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

Abstract

A telephone number and e-mail address to whom correspondence concerning the manuscript should be sent lili.milani@ut.ee +372 5304 5400 2 Conflict of interest notification pageConflict of interest V.M.L is a co-founder and owner of HepaPredict AB. Other authors have no conflict of interest to declare. ABSTRACT AND KEYWORDS 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. MethodsWe 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. ResultsOur 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. ConclusionWe 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.

Genetic insight into cytochrome P450 in Chinese from the Chinese Millionome Database

“…The diversity of drug responses in healthy participants in phase I clinical trials for one drug candidate could be minimized through sequencing the whole genome of patients and choosing the most homogenous ones. 8,29 The drug dosing scheme could be precise after it is marketed to be able to be adjusted to patients' genetic background. More interesting, the withdrawal or failed marketing of certain drugs is due to adverse drug reactions in patients with specific genetic variants, and NGS evidence would change the destiny of such drugs and have a cost-effectiveness benefit for the world.…”

“…Some genome sequencing projects of international and national populations have been carried out, such as the 1000 Genomes Project, the SweGen project, the Estonian Biobank and the UK100K Genomes Project. [6][7][8] These completed and ongoing projects mainly focus on Caucasian populations, and mainland Chinese are under-represented. The mainland Chinese population consists of approximately 1.4 billion people, and genetic screening of CYP2C9, CYP2C19 and CYP2D6 in 2127 healthy Han Chinese patients (1127 southern Chinese and 1000 northern Chinese) found additional alleles in addition to those previously identified.…”

Repurposing of Diagnostic Whole Exome Sequencing Data of 1,583 Individuals for Clinical Pharmacogenetics

“…Because difficulties with coverage and CNV calling are inherent limitations to the use of WES, it is difficult to solve these problems without resorting to other technologies like WGS or array‐based techniques. These techniques have been suggested by other groups as more suitable for PGx profiling . However, both these technologies have not been routinely implemented as extensively as WES in a diagnostic setting.…”

“…These techniques have been suggested by other groups as more suitable for PGx profiling. 8,29 However, both these technologies have not been routinely implemented as extensively as WES in a diagnostic setting. Applying these technologies will, therefore, lead to additional testing costs, whereas repurposing existing WES data does not.…”

Genetic counseling, 2030: An on‐demand service tailored to the needs of a price conscious, genetically literate, and busy world

“…Similar statistics have been shown for diseases like breast cancer (Khera et al, ), type 2 diabetes (Khera et al, ), Alzheimer's disease (Desikan et al, ), and more (Al Olama et al, ; Fritsche et al, ; Hagenaars et al, ). Coupled with advances in pharmacogenomics (Colombo et al, ; Shuldiner et al, ; Table of Pharmacogenomic Biomarkers in Drug Labeling, ), the amount of meaningful, impactful, clinically valid genetic information for a given individual continues to grow (Reisberg et al, ).…”