methods for understanding and treating patients who suffer from neuropsychiatric illnesses, with a focus on psychiatric and cardiovascular pharmacogenomics and AI analysis of their medication response phenotype. He has contributed to the founding of the new field of ‘pharmacoepigenomics’ and is now working on a complementary effort to establish ‘pharmacophenomics’.

He has trained or co-mentored over 16 MS, 20 PhD, and 8 postdoctoral fellows, and mentored 6 faculty K-awardees--all of whom are all enjoying successful careers in academia, industry, and government service. He has over 130 peer-reviewed papers and conference proceedings. He currently has 8 issued patents (US, Europe, Asia), with over 40 more US and international in the provisional pipeline being reviewed.

Athey has served as a deep consultant to the NIH Office of the Director & to the NIH CIO and to the Defense Advanced Research Projects Agency (DARPA). He served as Director, Biological Programs, for the Environmental Research Institute of Michigan (ERIM). He has been an advisor to over 10 biotechnology companies in the last 30 years, including SAB and corporate advisor roles. He served as Chair of the Scientific Advisory Board of Assurex Health (Mason, Ohio) from 2011-2018; this company was acquired by Myriad Genetics (Salt Lake City, UT), and now operates as Myriad Neuroscience. He is Co-founder and Chief Science and Technology Officer of Phenomics Health Inc. (Ann Arbor, MI), a company whose mission is to provide next-generation pharmacogenomics services to patients, physicians, and healthcare systems.

1. Feasibility determination of retrospective clinical validation and extension of pharmacogenomics assays using patient-specific data and analytics empowered by Machine Learning/AI platform(s)

Abstract Current prospective pharmacogenomics (PGx) clinical trials are expensive, time consuming, and have inherently low power for pharmacogenomic studies. In addition, Generation-1 SNP-based PGx genotyping assays are limited by many factors including a limited pharmacogene SNP biomarker list and the associated biomarkers assayed (set by FDA/CPIC/PharmVar); as well as by the rudimentary nature of the currently used genotype-measurement-to-reporting paradigm. This proposed contract will systematically address these issues to develop, test, refine, and deliver a set of interoperable 2nd and 3rd generation PGx platforms leveraging 4 inter-related projects, listed below. Additionally, the projects will set the stage to evaluate existing pharmacometabolomics (PMx) and develop a new 3rd generation whole genome PGx measurement and analysis method. For all 4 projects we will utilize the Michigan Genomics Initiative (MGI) data resources in the context of the UMHS EHR for these projects. We will also use the Prechter Bipolar Biorepository data resources to create training sets, leveraging its’ deeper phenotyping and rich longitudinal records. The IRB application to allow for this study to be initiated has been submitted (HUM00231342). We will also be utilizing the Michigan Genomics Initiative (MGI HUM00071298) and Prechter Bipolar Biorepository, Knowledge Base, and Longitudinal Study of Bipolar Disorder (HUM00000606); participating members of the Athey Laboratory are already listed on this IRB). All relevant UM COI disclosures related to this proposal are complete and up to date.

Project Number: 22-PAF03766 : Phenomics Health Inc.
Name of PD/PI: Brian Athey

2. Pioneering the development of a next-generation pharmacogenomic long-read sequencing assay (Monica Holmes, Greg Farber, and Brian Athey)
We are leveraging the precision of Cas9 technology and long read sequencing to target specific pharmacogenes. This innovative approach is bolstered by a patent-pending process designed to accurately calculate error rates associated with long-read sequencing outputs.

Such precision allows for the subsequent refinement of basecalling algorithms, ensuring enhanced accuracy in quality score production. Integral to our endeavor is the application of Artificial Intelligence (AI) across a wide spectrum of research activities. AI aids in the extensive development and elaboration of our mathematical framework, which underpins our patent-pending sequencing process. This holistic integration of AI not only optimizes our research efficiency but also significantly contributes to the advancement and precision of pharmacogenomic technologies.

