WHO landscape of human genomics technologies in clinical studies

Published: December 2025

WHO analysed the landscape of human genomics technologies used in clinical studies for all diseases. 
The analysis spans trials from 1990 through 2024, and provides insights into the number and location of studies, the age group of participants enrolled, the trial phases, and the diseases being targeted.
See below for details on the scope, analysis, and limitations. 

See also:

Health products in the pipeline from discovery to market launch for all diseases

What you see | Scope, analysis and limitations | Data sources 

What you see

The data visualization allows filtering for

  1. The recruitment status of studies
  2. Disease or condition targeted by the study (cancer, cancerous rare disease, and rare disease)
  3. Year of study registration
  4. Decade of study registration

The data visualization shows the number of studies by: 

Study characteristics

  • Cumulative number of studies over time (chart A.1)
  • Number of study registrations per year (chart A.2)
  • Phases of studies (chart A.3)
  • Number of studies per year per WHO region (chart A.4) and number of studies per country (chart A.5)
  • Map of number of studies (map A.6)
  •  Income group of the country in which the trial is taking place (chart A.7)

Disease or condition focus of trials by:

  • High level categories (chart B.1)
  • Organ or system categories (chart B.2)
  • Disease or condition (chart B.3)
  • High level category inclusion over the years (chart B.4)

Study participants:

  • Gender distribution of study inclusion criteria in total (chart C.1)
  • Age group inclusion by year of registration (chart C.2)
  • Number of studies including each age group (chart C.3)

 

Studies can be investigated in detail in the final table (Table D1)

Note: A clinical study can be conducted in multiple locations and each trial is counted once per country; therefore, the numbers displayed in these charts may total to more than the total number of studies.

Points to note

The number of studies including genomic technologies has been increasing especially since 2010 (see A2)

Human genomics technologies are being used in all phases of interventional trials. 70% of these studies do not have a phase as they are observational in nature, or the phase is unknown (see A3)

10 countries account for 70% of studies using human genomic technologies (chart A4)

The Western pacific region has expanded the use of human genomic technologies in studies very quickly since 2016 (chart A3), the majority driven by China (chart A4)

Most studies are focused on non-communicable diseases (chart B1) and this has remained steady over time (chart B4). While as expected this does indicate a lost opportunity to investigate how human genomics affect infectious disease natural history and treatments

 

 

 

To explore the data further

  • Select a specific region (chart B.2) or any other specific element or combination of elements to display the corresponding data in the other charts. 
  • Hold the ‘Ctrl’ key on your keyboard to select more than one option. 
  • Hover the cursor on a bar or a cell in a table to see more information in a pop-up window.
  • Undo a selection by clicking ‘undo’ or ‘reset’ near the bottom of the page or by clicking the same element again.

Scope, analysis and limitations of the data

Scope

  • Our analysis covers clinical studies using human genomic technologies registered in a clinical trial registry which contributes data to ICTRP from 1999 to 2024. See search terms used below. All studies investigating infectious disease

Analysis

  • The analysis leverages data from the WHO's International Clinical Trials Registry Platform (WHO ICTRP). The search terms used to identify genomics technologies were restrictive and focused only on human genomics, excluding pathogen genomics. 
  • The data presented in this visualization utilizes classifications that are not mutually exclusive. For example, a registered trial can recruit participants from multiple countries and regions. In this case, the trial will be counted once per region in chart A.4 but once per country in chart A.5. The total number of trials across the two charts is therefore not equivalent.

Limitations of the data

  • Genomic technology search terms were as follows:
"Genomic" OR "Genomics" OR "Genome" OR "Genome-wide" OR "GWAS" OR "Polygenic" OR "Genetic Risk Score" OR "Genetic Risk Scores" OR "Sequencing" OR "Next-generation sequencing" OR "NGS" OR "Whole-exome" OR "WES" OR "Whole-genome" OR "WGS" OR "Illumina" OR "nanopore" OR "Pyrosequencing" OR "Pharmacogenomics" OR "Pharmacogenetics" OR "Pharmacogenomic" OR "Pharmacogenetic" OR "Epigenomic" OR "Epigenomics" OR "Epigenetic" OR "Epigenetics" OR "Gene editing" OR "DNA editing" OR "Genome editing" OR "Base editing" OR "CRISPR" OR "genome engineering” OR "Clustered Regularly Interspaced Short Palindromic Repeat" OR "Transcriptomic" OR "Transcriptomics" OR "Transcriptome" OR "Gene Expression" OR "Transcript Expression"

Note: Genetic, gene, DNA, recombinant and similar terms were not included, as the focus was on genomics.

  • Automated data mining was used to generate information on the primary disease investigated in each trial using text-based data fields.
    • A list of disease synonyms was compiled using as a base the Unified Medical Language System (UMLS). This was complemented by synonyms drawn from the data, mostly to account for errors in data entry such as spelling errors or use of abbreviations.
    • An automated algorithm was applied to two data fields using the list of disease synonyms to generate the uniform disease classification field used in this analysis. The first field is a based on free-text keywords provided by the registrant. The second is the scientific title of the trial. If the first field provided a match the second was not used.
    • The first match closer to the beginning of the text field was selected. This was considered the primary disease investigated by the trial. It is possible that the trial has more than one disease focus, which is not captured in this analysis.
    • The algorithm was refined through various iterations but as with any automated algorithm, it is likely that some trials were not correctly matched.