Download 1000_Genomes_Project_and_beyond__ABSTRACT

PHARMACOGENOMIC PROFILES FROM 1,092 HUMAN GENOMES
Kleanthi Lakiotaki1, Evgenia Kartsaki1, Alexandros Kanterakis1, George P. Patrinos2 and
George Potamias1
Institute of Computer Science, Foundation for Research & Technology – Hellas, Heraklion,
Crete {kliolak, ekartsak, kantale, potamias}@ics.forth.gr
2
Department of Pharmacy, University of Patras, Hellas, [email protected]
1
Clinical response to medication often varies among individuals, ranging from expected
beneficial effects to adverse reactions, and sometimes to even fatal events. Pharmacogenomics
(PGx) holds promise to personalize medical interventions by determining genetic influence in
drug response and therefore enabling tailor-made drug prescription according to an individual’s
genetic makeup. Various genomic loci, also referred to as “pharmacogenes” that are related to
drug absorption, distribution, metabolism, excretion and toxicity (ADMET), affect the
physiological function of the resulting proteins and enzymes and give rise to inter-individual
variation in both gene expression and the level of the corresponding gene product [1].
The availability of extensive genotypic data from the 1000 genomes project has opened the
floodgates to researchers to study human genetic variation in several research areas, including
pharmacogenomics. Different populations carry different profiles of rare and common variants
[2]. In this work, we study how genetic variations cause differences in drug response. In
particular, we apply methods and tools developed during the design and implementation of an
electronic molecular pharmacogenomics assistant [3], to the 1092 samples of the 1000
genomes project that belong to 14 different populations. We first extracted pharmacogenomics
variants (variants related to pharmacogenes) and by analyzing 500 biomarkers we assigned a
pharmacogenomic profile to every individual for 41 different core pharmacogenes1, which in turn
are related to 200 drugs. Thirty percent of those PGx profiles show abnormal (either reduced or
increased) drug response. Our analysis show that PGx profile distribution differs significantly in
certain genes among the populations analyzed. In the case of CYP3A5 gene, for example, only
5% of Africans, 64% of Americans, 50% of Asians and 89% of Europeans are predicted to be
normal metabolizers, while in UGT1A5 more than 90% of all individuals are predicted to be
normal metabolizers. Individuals of African Ancestry exhibit greater PGx profile variation in most
genes among populations, which can be attributed to their increased genetic heterogeneity.
[1]
[2]
[3]
1
K. Kampourakis, E. Vayena, C. Mitropoulou, R. H. van Schaik, D. Cooper, J. Borg, and G. P. Patrinos, “Key
challenges for next generation pharmacogenomics,” EMBO Rep., vol 15, no 5, pp. 472-476, 2014.
G. R. Abecasis, A. Auton, L. D. Brooks, M. a DePristo, R. M. Durbin, R. E. Handsaker, H. M. Kang, G. T.
Marth, and G. a McVean, “An integrated map of genetic variation from 1,092 human genomes.,” Nature, vol.
491, no. 7422, pp. 56–65, Nov. 2012.
K. Lakiotaki, G. P. Patrinos, and G. Potamias, “Information Technology meets Pharmacogenomics : Design
Specifications of an Integrated Personalized Pharmacogenomics Information System,” in IEEE-EMBS
International Conferences on Biomedical and Health Informatics, 2014, pp. 13–16.
http://www.pharmgkb.org/