Project Number: 22-PAF03766 : Phenomics Health Inc.
Name of PD/PI: Brian Athey

3. Exploring the Intricacies of Bipolar Disorder: Assessing the Impact of Medication, Substance Abuse and Comorbidities on Longitudinal Mood Outcomes Utilizing a Mixed Effect Model (Qingzhi Zhiu, Veera Baladandyuthpani, David Belmonte, Pranjal Srivisatra, Lars Fritsche, Alex Ade, Anastasia Yocum, Melvin McGinnis, and Brian Athey)

Bipolar disorder is a complex psychiatric condition characterized by drastic mood swings. Understanding the individual response to medication, as well as the impact of substance abuse and comorbidities, is crucial for effectively managing this disorder, an area that still requires more research. Our study explores the post-diagnosis complexities of bipolar disorder, utilizing longitudinal data from the Prechter Bipolar Research Program. We employed linear mixed models to classify mood outcome trajectories of PHQ-9, GAD-7 and ASRM into specific subgroups for bipolar disorder types 1 and 2. This approach enabled us to discern patterns in individual trajectories by leveraging shared characteristics. Our comprehensive analysis of medication use, substance abuse and comorbidities across these subgroups revealed significant variations, offering insights into the factors that influence the progression of bipolar disorder. The study identifies notable differences in treatment responses and outcomes (to be edited), underscoring the importance of personalized treatment. This enriches our understanding of the disorder's multifaceted nature.

4. Enhancing Academic Productivity through Generative AI-Powered Workflows (Lars Fritsche and Brian Athey)

In today’s academic landscape, enhancing research output and streamlining the writing process are essential. In this project, we aim to leverage Generative Artificial Intelligence (GenAI) technology to improve the creation of academic manuscripts and grant proposals. By developing and refining specialized Generative Pre-trained Transformers (GPTs), we strive to support researchers at various stages of their writing projects, reducing the time spent on academic writing and facilitating more effective research communication.

Utilizing both accessible platforms and proprietary tools, we plan to establish workflows that draw upon extensive curated knowledge bases and the latest large language models (LLMs). Our goal is to guide colleagues and aspiring PIs through the intricate research documentation process, from ideation to submission. These knowledge bases, tailored to specific projects, domains, and tasks, ensure the workflows are relevant and practical.

In addition, we are committed to showcasing detailed examples and step-by-step guides. These resources aim to illustrate how GenAI-powered workflows can transform literature review and the initial stages of drafting research manuscripts or grants, addressing common challenges such as writer's block.

Ultimately, while the responsibility for research integrity remains with the individual researcher, the judicious use of GenAI tools can significantly enhance the efficiency and quality of academic writing. In doing so, we aim to foster a more dynamic and collaborative research environment at the University of Michigan, propelling our community toward discoveries and innovations.

5. Integrating Genomics and Health Data to Refine Hypertension Management: Insights from the NIH All of Us Research Program cohort (Lars Fritsche and Brian Athey)

Nearly half of the U.S. adult population (48.1%, or approximately 119.9 million) have hypertension, and only about one in four of these individuals manage to control their condition. Uncontrolled hypertension is associated with a high risk of cardiovascular diseases and was a primary or contributing cause of over 691,000 deaths in the U.S. in 2021 alone (CDC 2021).

This project aims to identify how comorbidities, drug-drug interactions, and genetic variations affect hypertension medication effectiveness within the All of Us cohort. By utilizing genomic data, harmonized electronic health records, and survey data on social determinants of health, we aim to identify patterns and disparities in blood pressure control across this diverse participant group.

We plan to use mixed models to estimate individual blood pressure baselines and trajectories over time. Additionally, we aim to integrate genetic predictors of drug response by utilizing known pharmacogenetic variants and tools like Aldy or Stargazer along with All of Us's genomics data. This integration will improve our comprehension of pharmacogenetic phenotypes affecting drug efficacy and safety.

The anticipated outcomes include insights into the interplay between genetics, comorbidities, and drug responses, facilitating the identification of patient subgroups with distinct treatment profiles. This research aims to study the feasibility of analyzing retrospective biobank data to understand blood pressure control variations. Through this approach, we strive to demonstrate the potential of biobank data in informing future research directions and methodologies in hypertension management for more targeted and effective interventions.

