Pharmacogenetic Recommendations for Personalised Medicine

Genetic variations between individual persons can lead to huge differences in how they respond to drugs. This is a major challenge for finding the right medication and dosage as well as for drug development. The study presented here is a first step towards understanding the underlying genetic mechanisms and aims to deduce recommendations for clinical practice.

In this study published in Genetics in Medicine, a team of geneticists and bioinformaticians around Dr. Lili Milani at the University of Tartu developed and tested algorithms for translation of preexisting genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations.

Dr. Sulev Reisberg

"We compared pharmacogenetic results obtained by genome sequencing, exome sequencing, and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia. Interestingly, almost all (99.8%) assessed individuals had a genotype associated with increased risks to at least one medication. We can calculate that the implementation of pharmacogenetic recommendations based on genotyping would affect at least 50 daily drug doses per 1000 inhabitants. From a methodological point of view, our most striking result was that the performance of genotyping arrays is similar to that of genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions.”

Dr. Sulev Reisberg, Institute of Computer Science, University of Tartu, STACC and Quretec, Tartu, Estonia. © Dr. Sulev Reisberg

Dr. Lili MilaniThe study suggests a number of technical steps to further improve the algorithms. In detail, these are: A further revision of pharmaco-genetically important allele definition tables based on existing haplotypes in different populations, an additional level of decision trees to prioritize variants causing nonfunctional alleles, and restricting the inclusion of rare alleles to functionally validated variants. With these improvements, the developed algorithms could be implemented into automated decision support tools for clinicians. This would allow the implementation of pharmacogenomics at the point of care in a multidisciplinary manner and with greater impact, leading to more personalised and effective treatments.

Dr. Lili Milani, Estonian Genome Center, Institute of Genomics, University of Tartu, Estonia; Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Sweden. © Renee Altnov

The Estonian biobank at the Estonian Genome Center of the University of Tartu now hosts DNA samples from 200,000 citizens and is actively contributing to the implementation of personalised medicine in Estonia.


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