Genetic Screening and Big Data’s Influence on Drug Production and Biomarker Identification

The Human Genome Project (HGP) was largely successful in its goal to identify the genetic blueprint of life. The scientific community has begun to translate this genetic information to identify new potential therapeutic targets. Further progress may be possible by continuing to screen patients and identifying subsets from a larger disease population that have increased frequency or severity of the disease. These patients may be carrying a mutation (or mutations) that exacerbate the disease. These screens are useful predictors towards what genes correspond to what disease and also help to identify potential biomarkers that could be targeted for therapeutic use. 

Increasing the number and diversity of patients screened is a process currently being worked upon to find further instances of disease development. One such way to expand upon the genetic screen is through large amounts of pooled genetic data from large populations. There are a number of worldwide studies looking to compile and publish large datasets of whole genomes from volunteer samples. For instance, at the end of 2022, the UK Biobank project ( published 200,000 whole genomes from volunteer samples and promised another 300,000 by the end of 2023. The entire 3 billion base pairs of DNA will be made available from each volunteer, along with anonymized medical information. This data will allow for new insights that could not normally be found with the more commonly available partial sequences or scans of genomic markers. Interplay between the genetic information and health status will also give new avenues for disease identification and targeting. The one downside to the UK Biobank is that the volunteer population is not ethnically diverse as it is derived primarily from middle-aged and elderly volunteers coming from European ancestry. However, there are other whole genome biobanks being developed and published for worldwide use. One such, the biobank at the All of Us ( research program from the National Institutes of Health will offer a more diverse population base, which could help to find health disparities not seen in the UK Biobank. All of Us has a goal of reaching 1 million volunteer samples by its conclusion. This focus on expanding the diversity of the genomes analyzed should create a richer and more useful data set for analysis.

Exploiting the large amount of data to come out of these published biobanks will be fruitful for researchers worldwide in the pursuit of developing new therapeutics. There will be a need going forward for good analytical tools and mathematical models to best utilize this raw data.  Similarly, there will be a need for computational skills to be able to take big genomic data and parse out the specific areas where there could be genetic hits that would be useful for designing therapies. For example, from the UK Biobank, it has recently been announced that there were the genetics of 35 blood and urine biomarkers discovered. Some of these biomarkers were already known, such as a relationship between cystatin C and stroke along with urate and gout.  It is further strengthened when there are hits that are for relationships that are already determined, as is the case for the example listed above. These pooled datasets can be ranked statistically and will provide new insights into therapeutic potential.  

As the genomic biobank data is disseminated worldwide and mathematically investigated, so too will there be a need to test for corresponding markers of disease and therapeutic efficacy. By working with a bioanalytical testing lab with extensive experience in biomarker assays, such as BioAgilytix, companies translating this new information from whole genome datasets will have a leg up on the competition. BioAgilytix already has over 600 biomarker assays available in-house with over 120 of those fully validated. These numbers have grown and will certainly continue to grow as new biomarkers are identified. BioAgilytix has the capabilities to test vast numbers of samples to measure these identified biomarkers. The potential from whole genome biobanks will likely unlock new drug innovations and working with trusted and experienced partners like BioAgilytix will only decrease the time to market for any therapeutic.  







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