Vilo, J. | Search bioRxiv

bioRxiv2018

Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions

PurposeBiomedical databases combining electronic medical records, phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype-phenotype associations.\n\nMethodsWe developed and tested algorithms for translation of pre-existing genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by whole genome sequencing, whole exome sequencing and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia.\n\nResultsOur most striking result was that the performance of genotyping arrays is similar to that of whole genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants.\n\nConclusionWe find that microarrays are a cost-effective solution for creating pre-emptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.

pharmacology and toxicology↗

bioRxiv2018

Healthspan pathway maps in C. elegans and humans highlight transcription, prolifera-tion/biosynthesis and lipids

The molecular basis of aging and of aging-associated diseases is being unraveled at an increasing pace. An extended healthspan, and not merely an extension of lifespan, has become the aim of medical practice. However, a precise definition of health and healthspan is not straightforward, and the causal molecular basis of health \"per se\" is largely unknown. Here, we define health based on the absence of diseases and dysfunctions. Based on an extensive review of the literature, in particular for humans and C. elegans, we compile a list of features of health and of the genes associated with them. Clusters of these genes based on molecular interaction data give rise to maps of healthspan pathways for humans, featuring the themes transcription initiation, proliferation and cholesterol/lipid processing, and for C. elegans, featuring the themes immune response, mitochondrion and biosynthesis based on genetic and compound intervention data, and lipids, biosynthesis and transcription based on WormBase compound intervention data. Overlaying healthspan-related gene expression data (describing effects of metabolic intervention associated with improvements in health) onto the aforementioned healthspan pathway maps, we observe the downregulation of Notch signalling in humans and of proliferation/cell-cycle in C. elegans. The former reflects the proinflammatory role of the Notch pathway. We identify transcription, proliferation/biosynthesis and lipids as a common theme on the annotation level, and proliferation-related kinases on the gene/protein level. Our literature-based data corpus, including visualization, is available as a reference for future investigations, at http://www.h2020awe.eu/index.php/pathways/.

bioinformatics↗