Brief introduction to current pharmacogenomics research tools

Eun-Young Cha; Hye-Eun Jeong; Woo-Young Kim; Ho Jung Shin; Ho-Sook Kim; Jae-Gook Shin

doi:10.12793/tcp.2016.24.1.13

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Cha, Jeong, Kim, Shin, Kim, and Shin: Brief introduction to current pharmacogenomics research tools

Original Article

Translational and Clinical Pharmacology 2016;24(1):13-21.

Published online: 15 March 2016

DOI: https://doi.org/10.12793/tcp.2016.24.1.13

Brief introduction to current pharmacogenomics research tools

Eun-Young Cha¹, Hye-Eun Jeong¹, Woo-Young Kim¹, Ho Jung Shin¹, Ho-Sook Kim^1,^2,^∗, Jae-Gook Shin^1,²

¹Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Korea

²Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan 47392, Korea

^∗Correspondence: H. S. Kim; Tel: +82-51-890-6748, Fax: +82-51-893-1232, E-mail: hosuegi@gmail.com

Received 1 January 201612 January 2016 Accepted 21 January 2016

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

There is increasing interest in the application of personalized therapy to healthcare to increase the effectiveness of and reduce the adverse reactions to treatment. Pharmacogenomics is a core element in personalized therapy and pharmacogenomic research is a growing field. Understanding pharma-cogenomic research tools enables better design, conduct, and analysis of pharmacogenomic studies, as well as interpretation of pharmacogenomic results. This review provides a general and brief introduction to pharmacogenomics research tools, including genotyping technology, web-based genome browsers, and software for haplotype analysis.

Keywords: Genotyping technology, Pharmacogenomics research tools, Software for haplotype analysis, Web-based genome browsers

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Figure 1.

Schematic representation of SNP detection method. (A) TaqMan probe method. The template DNA is combined with primers and fluorophore-labeled allele specific probes, such as FAM labeled-allele 1 probe and VIC labeled-allele 2 probe. When a FAM-labeled allele 1 probe perfectly complements the target SNP site at allele 1, the FAM is released by the 5' nuclease activity of Taq polymerase. Release of the FAM separates the 3' quencher, allowing FAM to be emitted and subsequently detected as homozygotes of SNP at allele 1. In contrast, the VIC signal indicates homozygotes of SNP at allele 2. Fluorescences from both signals indicate heterozygotes. (B) Single base extension method. Extension primers are designed a single base upstream from the target SNP. During polymerase reactions, extension primers are bound a single base upstream of SNP site and ddATP are bound and extended to the target SNP site at allele 1, and the reaction is terminated. In contrast, ddTTP are bound and terminated to the target SNP site at allele 2. The incorporated base is detected using fluorescence. (C) Goldengate assay. Genomic DNA is activated by binding streptavidin/biotin beads. Both primers (ASOs and LSO) are hybridized to the genomic DNA-bound streptavidin/biotin beads. Extension of the appropriate ASO and ligation of the LSO generates ligation products. This product is amplified using dye-labeled universal PCR primers, and then fluorescence is used for signal detection. (D) GeneChip Microarray. ^① Two genomic-specific flank regions are hybridized at genomic DNA ^② The gap is filled with complementary base of target SNP and ^③ ligated ^④ cleavage site is digested by exonuclease, then the inversion probe is amplified by PCR reaction.

Figure 2.

An example of what a Linkage Disequilibrium (LD) Map looks like (triangle format). This is a Linkage disequilibrium (LD) blocks structure of the inflammatory bowel disease 5 (IBD5) gene in chromosome 5q31-q33. The white line on top represents a strand of a chromosome. The black bars on the white line of the chromosome are SNPs (Single nucleotide polymorphism) that have been identified and sequenced. These SNP locations or loci are labeled in this picture as 1, 2, 3 and so on (#1∼20 in this case). The kilobase (kb) in each LD blocks means the distance between first of SNP and end of SNP. The values in diamond represent the D' values (×100) between the two SNPs. For example, the diamond in which the columns leading from SNP#1 and SNP#7 intersect has a number, 95 with red color. Thus SNP#1 and SNP#7 have a D' value of 0.95 and are in high linkage disequilibrium with each other. The color is categorized according to D' value (D' ≥ 0.80, red; 0.5 ≤ D' < 0.8, pink; 0.2 ≤ D' < 0.5, blue; and D' < 0.2, white).

Table 1.

Comparative analysis of commonly used genotype platforms

Assay name	Reaction principle	SNP number	Sample number∗	Flexibility	Advantages	Limitation	Reference
RFLP	Restriction enzyme reaction	1 SNP	Small samples	Fixed	Easy access	User-defined detection error	[8]
Taqman	5'nuclease reaction	1 SNP	Large samples	Fixed	Easy access/ Real-time	Uniplex only	[9,10]
Fluidigm	5'nuclease reaction	48 or 96 SNPs	Large samples	Semifixed	High multiplexing/ Flexibility	Specialized equipment	[11]
Pyrose-quencing	Primer extension	∼3 SNPs	Middle samples	Fixed	Quantitation/ Semi-multiplex	Difficult to design multiplex/ expensive	[14,15]
SNaPshot	Primer extension	∼12 SNPs	Middle samples	Flexible	Multiplexing/ High accuracy	Multiple steps	[16]
Sequenom	Primer extension	40∼50 SNPs	Middle samples	Flexible	Multiplexing/ High throughput/ High accuracy	Multiple steps/ Specialized equipment	[17]
GoldenGate	Oligonucleotide ligation	384∼1536 SNPs	Various numbers of sample	Fixed	High accuracy	Multiple detection steps/ Specialized equipment	[19]
GeneChip array	Oligonucleotide ligation	10K∼11M SNPs	Various numbers of sample	Fixed V	Very high throughput CNV detection	t/ Complex experimental steps / Expensive/ Specialized equipment	[20]

^∗ Sample number is categorized as small< 1500, middle 1500<∼< 5000, and large> 5000, K: Kilobase, M: Millionbase.

Table 2.

Comparison of haplotype software

Contents	PHASE	Haploview	PLINK
Algorithm	Bayesian	Expectation-Maximization	Customized
SNP/CNV/Multi Allelic	○	x	○
Handling missing data	○	○	○
Haplotype Inference	○	○	○
Diplotype-based analysis	○	x	○
Visualization of LD block	x	○	○
Statistical analysis for association study	x	○	○
Input type	Text	6 Formats∗	Customized
Export type	Text	PNG, text	Customized

^∗ 6 Formats: Linkage format, Haps format, HapMap format, HapMap PHASE, HapMap Download, PLINK format, Abbreviation; SNP: Single Nucleotide Polymorphism, CNV: Copy Number Variation, LD: Linkage Disequilibrium, PNG: Portable Network Graphics.

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