# Leveraging the Microbiome, Local Admixture, and Machine Learning to Optimize Anticoagulant Pharmacogenomics in Medically Underserved Patients

> **NIH NIH R01** · UNIVERSITY OF ARIZONA · 2022 · $106,845

## Abstract

ABSTRACT
 Currently available pharmacogenomic (PGx) algorithms have critical limitations, including a lack of
generalizability to non-white populations. Under-representation in clinical studies, the propensity to cause
adverse events, and a lack of consideration of admixed populations in clinical PGx guidelines are all factors that
contribute to limited utility of PGx algorithms in diverse populations. Thus, our originally awarded proposal
focused on improving warfarin stable dose prediction, as it continues to remain one of the most prescribed drugs
in the United States and a leading cause of adverse drug events particularly in underserved patients such as
African Americans (AAs) and Latinos. Preliminary results from this proposal demonstrate that generation of local
ancestry (LA) estimates enables inclusion of admixed populations and improves power in genetic association
studies on diverse and admixed populations. Thus, we seek to expand upon our original proposal to perform
more inclusive pharmacogenetic studies by generating LA estimates in the large, racially/ethnically diverse
AllofUs cohort. We will investigate the relationships between LA and PGx variants and showcase the utility of LA
estimates and the AllofUs cohort by identifying novel PGx variants associated with warfarin stable dose. Our
overarching hypothesis is that LA can be used to enable genomic association analyses that are more inclusive
of admixed and diverse cohorts and to uncover novel findings that were previously overlooked in ancestrally
European populations. We will pursue two Specific Aims (SAs) to test this hypothesis: (SA1) Characterize LA
for major pharmacogenes and its correlation with global ancestry and PGx variants in diverse populations from
AllofUs and; (SA2) Leverage LA to identify novel PGx variants related to warfarin stable dose in admixed AllofUs
participants. In SA1, We will estimate LA using RFMix from genome array and sequencing data in the AllofUs
Controlled Tier. We will test if LA at clinically relevant pharmacogenes correlates with patient-level global
ancestry and presence of clinically relevant pharmacogenomic variants. In SA2, we will incorporate LA estimates
from SA1 into genome-wide association analyses for warfarin stable dose using Tractor while controlling for
clinical characteristics and clinically relevant PGx variants in admixed individuals from AllofUs, including
Hispanic, AA, and multi-race individuals. The outcomes of this work will provide a framework for LA investigation
with other PGx drug-gene pairs and enable the identification of novel PGx variants that affect drug response in
medically underserved, diverse populations. This research has the potential to identify new sources of variability
in warfarin dose, improve the safety and efficacy of warfarin treatment, and reduce disparities in PGx research
for medically underserved patients.

## Key facts

- **NIH application ID:** 10656719
- **Project number:** 3R01HL158686-02S1
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** Jason Hansen Karnes
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $106,845
- **Award type:** 3
- **Project period:** 2022-08-12 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10656719

## Citation

> US National Institutes of Health, RePORTER application 10656719, Leveraging the Microbiome, Local Admixture, and Machine Learning to Optimize Anticoagulant Pharmacogenomics in Medically Underserved Patients (3R01HL158686-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10656719. Licensed CC0.

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