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Global Genomic Data Set for Real World Evidence (RWE) Applications Market to Reach US$1.2 Billion by 2030

The global market for Genomic Data Set for Real World Evidence (RWE) Applications estimated at US$533.3 Million in the year 2024, is expected to reach US$1.2 Billion by 2030, growing at a CAGR of 15.1% over the analysis period 2024-2030. Genetic DNA Data, one of the segments analyzed in the report, is expected to record a 17.3% CAGR and reach US$547.4 Million by the end of the analysis period. Growth in the Transcriptome RNA Data segment is estimated at 12.8% CAGR over the analysis period.

The U.S. Market is Estimated at US$145.3 Million While China is Forecast to Grow at 20.4% CAGR

The Genomic Data Set for Real World Evidence (RWE) Applications market in the U.S. is estimated at US$145.3 Million in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$273.0 Million by the year 2030 trailing a CAGR of 20.4% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 10.9% and 13.7% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 12.1% CAGR.

Global "Genomic Data Set for Real World Evidence (RWE) Applications" Market - Key Trends & Drivers Summarized

How Are Genomic Data Sets Transforming the Foundation of Real World Evidence?

The application of genomic data sets in Real World Evidence (RWE) frameworks is rapidly reshaping how the life sciences industry approaches evidence generation, regulatory decision-making, and clinical strategy. Traditionally, RWE relied on observational data from sources such as electronic health records (EHRs), insurance claims, patient registries, and health surveys. However, with the exponential growth of genomic sequencing, especially next-generation sequencing (NGS), vast genomic data sets are being integrated into RWE platforms to create a more biologically anchored, precision-driven foundation for healthcare insights. This shift enables a deeper understanding of disease etiology, patient stratification, treatment efficacy, and outcomes based on genetic variability. For example, genomic RWE is becoming instrumental in identifying biomarkers predictive of drug response, adverse events, and disease progression-information critical for both drug development and clinical practice. Biopharma companies and research institutions are increasingly leveraging linked genomic and phenotypic data from large population cohorts, such as those curated by the UK Biobank, All of Us Research Program, and Genomics England, to derive actionable evidence at scale. Moreover, as global health systems digitize and adopt precision medicine models, the use of comprehensive genomic data sets in real-world contexts is unlocking unprecedented potential in observational research, accelerating the development of targeted therapies, and guiding regulatory and reimbursement strategies across markets.

What Technological and Infrastructure Advances Are Enabling the Use of Genomic RWE?

The integration of genomic data into RWE applications is heavily dependent on infrastructure readiness, technological interoperability, and advanced analytics. At the core are high-throughput sequencing platforms that generate extensive genomic profiles, including whole-genome, whole-exome, and targeted sequencing. These genomic data sets are then linked with longitudinal patient data using privacy-preserving record linkage technologies and cloud-based data lakes designed to store and harmonize heterogeneous data types. Cloud infrastructure, particularly through partnerships with providers like AWS, Google Cloud, and Microsoft Azure, allows scalable and secure access to genomic RWE pipelines. Bioinformatics tools and artificial intelligence algorithms are central to mining this complex data, enabling identification of genotype-phenotype correlations, population-specific variant analysis, and real-time evidence generation from decentralized data sources. Federated learning models are also gaining traction, allowing institutions to collaborate on RWE studies without moving sensitive genomic data outside protected environments. Interoperability standards such as FHIR Genomics and OMOP CDM extensions for genomic information are essential for ensuring that these data can be consistently integrated into EHRs, clinical trials, and observational studies. At the institutional level, genomic data warehouses and consent management frameworks are being deployed to ensure patient privacy while facilitating secondary use for research and policy-making. The sophistication of these tools is pushing the boundaries of what can be achieved in RWE, positioning genomics as a foundational layer of future-ready healthcare systems.

How Are End Users Adopting Genomic RWE Across Healthcare and Life Sciences?

The adoption of genomic data sets in RWE applications is expanding across a broad spectrum of end users, including pharmaceutical companies, payers, providers, regulators, and academic institutions. In drug development, biopharma companies are utilizing genomic RWE to optimize clinical trial design through targeted patient recruitment, adaptive trial models, and post-market safety monitoring. By analyzing how specific genetic variants influence treatment responses in diverse, real-world populations, companies can better identify unmet needs, select relevant endpoints, and tailor therapies more precisely. In the payer space, insurers are beginning to rely on genomic RWE to support decisions around value-based contracting, coverage criteria, and precision formulary management. Hospitals and health systems are also incorporating genomic data into clinical decision support tools to enhance diagnostic accuracy and personalize care pathways, particularly in oncology, rare diseases, and pharmacogenomics. Regulators such as the FDA and EMA are embracing genomically-informed RWE to support supplemental approvals, label expansions, and risk-benefit assessments of drugs already in the market. Furthermore, academic researchers are applying these data sets to study real-world gene-environment interactions and population-specific health disparities. Real-world genomics is also being used in public health genomics initiatives, such as surveillance of infectious disease mutations and assessment of vaccine effectiveness. The convergence of stakeholder demand, technological readiness, and policy evolution is driving widespread institutional commitment to embedding genomic RWE across the healthcare continuum.

What Are the Main Drivers Fueling Growth in the Genomic RWE Market?

The growth in the genomic data set for Real World Evidence (RWE) applications market is driven by several factors related to technological advances, shifting healthcare models, data accessibility, and end-user incentives. First, the dramatic reduction in the cost of genomic sequencing has made it feasible to conduct large-scale sequencing of diverse patient cohorts, enabling more representative and inclusive RWE studies. Second, the rise of value-based care and precision medicine models is creating a strong demand for individualized evidence that incorporates genetic data to support better outcomes at lower costs. Third, regulatory bodies are increasingly recognizing genomic RWE as a valid evidence stream for post-market surveillance, accelerated approvals, and drug repurposing efforts, providing a clear incentive for pharmaceutical firms to invest in genomically-enhanced real-world studies. Another major driver is the proliferation of longitudinal bio-banks and national genomics programs that link genomic profiles with lifetime health records, offering rich data environments for observational analysis. Additionally, payer interest in genomics-informed utilization management is growing, particularly in high-cost areas like oncology and rare diseases. The development of interoperable data standards and consent-based data sharing frameworks is also facilitating easier integration and use of genomic RWE in real-world research environments. Furthermore, AI-powered analytics platforms are enabling faster insights and more dynamic hypothesis testing using multi-omic data in real-world populations. Finally, the global expansion of partnerships between tech firms, health systems, and life sciences players is accelerating the commercialization and operationalization of genomic RWE applications. Together, these factors are driving a robust and expanding global market poised to redefine the standards of real-world evidence generation in the precision medicine era.

SCOPE OF STUDY:

The report analyzes the Genomic Data Set for Real World Evidence (RWE) Applications market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:

Data Type (Genetic DNA Data, Transcriptome RNA Data, Proteome Protein Data, Metabolite Data); Application (Oncology Application, Cardiovascular Application, Neurology Application, Immunology Application, Other Applications); End-Use (Biopharma End-Use, Other End-Uses)

Geographic Regions/Countries:

World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

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TABLE OF CONTENTS

I. METHODOLOGY

II. EXECUTIVE SUMMARY

III. MARKET ANALYSIS

IV. COMPETITION

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