The Role of Genomic Data in Drug Discovery and Early Research

Leverage Ovation Omics Data to accelerate therapeutic development and gain deeper understanding of patient populations.

Genomic Data Use Cases

By analyzing the relationship between an individual’s genome or omic profile and clinical phenotype, researchers are able to better understand the underlying causes of diseases and identify potential therapeutic targets or biomarkers. In addition, linked omic and phenotype data can be used to predict which patients are likely to progress in a disease condition or respond to a particular treatment, accelerating development and leading to more personalized and effective medicine.

Use cases of genomic data in early research and development

The use of genomic data in discovery and early research is revolutionizing the way we approach the treatment of diseases.

We have already seen practice-changing precision medicines in Oncology come to market and broad use of oncology genomic data in drug development, but have yet to reach maturity of genomic applications in other disease areas like cardiometabolic and autoimmune disease. We hope to change that.

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Omics data linked with clinical phenotypic data can be used to identify candidate biomarkers for a wide range of diseases. By analyzing an individual’s genome, researchers can identify genetic variations associated with a particular disease or phenotypic outcome. These variants can be used as biomarkers for early detection and diagnosis as well as potential targets or prognostic measures for therapeutic development.

Learn More About the Potential for Omics Data to Enhance Biomarker Discovery


Genetic or transcriptional variants associated with a particular disease can be targeted by the development of new drugs. Additionally, target validation can also involve functional assays to demonstrate that the target is biologically active and can be modulated by the drug, which can further increase the chances of success in clinical trials. Genomic data can be used to help identify targets that are more likely to be effective for specific populations.


Omics data can be used to understand the biological processes and pathways that lead to the development and progression of a particular disease and the genetic factors that contribute to this disease development. The disease mechanism of action may involve:

  • Genetic and environmental factors
  • Changes in cellular signaling pathways
  • Dysfunction of specific organs or tissues
  • Disruptions in normal physiological processes

Additionally, transcriptomic data can be used to study the expression of genes in different tissues and at different stages of the disease, providing insight into the disease’s progression over time.


Pharmacogenomics research is aimed at understanding how an individual’s genetic makeup affects their response to drugs. Genomic data can be used to identify variations in genes that encode drug targets, drug-metabolizing enzymes, and drug transporters. These variations can affect the way a person metabolizes a drug, leading to differences in drug efficacy, toxicity, and side effects between individuals.This information can be used to optimize dosing, to predict potential side effects and prevent drug-drug interactions.To learn more about pharmacogenomics and dosing in cardiovascular, see our blog.


Knowing the details of an individual’s genome, especially when combined with that individual’s clinical history, can help researchers identify specific genetic variations that are associated with a higher risk of a disease and interventions that can be implemented to prevent or delay the onset of the disease, such as lifestyle changes or regular screenings.


By analyzing an individual’s genome, researchers can identify genetic variations that are associated with a particular disease or response to a treatment. This information can be used to:

  • Group patients with similar genetic profiles
  • Tailor treatment options to specific groups of patients
  • Understand the underlying mechanisms of a disease for subpopulations of patient cohorts

Genomic patient stratification in drug development helps to improve personalization of the diagnosis, treatment, and prevention of diseases for targeted populations.

Use cases of genomic data in early research and development

Case Studies

Case Study:
Real-world RSV testing data supports clinical trial enrollment strategies for the ConquerRSV vaccine trial

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Case Study:
COVID-19 Variant Surveillance as Part of the NIH RADx Program

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Case Study: Realizing the value of biospecimens for life sciences research in IBD

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Case Study: NAFLD genomic data show variants that increase risk of comorbidities

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Ovation helps advance genomic data use cases

In a time where genomic data is actively and more regularly being applied to early drug discovery and pre-clinical research, Ovation’s ability to coordinate across labs, sequencing partners and phenotypic data vendors to deliver high quality, 30-50x whole-genome sequenced cohorts, whole exome sequencing and transcriptomics can alleviate many of the challenges researchers have with obtaining genomic data. Ovation is committed to providing researchers with access to a diverse collection of omics datasets at scale sourced from a network of labs that are representative of the US population.

Ovation helps researchers access high-quality genomic data linked to rich, longitudinal phenotypic data at scale which enables life sciences to advance drug discovery and development more efficiently.

Ovation has biobanked over 1.69M samples consented for research and commercial purposes, that can be sequenced using state of the art technology with our lab partners.

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