Genomic architecture of pharmacological efficacy and adverse events

Abstract

The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.

Keywords: genetic architecture; genomics; heritability; linear mixed modeling; pharmacogenomics; polygenic architecture; polygenic modeling.

Figure 1

Contrasting the effect size and minor allele frequency range of pharmacogenomic variants versus variants from the NHGRI GWAS catalog. Black circles are the NHGRI GWAS catalog results, plotted by OR results in logarithmic (base10) scale versus minor allele frequency. Each green cross represents a replicated efficacy result for a pharmacogenomics study. Each red X represents a replicated toxicity result for pharmacogenomics. The solid lines represent the 80% power equivalent curves across minor allele frequency, from top to bottom for n = 1 × 10³, 10 × 10³ and 100 × 10³, respectively (assuming n/2 cases and n/2 controls). OR: Odds ratio.

Figure 2

Overview of polygenic analysis methods. On the left, the general work flow of using Mixed Linear Modeling pursued using the software genome-wide complex trait analysis. On the right, the general workflow of Polygenic Modeling. Both methodologies allow the user to identify multiple SNPs related to pharmacogenomics outcome, with different information resulting from each approach. GWAS: Genome-wide association study.

Figure 3

Uncovering the genetic etiology of pharmacogenomic traits: methodologies and data. Along the top of the figure: pharmacogenomics studies should incorporate multiple types of analyses, beyond GWAS moving forward. Lower part of the figure: pharmacogenomics methods need to incorporate multiple types of genomic data, and consider the importance of environment as a modifier. Combining these elements may to yield improved predictions of pharmacogenomics outcomes. Furthermore, detailed molecular genetics studies following up on genomic association discovery will be important for identifying robust biomarkers for clinical decision-making. GWAS: Genome-wide association study. DNA/histone lower part of figure adapted with permission from [142].