Feng, Xiaodong_ Xie, Hong-Guang - Applying pharmacogenomics in therapeutics-CRC Press (2016)

Applying Pharmacogenomics in the Therapeutics of Pulmonary Diseases
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response to a particular drug may be affected by the activity of drug-metabolizing
enzymes, whose expression can be regulated by local (cis-acting) SNPs (expression
quantitative trait loci or eQTL). A patient with a gain-of-function variant allele in
the protein-coding region of a cytochrome P450 (CYP) enzyme may have higher
catalytic activity to metabolize its substrate drug, thus rendering the patient to
require higher doses for effective treatment. Specifically, taking advantage of the
high-quality genotyping data that may be available through high-throughput genotyping
and sequencing platforms, pharmacogenetic and pharmacogenomic studies
aim to elucidate the contributions of genetic variants to interindividual variability
of drug response.
GENERAL PHARMACOGENETIC AND
PHARMACOGENOMIC APPROACHES
Depending on the hypothesis, there are two general strategies used to elucidate the
genetic basis of drug response: the candidate gene approach and the whole-genome
approach. For the former, the search for drug response–associated genetic variants
is within a well-defined set of genes and/or pathways. Previous studies have
implicated genetic variations related to many mechanisms that may be relevant
to drug therapy, through their effects on the genes encoding drug-metabolizing
enzymes, transporters, and receptors, as well as their effects on pharmacokinetics
(affecting drug concentrations) and pharmacodynamics (affecting drug action)
characteristics of a drug. 6 For example, P450 enzymes are a superfamily of hemecontaining
proteins expressed abundantly in the hepatocytes, enterocytes, and in
the lung, kidney, and brain. P450 enzymes CYP1, CYP2, and CYP3 are among the
major families responsible for the oxidative metabolism of drugs and environmental
chemicals.
In contrast, the whole-genome approach or GWAS scans the entire human
genome for genetic variants associated with a particular complex trait. The wholegenome
approach is unbiased because it does not require prior knowledge of candidate
genes or pathways. GWAS has been a powerful tool for elucidating genetic
variants for complex traits, by assuming the common variant–common disease/
trait hypothesis, which predicts that common disease-causing alleles, or variants,
will be found in human populations that manifest a given trait. In population
genetics, linkage disequilibrium (LD) is a general characteristic in the human
genome. 7 The existence of LD suggests nonrandom association of two alleles at
two or more loci in the human genome, thus allowing the possibility of “tagging”
causal variants by other known or genotyped variants. The technical advances of
the past decade have allowed various cost-efficient approaches, including microarray
based and sequencing based, for genotyping tens of thousands or millions of
SNPs in one experiment. Therefore, by taking advantages of the LD characteristics
in the human genome and the genotyping technologies, GWAS-based statistical
methods for testing associations facilitate the applications of the whole-genome
approach in detecting variants associated with complex traits, including response
phenotypes of drugs.