The Athey Lab

Department of Computational Medicine and Bioinformatics 

University of Michigan Medical School

Brian D. Athey, PhD

Brian Athey, Ph.D.

Principal Investigator 

Current Research Focus

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 Fritze, 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.

Figure 1: Analytical pipeline for examining different patterns in mood trajectories and assessing the influence of potential exposures on these trajectories. (step 0 input data, outputs)

Figure 1: Analytical pipeline for examining different patterns in mood trajectories and assessing the influence of potential exposures on these trajectories. (step 0 input data, outputs)  

4. Enhancing Academic Productivity through Generative AI-Powered Workflows (Lars Fritze 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 Fritze 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 


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 $18,721,603 

Work Titles

Michael Savageau Collegiate Professor & Chair

Department of Computational Medicine & Bioinformatics

Professor of Psychiatry

University of Michigan



Ph.D., Biophysics, University of Michigan (1990)

B.S., University of Michigan-Dearborn; Dearborn, MI (1982)

Classical Studies, St. Johns College, Annapolis, MD (1976-1977)


1991-1993  NIH Postdoctoral Fellowship

1990-1991 NIH Postdoctoral Fellowship

Contact Information

Work Email Address:


DCMB Website

MI Experts Profile

Fax Number:


Office Number:


Administrative Contact

Theresa Nester

Lab Address:

NCRC Building 520, Room 3393

1600 Huron Parkway

Ann Arbor, MI 48105

Office Address:

2017F Palmer Commons

100 Washtenaw Ave.

Ann Arbor, MI 48109-2218

Athey Lab Members

Brian Athey, Ph.D.
Principal Investigator Michael Savageau Collegiate Professor & ChairDepartment of Computational Medicine & BioinformaticsProfessor of PsychiatryUniversity of Michigan
Veerabhadran Baladandayuthapani, Ph.D.
Professor of Biostatistics Professor of Computational Medicine and Bioinformatics
Lars Fritsche, Ph.D.
Associate Research Scientist of Biostatistics
Gerry Higgins, Ph.D.
Adjunct Research Professor of Computational Medicine and Bioinformatics
Alex Ade, M.S.
Applications Programmer/Analyst 
Monica Holmes, M.S.
Ph.D. Student in Bioinformatics and Research Specialist
Qingzhi Liu
Ph.D. Student in Biostatistics
David Belmonte, M.D.
Masters Student in Bioinformatics

Pranjal Srivastava
PhD Student in Bioinformatics

Recent Graduates

Ari Allyn-Feuer, PhD
Director, AI Products, GlaxoSmithKline  
Xinhai Hou (Renly)
Alex Kalinin, Ph.D.
Postdoctoral FellowBroad Institute, Cambridge MA
Narathip “Nick” Reamaroon

Selected Publications

Sarntivijai S, Lin Y, Xiang Z, Meehan TF, Diehl AD, Vempati UD, Schurer SC, Pang C, Malone J, Parkinson H, Liu Y, Takatsuki T, Saijo K, Masuya H, Nakamura Y, Brush M, Haendel MA, Zheng, J, Stoechert CJ, Peters B, Mungall CJ, Carey TE, States D, Athey BD and He Y. CLO:“The Cell Line Ontology.” Journal of Biomedical Semantics. 2014 DOI: 10.1186/2041-1480-5-37, ISSN: 2041-1480. PMID:25852852 PMCID: PMC4387853.

Higgins GA, Allyn-Feuer A, Athey BD. “Epigenomic mapping and effect sizes of noncoding variants associated with psychotropic drug response.” Journal of Pharmacogenomics. September 4, 2015. DOI: 10.2217. PMID:26340055.

Sarntivijai S, Zhang S, Jagannathan DG, Xiang, Zuoshuang, Burkhart K, Omenn GS, He Y, Athey BD and Abernethy DR. “Linking MedDRA-Coded Clinical Phenotypes to Biological Mechanisms by The Ontology of Adverse Events: A Pilot Study on Tyrosine Kinase Inhibitors.” Journal of Drug Safety. March 22, 2016. DOI: 10.1007/s40264-016- 0414-0. PMID:27003817.

Higgins G.A., Allyn-Feuer A, Georgoff P, Nikolian V, Alam HB, Athey BD. “Mining the Topography and Dynamics of the 4D Nucleome to Identify Novel CNS Drug Pathways.” Methods. 2017 Jul 1;123:102-118. PMID: 28385536.

Allyn-Feuer A, Ade A, Luzum J, Higgins GA, Athey BD. (2018). “The Pharmacoepigenomics Informatics Pipeline Defines a Pathway of Novel and Known Warfarin Pharmacogenomics Variants,” Pharmacogenomics. 2018 Apr;19(5):413-434. PMID: 29400612.

Kalinin AL, Higgins GA, Reamaroon N, Soroushmehr SM, Allyn-Feuer A, Dinov ID, Najarian K, Athey, BD. January 23, 2018. “Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification” Pharmacogenomics. 2018 May;19(7):629-650. PMID: 29697304.

Higgins GA, Williams AW, Ade AS, Hasan AB, Athey BD (2019) “Druggable Transcriptional Networks in the Human Neurogenic Epigenome: Transcription factors and chromatin remodeling complexes reveal novel drug-disease pathways.” Pharmacological Reviews, 1521-0081/71;1-19, October 2019. PMID: 31530573.

Higgins GA, Handelman SA, Allyn-Feuer A, Ade AS, Burns JS, Omenn GS, Athey BD.

“Ketamine’s pharmacogenomic network in human brain contains sub-networks associated with glutamate neurotransmission and with neuroplasticity”. bioRxiv doi: April 30, 2020.

Reamaroon N, Sjoding MW, Gryak J, Athey BD, Najarian K, Derksen, H. “Automated

Detection of Acute Respiratory Distress Syndrome from Chest X-Rays Using Directional

Blur and Deep Learning Features”. Computers in Biology and Medicine. November 2020.

Submission CIBM-D-20-03386R1. Comput Biol Med. 2021 Jul;134:104463. doi: 10.1016/j.compbiomed.2021.104463. Epub 2021 May 11. PMID: 33993014

Kalinin, AA., Hou X, Ade AS, Fon GV, Meixner W, Higgins G, Sexton JZ, Wan X, Dinov

ID, Athey BD, “Valproic Acid-Induced Changes of 4D Nuclear Morphology in Astrocyte

Cells.” Mol Biol Cell. doi: 10.1091/mbc.E20-08-0502. April 28, 2021. PMID: 33909457.