Personalized medicine: Genetic risk prediction of drug response

Abstract

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

Keywords: Complex PGx traits; Genetic architecture; Genetic risk prediction; Monogenic (Mendelian) PGx traits; Pharmacogenetics; Pharmacogenomics (PGx); Predominantly oligogenetic PGx traits.

Fig. 1

Phenotype distribution of different traits. A: a recessive Mendelian trait with two discrete phenotypes; B: a distinct codominant Mendelian trait with three discrete phenotypes; C: a quantitative trait –– controlled predominantly by a large-effect gene, and undoubtedly additional modifiers showing continuous distribution, with three distinct modes; D: a quantitative trait influenced by numerous genetic and environmental factors, i.e. a polygenic trait that follows a normal distribution.

Fig. 2

Distribution of measurable CYP2D6 phenotypes. Four phenotypic groups can be distinguished: ultra-rapid (UM), extensive, sometimes called “efficient” (EM), intermediate (IM), and poor (PM) metabolizers [modified from The Lancet 2000; 356: 1667–71].

Fig. 3

Effect-size (measured by variance explained, R²) that can be identified by GWAS as a function of different sample sizes [power = 80%, significance level α = 5 × 10⁻⁶ (green dashed line) and 5 × 10⁻⁸ (red solid line)]. As shown by the figure, GWAS with ~1000 samples have appropriate power (>80%) to detect genetic variants that are likely to explain ~4% of phenotypic variance. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Performance of diagnostic test for a dichotomized outcome (evaluated by AUC, area-under-curve) is principally determined by percentage of variance in phenotype that can be explained by predictors. The three curves illustrate the AUC as a function of phenotypic variance explained by predictive variable(s), providing different prevalences of 0.01 (red solid line), 0.1 (green dashed line), and 0.50 (blue dotted line), of the dichotomized outcome. Given the same level of variance explained, greater discriminatory power (i.e. larger AUC) can be obtained for less frequent outcomes. Generally, in order to achieve good discriminative power (AUC >80%; grey horizontal line), the predictors should explain at least 20%–40% phenotypic variance; however, for most complex PGx traits, the identified genetic variants explain far less phenotypic variance than this amount. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Different classes of drugs having FDA PGx labels.