Use of Biological Knowledge to Inform the Analysis of Gene-Gene Interactions Involved in Modulating Virologic Failure With Efavirenz-Containing Treatment Regimens in Art-Naïve Actg Clinical Trials Participants

Abstract

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.

Worldwide Variation in Human Drug-Metabolism Enzyme Genes CYP2B6 and UGT2B7 : Implications for HIV/AIDS Treatment

“…It is worth noting that for each reported association, or where there was no apparent association, the implicated CYP2B6 / UGT2B7 variant differs considerably in frequency among ethnic groups. Although CYP2B6 and UGT2B7 variation has important clinical implications for HIV/AIDS treatment, recent studies indicate that the contributions of other drug-metabolism enzyme genes [47,85], drug-transporter genes [86], transcription factor genes [64,87] and protein–protein interactions [88] should not be discounted. Furthermore, in addition to SNPs, other key genomic mechanisms are likely to underly phenotypic variability; these include recently discovered alternative splicing of UGT2B7 , which gives rise to multiple mRNA splice variants with novel functions [89].…”

The genetics of alcohol dependence: Advancing towards systems-based approaches

“…Given its nature, parametric logistic regression cannot be employed in GEM studies because large sample sizes would be required. Alternatively, researchers may opt to limit the 1 million markers on a DNA micro array to only those belonging to genes in candidate pathways (given prior knowledge to select these markers; Grady et al, 2011). The likely alternative approach is the application of nonparametric methods that are currently being adapted to GWAS, such as MDR and HotNet (Vandin et al, 2012, 2011).…”

Analysis pipeline for the epistasis search – statistical versus biological filtering

“…It can build SNP–SNP models based on known interactions between genes and proteins in curated pathways and networks. Grady et al (2011) utilized the Biofilter software to look for epistasis contributing to the risk of virologic failure. Approximately two million SNP–SNP interaction models were produced by Biofilter, and Grady et al (2010) tested these models by using logistic regression via the software package PLATO.…”

“…Filtering SNPs based on their marginal effects is frequently used for a high-dimensional gene–gene interaction search. It is often combined with biological filtering to identify interactions among SNPs that are marginally associated with a phenotype (Baranzini et al, 2009; Grady et al, 2011; Turner et al, 2011; Ma et al, 2012; Pendergrass et al, 2013a). This approach follows the principles of hierarchical model building in the general linear model, where the interaction terms are tested only after all main-effect terms are deemed statistically significant.…”