Pharmacogenomics & PRS

Overview

In the era of personalized health management, our genetic makeup provides a wealth of information about our unique biological characteristics. Among them, Pharmacogenomics (PGx) and Polygenic Risk Score (PRS) are two important research fields, which help us understand the relationship between genes and health from different perspectives.

Pharmacogenomics is a discipline that studies how an individual's genetic variations affect their body's response to specific drugs. Everyone's genes have minor differences (referred to as single nucleotide polymorphisms, SNPS), and these differences may lead to different activities of certain enzymes (proteins responsible for breaking down or transporting drugs) in the body. The level of enzyme activity directly affects the concentration and efficacy of drugs in the body. Polygenic risk score is a statistical tool used to estimate the degree of genetic predisposition an individual has for a certain common health condition (such as cardiovascular disease, type 2 diabetes, etc.) based on multiple genetic variations (usually thousands or even millions). Researchers have compared the genetic data of millions of people with their health records through large-scale genome-wide association studies (GWAS) to identify thousands of genetic variations that are significantly associated with specific health conditions. Each variation is assigned a weight based on the magnitude of its impact. PRS is derived by adding up the weights of all the risk variations a person possesses.

Our Pharmacogenomics & Polygenic Risk Scores Service Enhances Your Research with:

Drug metabolism rate: Genes can determine whether a person is a "fast metabolizer", a "normal metabolizer" or a "slow metabolizer". Fast metabolizers may cause drugs to lose their efficacy too quickly, and alternative solutions need to be explored. People with slow metabolism may experience drug accumulation in their bodies, increasing the risk of adverse reactions.
Drug effectiveness: Certain genetic variations can affect the targets of drug action, making the drug highly effective for some people but less effective for others.
Risk of adverse reactions: A specific genetic composition may indicate a higher likelihood of an individual experiencing specific side effects from certain medications.
Research tool: PRS is a powerful research tool that helps scientists identify new genes and biological pathways involved in disease development and understand the genetic architecture of diseases.
Population risk stratification: In epidemiological studies, PRS can be used to distinguish groups with different risk levels at the population level, which is of great significance for planning public health resources and research priorities.

What Is Pharmacogenomics & Polygenic Risk Scores

Pharmacogenomics (PGx) and Polygenic Risk Scores (PRS) are two powerful fields at the intersection of genetics and data science. Pharmacogenomics and PRS modeling merge genomics with statistical algorithms to predict drug efficacy and disease susceptibility. PRS quantifies cumulative genetic risk by aggregating variants across the genome, while pharmacogenomics identifies gene-drug interactions influencing treatment response. Integrating these approaches enables drug selection and dosage adjustments, optimizing therapeutic outcomes.

How We Deliver This Solution

Sequencing

Whole Genome Sequencing (WGS)

Purpose: Comprehensive detection of single-nucleotide variants (SNVs), structural variants (SVs), and copy number alterations (CNAs) across populations.
Application: Identifies rare disease-causing mutations, population-specific genetic markers, and drug-response biomarkers.
Link: /pop-genomics/seq/whole-genome-reseq.html

Whole Exome Sequencing (WES)

Purpose: High-throughput sequencing of protein-coding regions to prioritize functional variants.
Application: Accelerates discovery of disease-associated genes and therapeutic targets in large cohorts.
Link: /pop-genomics/seq/whole-exome-sequencing.html

Genotyping Arrays

Purpose: High-throughput genotyping of pre-selected SNPs for cost-effective PRS calculation.
Application: Scales PRS deployment across large populations in clinical trials or healthcare systems.
Link: /pop-genomics/seq/gbs.html

Bioinformatics

PRS Algorithm Development

Purpose: Integrates multi-ancestry GWAS data to build robust, generalizable risk scores.
Application: Predicts disease susceptibility and drug response in diverse populations.

Population Stratification Correction

Purpose: Adjusts for ancestry-specific genetic effects to minimize bias in PRS accuracy.
Application: Ensures equitable clinical translation across ethnic groups.

Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling

Purpose: Links PRS-derived genetic risk to drug exposure-response relationships.
Application: Optimizes dosing regimens for high-risk individuals.

