Assessment of genotype-phenotype discordance using recalibrated genotype measures of HIV drug resistance

Music City Center 

HIV drug resistance is most commonly assessed by means of genotyping. However, these sequence-based predictions can be discordant with those of phenotype-based assays, which are often considered the gold-standard measure for resistance. Owing to cost and accessibility constraints, phenotyping is infeasible in many resource-limited settings with the highest prevalence of drug resistance. The publicly available genotype-phenotype data at the Stanford HIV Drug Resistance Database offer a unique opportunity to understand which mutations contribute to this discordance and potentially improve the predictive accuracy of the genotypic algorithm.
We have developed a statistical procedure that uses phenotype information to adjust the weights of the genotype-based prediction algorithm with the goal of reducing discordance between genotype- and phenotype-based drug resistance predictions. For each specific medication, we first model the relationship between phenotypic and genotypic scores via semiparametric regression, thus eliminating variance in phenotypic score attributable to genotypic score. We then regress residuals from this model on the mutation-indicator matrix to quantify each mutation's contribution to unexplained phenotypic variance.
Statistical challenges include censoring at the upper bound of the phenotype score, and properly accounting for false discovery rate in identifying mutations that drive unexplained variation. We account for these by implementing a Bayesian mixture model in the first step to allow for the probability of right-censoring, followed by a Bayesian regression in the second step which enables model selection via examination of posterior probabilities for each mutation coefficient.
We illustrate using data from the Stanford HIV Drug Resistance Database. Current work involves comparing adjusted scores with unadjusted scores in their ability to predict clinical endpoint data.

recalibrated genotype measures