The Significance of Biobanks in Genomics and Personalized Medicine Research

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Introduction

Advances in omics technologies, largely powered by Next-generation sequencing applications (NGS), have expanded the role of biobanks in genomic and personalized medicine. Modern biobanks now manage a lot of activities ranging from biological sample and informed consent collection, biological sample and associated data transfer, clinical/lifestyle data storage to large-scale collaborations across institutions.

This article discusses the significance of biobanks in genomic and personalized medicine research. Many major breakthroughs in genetics and genomics would not have been possible without biobank contributions. The following sections detail how biobanks have shaped and advanced this field.

Estimation of Population Frequencies of Genetic Variants

Genetic datasets from biobanks are key resources for estimating population variant frequencies. Collecting allele frequency data was an early reason for creating genetic repositories and biobanks. Several projects have since developed “population reference genomes” or local allele frequency databases, with recent examples from the United Arab Emirate (Daw E. et.al 2021), Russia (Barbitoff, Y.A et. Al 2021), the Netherlands (Boomsma, D.I, et.al 2014) and Japan (Takayama, J et.al 2021).

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The push for such references comes from many population-specific variants missing in the standard human reference genome or large global resources like the genome aggregation data base (gnomAD). For clinical geneticists and bioinformaticians, population-level allele frequencies are crucial in interpreting NGS-based diagnostic testing for inherited disorders. Using population allele frequency instead of global frequency significantly reduces the effort needed for variant interpretation.

Allele frequency data also guide clinical classification of variants under American College of Medical Genetics and Genomics (ACMG) guidelines. Another major use is studying human population structure. These datasets reveal ancestral migrations and combined patterns, as shown by Reich D. et al. (2009) on Indian populations. Studies by Cassa C.A. et al. (2017) and Havrilla J.M. et al. (2019) further showed that comprehensive allele frequency resources help study evolutionary conservation and highlight genes and genomic regions with clinical relevance.

Construction of Local Reference Genotype Panels

Whole-genome sequencing (WGS) data from biobanks can be used to build local reference panels, adding valuable information on genetic variation. Example of this includes the Finnish SISu panel and a recent WGS-based UK panel (Halldorsson B.V et. Al 2022).

These panels enable genotype imputation—predicting genotypes at sites not covered by arrays—which boosts Genome-wide association study (GWAS) power and supports meta-analyses (Das S. et al. 2016). They also allow fine mapping to pinpoint causal variants at GWAS loci (Schaid D.J. et al. 2018). Reference panels further provide linkage disequilibrium and haplotype data useful for studying natural selection (Nielsen R. et al. 2016) and population structure.

External Controls

Samples that are genotyped which are provided by biobanks can be used as external controls in GWAS for rare or common diseases. Integration of external controls in case-control studies has been shown to improve the power of rare variant association (Li Y et. al. 2022). Such external controls are frequently drawn from open genotype datasets such as the UK Biobank dataset.

Apart from the direct inclusion of external controls in the analysis, reference data generated by biobanks might be used in other types of analysis (Lazareva T.E. et al, 2022). For example, genotype and phenotype data can be used to define exposures for Mendelian randomization (MR), a group of methods designed for testing a causal relationship between traits (Sanderson E. et. al. 2022).

Provision of Information for Association Studies

Biobanks collect questionnaires, physical measures, health check-ups, and hospital data, creating extensive datasets for association studies. Numerous single-trait Genome wide association studies (GWAS) using biobanks have advanced understanding of human genetics and disease (Mills M.C & Rahal C.A 2019).

These studies support development and evaluation of polygenic risk scores (Li R et al. 2020). Polygenic risk score is a statistical calculation that estimates an individual’s genetic risk for developing a specific trait or disease by aggregating the effects of many common genetic variants across the genome. Polygenic risk score is central to personalized medicine (Khera A.V et. al 2018).

Population-level biobanks may also enable embryonic Polygenic risk score for preimplantation testing and clinical use (Treff N.R. et al. 2020). The real power of biobanking lies in multi-trait analyses. Biobanks like UK Biobank and FinnGen make Genome wide association studies (GWAS) results for thousands of phenotypes publicly available (Lazereva T.E et.al 2022). Aggregating such data enables phenome-wide association studies (PheWAS) and identification of pleiotropic genes affecting multiple traits or diseases (Watanabe K. et al. 2020; Shikov A.E et al. 2020).

Adoption of Precision Medicine by Healthcare Institutions

Biobanking drives healthcare toward precision medicine. Genome-informed approaches are emerging by returning genomic results to cohort participants. Key concerns include which genes to examine without specific diagnosis, which results to return, and ensuring that returned results are clinically significant, requiring careful assessment of variant function and disease link (Lazareva T.E. et al, 2022).

Healthcare systems also need protocols for managing patients with gene-associated diseases. Germline variant pathogenicity follows ACMG guidelines (Richards S. et al. 2015), while somatic variants are classified according to the Association for Molecular Pathology (AMP) in conjunction with the American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP) guidelines (Marilyn M. et.al 2023). Among the five variant groups, variants of uncertain significance (VUS) are most debated in clinical practice. EuroGentest and the Canadian College of Medical Geneticists recommend periodic VUS reanalysis but give no guidance on reporting (Bears D.F. et al. 2018). ACMG advises excluding VUS from clinical decision-making (Richards S. et al. 2015).

Another concern in genomic data analysis is unsolicited or incidental findings (UFs), which are variants outside the initial testing purpose but medically significant for patients or relatives (Schoot V.V. et al. 2021). The European Society of Human Genetics recommends targeted analysis and evaluating UFs before routine use, while Genomics England and ACMG propose reporting based on informed consent for a clinically significant gene set unrelated to the test’s primary purpose (De Wert G. et al. 2021). ACMG guidelines manage such secondary findings and update gene lists where pathogenic variants should be reported, prioritizing high-penetrance genes with effective early prevention (Miller D.T. et al. 2021). Though aimed at clinical testing, these criteria may guide biobank return policies. Ethical, psychological, and security issues must also be considered.

Conclusion

The role of biobanks in genomics and personalized medicine research cannot be overemphasized and is highlighted by population allele frequencies, genotyping panels, imputation/linkage analysis and population centered Polygenic risk scores. In addition, Clinical translation and more extensive research are made possible by evolving biobank collections with high throughput data.

References

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About the Author

Tijani Abdulrahman Debo

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Bsc. Zoology, BMLS, Msc. Biobanks & Complex data management (In view)

Tijani Abdulrahman is a medical laboratory scientist and a biobanking professional with 10+ years of demonstrated history in working in the medical science practice industry. Skilled in laboratory medicine, biobanking, quality management system and biomedical research. Currently a master’s student at the University Cote d’Azur, France with focus on Biobanking and complex data management. With a multidisciplinary background Tijani Abdulrahman strives to champion biobank and biomedical enabled research in order to strengthen biobank and biomedical innovation.

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