Service flowchart

Figure 1: Pharmacogenomics & Polygenic Risk Scores

Our Advantages

Comprehensive Pharmacogenomic Integration & PRS Customization

Seamlessly combine pharmacogenomic data (e.g., drug-metabolizing enzyme variants) with PRS models tailored to specific diseases or drug responses. This enables precise prediction of treatment efficacy and adverse event risks across diverse populations, leveraging multi-ethnic genomic datasets for broader applicability.

Scalable PRS Validation & Clinical Translation

Validate PRS models using real-world evidence from 100,000+ patient cohorts, ensuring robust performance across ancestries. Our platform supports end-to-end workflows, from variant selection to risk stratification, accelerating regulatory approval and adoption in precision oncology, cardiology, and rare disease management.

AI-Powered PRS Optimization

Leverage machine learning algorithms to refine polygenic models, improving risk prediction for complex traits and therapeutic outcomes. Our adaptive models dynamically update with new data, ensuring relevance across evolving genomic landscapes.

Multi-Omics Integration Framework

Seamlessly combine genomic, transcriptomic, and proteomic data to unravel molecular mechanisms underlying drug response variability. This holistic approach enhances PRS accuracy by capturing regulatory and functional impacts of genetic variants.

Applications

1. Target Identification and Validation

Genomic profiling uncovers disease-associated genes and pathways, providing high-confidence targets for drug development.

Example: Cystic Fibrosis (CF) and CFTR Modulators

Challenge: CF is caused by mutations in the CFTR gene, leading to defective chloride channels. Traditional therapies treated symptoms, not the root cause.
Solution: Pharmacogenomic analysis identified specific CFTR mutations (e.g., ΔF508) and enabled the development of CFTR modulators (e.g., ivacaftor, lumacaftor) that restore channel function.
Outcome: Precision therapies improved lung function and quality of life for patients with actionable mutations, demonstrating the power of genetic target validation.

2. Polygenic Risk Stratification for Drug Repurposing

PRS models quantify cumulative genetic susceptibility to complex diseases, identifying high-risk subgroups for targeted interventions.

Example: Cardiovascular Disease (CVD) and Statin Therapy

Challenge: CVD risk varies widely due to polygenic factors, making blanket statin prescriptions suboptimal.
Solution: A PRS integrating >6 million variants predicted CVD risk with high accuracy. Patients in the top 10% of PRS scores showed a 3x higher benefit from statins compared to low-risk groups.
Outcome: PRS-guided statin use reduced adverse events and improved cost-effectiveness, showcasing PRS as a tool for personalized prevention.

3. Pharmacogenomic-Guided Dose Optimization

Genetic testing predicts drug metabolism efficiency, enabling tailored dosing to maximize efficacy and minimize toxicity.

Example: Warfarin and CYP2C9/VKORC1 Genotyping

Challenge: Warfarin's narrow therapeutic window causes frequent bleeding or clotting events due to genetic variability in metabolism (CYP2C9) and sensitivity (VKORC1).
Solution: Pharmacogenomic algorithms incorporating CYP2C9/VKORC1 variants recommended personalized starting doses, reducing time to stable anticoagulation by 50%.
Outcome: FDA-approved labeling now includes genetic testing guidelines, preventing 85,000 serious adverse events annually in the U.S. alone.

4. PRS-Enhanced Clinical Trial Enrichment

PRS models identify genetically homogeneous subgroups, improving trial efficiency and success rates for novel therapies.

Example: Alzheimer's Disease (AD) and Anti-Amyloid Therapies

Challenge: AD trials failed due to heterogeneous populations with varying genetic contributions to pathology.
Solution: A PRS for APOE and other AD-associated variants enriched trials with high-risk individuals, doubling the observed effect size of anti-amyloid drugs.
Outcome: PRS-based enrollment accelerated drug approval timelines and reduced sample sizes by 40%, lowering development costs.

Demo

Figure 2: Implementation of PRS results into medical decision process. (Simona, 2023)

Case Study

Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing.

Journal：Sci Adv.

Published：2024

Circulating lipids, encompassing total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL-C), and low-density lipoprotein (LDL-C), stand as crucial, modifiable, and heritable risk factors for coronary heart disease (CHD). Previous research indicates a moderate-to-high heritability for lipid level variations, with estimates spanning 20 to 60%. Despite genome-wide association studies (GWASs) identifying numerous common susceptibility variants for circulating lipids, most confer only minor individual risks and exhibit limited predictive power for CHD. Traditional polygenic risk scores (PRSs) for lipid traits, constructed solely from genome-wide significant genetic variants, have also shown limited success in enhancing CHD risk prediction. Thus, there is a pressing need for more comprehensive and effective genetic tools to quantify lifetime disease risk and improve risk stratification for clinical utility.

Leveraging advancements in computational algorithms and large-scale datasets, novel PRSs for common diseases have been developed and validated. These PRSs fully capture genome-wide variation and are constructed using two primary strategies: (1) liberalizing significance thresholds for variant inclusion while accounting for linkage disequilibrium (LD) patterns in specific populations, and (2) assigning new variant weightings via Bayesian methods that infer posterior mean effects using GWAS summary statistics, genomic correlation information, and pre-specified causal variant proportions. For instance, Khera et al. constructed six genome-wide PRSs incorporating 5,218 to 6,917,436 common genetic variants to predict risks for CHD, atrial fibrillation, type 2 diabetes (T2D), inflammatory bowel disease, breast cancer, and severe obesity in predominantly European ancestry cohorts. Building on these advancements, recently developed computational methods were applied to optimize PRSs for four lipid traits across multiple East Asian cohorts at various life stages, with subsequent testing in general populations and T2D patients. The best-performing PRSs were further evaluated for their impact on 3-year lipid changes in adolescents and their potential clinical implications for subclinical atherosclerosis in adult women and CHD in T2D patients.

The optimized PRSs demonstrated robust predictive performance for lipid traits in East Asian populations, outperforming traditional PRSs limited to genome-wide significant variants. These scores effectively captured genome-wide genetic variation and improved risk stratification for dyslipidemia and T2D-related cardiovascular complications. In adolescents, the best-performing PRSs were significantly associated with 3-year lipid level changes, highlighting their potential for early identification of individuals at risk for metabolic disorders. Furthermore, in adult women, these PRSs were linked to subclinical atherosclerosis, while in T2D patients, they showed promise in predicting CHD risk, underscoring their clinical utility across diverse populations and disease contexts. These findings support the integration of comprehensive genetic information into risk assessment frameworks to enhance precision medicine and preventive healthcare strategies.

Figure 3: Odds ratio (OR) of coronary heart disease stratified by quintile of polygenic risk scores.

FAQ

How does pharmacogenomics enhance drug development compared to conventional trials?

A: Unlike one-size-fits-all trials, pharmacogenomics:

Identifies genetic subgroups with differential drug responses (e.g., CYP2C19 variants affecting clopidogrel efficacy).

Reduces trial failure rates by excluding non-responders or high-risk patients early.

Enables repurposing of existing drugs for genetically defined subgroups (e.g., PCSK9 inhibitors for familial hypercholesterolemia).

How do you ensure PRS models are clinically actionable across diverse populations?

A: We validate models through:

Ancestry-stratified analysis to detect and correct for population-specific bias.

Prospective cohort studies in underrepresented groups (e.g., South Asians for type 2 diabetes PRS).

Integration with electronic health records (EHRs) to assess real-world impact on screening and treatment decisions.

How do population genetics services differ from traditional genomic screening?

A: Traditional screening often focuses on common variants in European populations, while population genetics services:

Analyze ancestry-specific rare variants (e.g., HBB mutations causing sickle cell in African populations).

Use global reference panels (e.g., 1000 Genomes Project, All of Us) to avoid bias and improve variant interpretation across diverse ethnic groups.

Integrate epidemiological data (e.g., diabetes prevalence in Polynesians) to prioritize clinically relevant genetic targets for specific populations.

References

Simona A, Song W, Bates DW, et al. Polygenic risk scores in pharmacogenomics: opportunities and challenges-a mini review. Front Genet. 2023 Jun 15;14:1217049. https://doi.org/ 10.3389/fgene.2023.1217049IF:
Tam CHT, Lim CKP, Luk AOY, et al. Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians. Genome Med. 2021 Feb 19;13(1):29. https://doi.org/ 10.1186/s13073-021-00831-z.

* Designed for biological research and industrial applications, not intended for individual clinical or medical purposes.