1. Pharmacogenomic Profiles from 1,092 Human Genomes
Kleanthi Lakiotaki1, Evgenia Kartsaki1, Alexandros Kanterakis1, George P. Patrinos2 and George Potamias1
1Institute of Computer Science, Foundation for Research & Technology – Hellas, Heraklion, Crete {kliolak, ekartsak, kantale,
2Department of Pharmacy, University of Patras, Hellas,
Pharmacogenomics (PGx) holds promise to personalize medical interventions by determining genetic influence in drug response and enabling tailor-made drug prescription according to an individual’s genetic makeup.
Motivations. Drug response varies among individuals, ranging from expected beneficial effects to adverse reactions, and sometimes to even fatal events Various pharmacogenes relate and affect drug response Different populations carry different profiles of rare and common gene variants In this work we study how 72 ADMET (core) genes and their variants (500 SNP biomarkers) affect the PGx metabolizer status of Genomes (1kG) samples across 14 populations following an elaborative PGx translation process. ★This work is carried out in the context of the eMoDiA (electronic Molecular Diagnostics Assistant) project.
Findings A significant portion (218 out of 718, ~30%) of SNP biomarkers in known and well-studied pharmacogenes is not covered by 1kG sample genotype profiles  PGx metabolizing profiles differ significantly among 1kG samples in most (~75%) of the studied pharmacogenes
PGx metabolizing profiles exhibit a statistical significant variation among the different 1kG populations
Methodology. We developed an automated PGx translation algorithm, which infers metabolizer phenotypes (extensive, intermediate, poor/ultra) from individual genetic (SNP) profiles. For each pharmacogene, and based on available (PharmGKB) haplotype/allele tables, an individual’s genotype-profile is matched against the available gene-alleles. Next, each inferred allele is assigned to a metabolizer phenotype, according to available “look up” tables. [the algorithm was verified with the Affymetrix© DMET Plus respective translation results]
Pharmacogene - any evidence based gene related to drug Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET);
Pharmacogenomic marker - any Single Nucleotide Polymorphism (SNP) present in a pharmacogene locus.
Tools. Pharmacogenomics Knowledge Base (PharmGKB), collects, curates and disseminates knowledge about the impact of human genetic variation on drug responses. Affymetrix DMET™ Console Analysis Software provides reporting and translation from genotypic data to predicted metabolizer status for the most clinically relevant genes.
eMoDiA: electronic Molecular Diagnostic Assistant
Personalization
Quality of information
Cost
Updatable
Current scientific Knowledge bases × √
Commercial Direct To Consumer (DTC) companies ?
eMoDiA: electronic Molecular Diagnostic Assistant
To integrate heterogeneous PGx information from several valid PGx resources (PharmGKB, Ensembl …)
To offer automated personalized PGx translation (genotype-to- phenotype) services
To provide a user friendly interface for submitting newly discovered PGx related gene-variants and alleles
PGx Coverage of 1kG
PGx Profile Variation among 1kG Populations
The PGx (SNP) markers are shifted towards the more frequent band of the 1kG MAF spectrum.
This finding highlights the well- studied nature of PGx markers that are located solely in gene areas !
About ~30% (220 out of 728) PGx SNPs are not covered by 1kG
PGx metabolizer profiles vary among genes – e.g., all samples are extensive metabolizers in CYP2R1 and CYP2S1 genes, whilst most of the samples are either Poor or Ultra-rapid Metabolizers (PM_or_UM) in CYP2A6. For CYP2C19 and CYP3A4 most of the samples did not match to any (known so-far) PGx diplotype.
CYP2D6, one of the most important pharmacogenes, with a large contribution of genetic variation to the inter-individual variation in enzyme activity, involved in the metabolism of up to 25% of commonly used clinical drugs, is covered by only 50% in 1kG.
CFTR gene that is involved in Cystic Fibrosis, maybe the most common life-limiting autosomal recessive disease among people of European heritage, is slightly covered by only 8%.
About ~50% (396 out of 786) PGx haplotypes are not covered by 1kG
For CYP2D6, 1kG covers just a limited number of the available haplotypes (~3) compared to eMoDiA PGx that covers ~65 ! … the same holds for CFTR.
Although 64% of PGx genes are adequately covered by 1kG (in terms of SNPs), 26% of them lack PGx haplotype information.
25% of 1kG samples are Poor or Ultra-rapid Metabolizers (PM_or_UM) in 44% of pharmacogenes.
1kG populations exhibit different Poor or Ultra-rapid Metabolizing (PM_or_UM) patterns among pharmacogenes.
Inferential Statistical Analysis of 1,092 PGx Phenotype Profiles
H0: Pharmacogenomics metabolizer status does not vary among 1kG populations at a significance level a= (Bonferroni corrected)
CYP3A7
CYP4A22. The most likely significant PGx profile variation among 1kG populations was found in CYP4A22 and is attributed mainly to the PUM (Poor_or_UltraRapid) profile of LWK [χ2(18, N=202)=208,35, p< ]
BRCA1. PUM PGx profiles of African ancestry population, and EM PGx profiles of Asian ancestry population exhibit a significant pattern variation in BRCA1 [χ2(26, N=553)=510,80, p< ]
PIK3CA. All Asian (CHB, CHS, JPT) and African (ASW, LWK, YRI) populations contribute to the significance of PGx variation in PIK3CA, however their PGx profile contribution is complementary [χ2(26, N=1092)=469,07, p< ]
Europeans are the major influencers of PGx variation in CYP3A7, due to their significant increase in Poor or Ultra-rapid (red) metabolizer profiles, in contrast to African population [χ2(26, N=1092)=435,77, p< ]
The presented wok is supported by the (Greek-funded) eMoDiA project (11SYN_10_145) in the context of the COOPERATION 2011 program