6. NIH VIOLIN 2.0: Vaccine Information and Ontology Linked Knowledge-base

Major Goals: To promote community-wide data/metadata standardization and analysis, advance the understanding of the vaccine mechanisms, and support rational vaccine mechanism study and rational vaccine design against various infectious diseases, leading to safer public health.

Project Number: NIH-NIAID U24A171008
Name of PD/PI: He, Oliver, Co-I, Athey

7. NIH University of Michigan O’Brien Kidney Translational Core Center

Major Goals: Supports the translational pipeline providing resources to investigators in our Institutional and International Research Bases: 1.) Expands our unique CKD cohort combined with its longitudinal tissue, urine and serum biobanks to allow our research base investigators to investigate the molecular causes and endpoints of chronic kidney diseases; 2.) Disseminates and supports modern and powerful systems biological approaches for investigators to help them identify novel and robust biomarkers, endpoints and targets for diagnosis and treatment of CKD; 3.) Expert analysis and integration of cohort and systems data for our investigators using sophisticated bioinformatics and database integration that promote identification of specific pathways and targets for treatments for individuals or groups of subjects with CKD.

Note: I am not involved with individuals, funding, or data from outside countries on this grant.

Trainees and faculty participating in this project may be from various countries. None is currently paid under my supervision.

Status of Support: Active
Project Number: P30 DK08194325-01A1
Name of PD/PI: Pennathur, Sub; Co-I Athey

8. High-Performance Computing Cluster for Biomedical Research

NIH NIGMS 1S10OD0268

Name of PD/PI: PI Athey

We have recomposed the S10 advisory committee to include the following members, intended to provide a balance of stakeholders in terms of research interests, usage patterns, and organizational homes:

Prof. Lydia Freddolino (chair) -- Dr. Freddolino's research group is a major user of the resource, including much of the method development and application for protein structure/function prediction.

Prof. Brian Athey -- Dr. Athey is chair of the Department of Computational Medicine and Bioinformatics, the administrative home of the S10, and has lab members who are direct users of the resource.

Prof. Josh Welch -- Dr. Welch is another current major user of the GPU partition of the S10 resource for running deep learning based protein structure prediction.

Prof. Jianzhi Zhang -- Dr. Zhang's lab is a heavy user of the CPU partition of the S10 resource for analyzing.

Dr. Paul Wolberg (representing Prof. Denise Kirschner) -- Dr. Wolberg is a research scientist in the Kirschner lab, which makes heavy use of the S10 CPU partition to simulate the dynamics of tuberculosis infection.

Jonathan Poisson -- Mr. Poisson is a systems administrator in the Department of Computational Medicine and Bioinformatic and assists in running the S10 resource.

Brock Palen -- Mr. Palen is a systems administrator at the Academic Research Computing office at the University of Michigan and serves as a liaison to other campus-level IT resources.

9. NIH The role of epithelial barrier dysfunction in food anaphylaxis

Major Goals: Food allergy affects nearly 10% of the United States population, and misdiagnosis leads to difficult, anxiety provoking, and growth-limiting food avoidance. Current diagnostic tools are fraught with inaccuracy or require ingesting possible food allergens under medical monitoring, which is cumbersome, risky, and costly. The goal of this project is to understand the role of leaky skin and gut barriers in food allergy to develop more accurate and less onerous tests to diagnose food allergy.

Status of Support: Active
Project Number: F065420
Name of PD/PI: PI Chase Schuler, Co-I, Athey

10. Title: COMPASS: A comprehensive mobile precision approach for scalable solutions in mental health treatment (Fritshe, Athey, Tewari)

Major Goals: This study applies machine learning approaches to genomic, active and passive mobile behavioral tracking and EHR data from a large cohort before and during digital and clinic-based mental health care to develop individualized prediction models that will optimize mental health treatments.

Status of Support: Pending
Project Number: U01 MH136025
Name of PD/PI: Bohnert-Contact/Sen/Fritsche
Source of Support: NIH
Project/Proposal Start and End Date: 04/01/2024-03/31/2029
Primary Place of Performance: University of Michigan, Ann Arbor
Total Award Amount $17,927,886