Search PubMedSearch

PubMed · 40240884

Underrepresented populations in genomic research: a qualitative study of researchers' perspectives.

Abstract

BACKGROUND: The lack of diversity in genomic data limits researchers' ability to investigate the relationships between genetic profiles, disease manifestations, and responses to new therapies. As a result, innovations in treatment could have potentially harmful effects on a significant portion of the population due to incomplete or inaccurate genomic data. In addition, the lack of harmonization in the use of population descriptors in genomic studies raises both ethical and scientific concerns regarding which descriptors should be used to study and recruit underrepresented populations. Therefore, understanding the factors contributing to the lack of diversity in genomic research is an urgent scientific, clinical, and public health priority. This study aims to explore the social and contextual factors influencing the participation of underrepresented populations in genomic research, from the perspective of researchers in the field. METHODS: A total of 13 semi-structured interviews were conducted with researchers experienced in genomic research in Canada and fluent in either French or English. The interview transcripts were analyzed using thematic analysis. RESULTS: Researchers identified several factors contributing to the low participation of underrepresented populations in genomic research, with one key factor being the geographic distribution of research institutions and the disconnect between research efforts and the communities being studied. To address this issue, participants stressed the importance of moving away from colonial practices, such as conducting research on a community without consulting its members in the design phase. Furthermore, it was suggested that existing diversity, equity, and inclusion policies alone were insufficient to effectively address the challenge. Lastly, the study also highlighted a potential link between how study populations are categorized and the willingness of underrepresented groups to participate in genomic research. CONCLUSION: Although researchers are generally aware of the literature on the causes, consequences, and potential solutions for increasing participation, confusion remains regarding the use of population descriptors. Our findings highlight the need for improved education, greater consensus, and expanded dialogue within the genomic research community to promote the harmonization of population descriptors.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Arian Omeranovic, Flora Nguyen Van Long, Asma Boubaker, Annie Turgeon, Hermann Nabi. 2025-04-16. Underrepresented populations in genomic research: a qualitative study of researchers' perspectives.. https://doi.org/10.1186/s12920-025-02140-5

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans