Objectives Prior candidate gene studies have associated CYP2B6 516G→T [rs3745274] and 983T→C [rs28399499] with increased plasma efavirenz exposure. We sought to identify novel variants associated with efavirenz pharmacokinetics. Materials and methods Antiretroviral therapy-naive AIDS Clinical Trials Group studies A5202, A5095, and ACTG 384 included plasma sampling for efavirenz pharmacokinetics. Log-transformed trough efavirenz concentrations (Cmin) were previously estimated by population pharmacokinetic modeling. Stored DNA was genotyped with Illumina HumanHap 650Y or 1MDuo platforms, complemented by additional targeted genotyping of CYP2B6 and CYP2A6 with MassARRAY iPLEX Gold. Associations were identified by linear regression, which included principal component vectors to adjust for genetic ancestry. Results Among 856 individuals, CYP2B6 516G→T was associated with efavirenz estimated Cmin (P = 8.5 × 10−41). After adjusting for CYP2B6 516G→T, CYP2B6 983T→C was associated (P = 9.9 × 10−11). After adjusting for both CYP2B6 516G→T and 983T→C, a CYP2B6 variant (rs4803419) in intron 3 was associated (P = 4.4 × 10−15). After adjusting for all the three variants, non-CYP2B6 polymorphisms were associated at P-value less than 5× 10−8. In a separate cohort of 240 individuals, only the three CYP2B6 polymorphisms replicated. These three polymorphisms explained 34% of interindividual variability in efavirenz estimated Cmin. The extensive metabolizer phenotype was best defined by the absence of all three polymorphisms. Conclusion Three CYP2B6 polymorphisms were independently associated with efavirenz estimated Cmin at genome-wide significance, and explained one-third of interindividual variability. These data will inform continued efforts to translate pharmacogenomic knowledge into optimal efavirenz utilization.

Research in human genetics and genetic epidemiology has grown significantly over the previous decade, particularly in the field of pharmacogenomics. Pharmacogenomics presents an opportunity for rapid translation of associated genetic polymorphisms into diagnostic measures or tests to guide therapy as part of a move towards personalized medicine. Expansion in genotyping technology has cleared the way for widespread use of whole-genome genotyping in the effort to identify novel biology and new genetic markers associated with pharmacokinetic and pharmacodynamic endpoints. With new technology and methodology regularly becoming available for use in genetic studies, a discussion on the application of such tools becomes necessary. In particular, quality control criteria have evolved with the use of GWAS as we have come to understand potential systematic errors which can be introduced into the data during genotyping. There have been several replicated pharmacogenomic associations, some of which have moved to the clinic to enact change in treatment decisions. These examples of translation illustrate the strength of evidence necessary to successfully and effectively translate a genetic discovery. In this review, the design of pharmacogenomic association studies is examined with the goal of optimizing the impact and utility of this research. Issues of ascertainment, genotyping, quality control, analysis and interpretation are considered.

BACKGROUND Mitochondrial DNA (mtDNA) variation has been associated with time to progression to AIDS and adverse effects from antiretroviral therapy (ART). In this study, full mitochondrial DNA (mtDNA) sequence data from U.S.-based adult participants in the AIDS Clinical Trials Group (ACTG) study 384 was used to assess associations between mtDNA variants and CD4 T cell recovery with ART. METHODS Full mtDNA sequence was determined using chip-based array sequencing. Sequence and CD4 cell count data was available at baseline and after ART initiation for 423 subjects with HIV RNA levels <400copies/mL plasma. The primary outcome was change in CD4 count of ≥100 cells/mm3 from baseline. Analyses were adjusted for baseline age, CD4 cell count, HIV RNA, and naïve:memory CD4 cell ratio. RESULTS Race-stratified analysis of mtDNA variants with a minor allele frequency >1% revealed multiple mtDNA variants marginally associated (P < 0.05 before Bonferroni correction) with CD4 cell recovery. The most significant SNP associations were those tagging the African L2 haplogroup, which was associated with a decreased likelihood of ≥100 cells/mm3 CD4 count increase at week 48 in non-Hispanic blacks (adjusted OR=0.17; 95% CI=0.06–0.53; P=0.002). CONCLUSIONS An African mtDNA haplogroup was associated with CD4 cell recovery after ART in this clinical trial population. These initial findings warrant replication and further investigation in order to confirm the role of mtDNA variation in CD4 cell recovery during ART.

Personalized medicine is a high priority for the future of health care. The idea of tailoring an individual's wellness plan to their unique genetic code is one which we hope to realize through the use of pharmacogenomics. There have been examples of tremendous success in pharmacogenomic associations however there are many such examples in which only a small proportion of trait variance has been explained by the genetic variation. Although the increased use of GWAS could help explain more of this variation, it is likely that a significant proportion of the genetic architecture of these pharmacogenomic traits are due to complex genetic effects such as epistasis, also known as gene-gene interactions, as well as gene-drug interactions. In this study, we utilize the Biofilter software package to look for candidate epistasis contributing to risk for virologic failure with efavirenz-containing antiretroviral therapy (ART) regimens in treatmentnaïve participants of AIDS Clinical Trials Group (ACTG) randomized clinical trials. A total of 904 individuals from three ACTG trials with data on efavirenz treatment are analyzed after race-stratification into white, black, and Hispanic ethnic groups. Biofilter was run considering 245 candidate ADME (absorption, distribution, metabolism, and excretion) genes and using database knowledge of gene and protein interaction networks to produce approximately 2 million SNP-SNP interaction models within each ethnic group. These models were evaluated within the PLATO software package using pair wise logistic regression models. Although no interaction model remained significant after correction for multiple comparisons, an interaction between SNPs in the TAP1 and ABCC9 genes was one of the top models before correction. The TAP1 protein is responsible for intracellular transport of antigen to MHC class I molecules, while ABCC9 codes for a transporter which is part of the subfamily of ABC transporters associated with multi-drug resistance. This study demonstrates the utility of the Biofilter method to prioritize the search for gene-gene interactions in large-scale genomic datasets, although replication in a larger cohort is required to confirm the validity of this particular TAP1-ABCC9 finding.