HLA and HIV Infection Progression: Application of the Minimum Description Length Principle to Statistical Genetics

Peter T. Hraber, Bette T. Korber, Steven Wolinsky, Henry Erlich, Elizabeth Trachtenberg, , andhomas B. Kepler

03-04-023

The minimum description length (MDL) principle was developed in the context of computational complexity and coding theory. It states that the best model to account for some data minimizes the sum of the lengths, in bits, of the descriptions of the model and the data as encoded via the model. The MDL principle gives a criterion for parameter selection by using the description length as a test statistic. Class I HLA genes play a major role in the immune response to HIV, and are known to be associated with rates of progression to AIDS. However, these genes are highly polymorphic, making it difficult to associate alleles with disease outcome, given statistical issues of multiple testing. Application of the MDL principle to immunogenetic data from a longitudinal cohort study (Chicago MACS) enables classification of alleles associated with plasma HIV RNA abundance, an indicator of infection progression. We recently reported that MDL analysis of the relationship of HLA supertypes (a classification of alleles by epitope-binding anchor motifs) with HIV RNA levels identifies associations between human genotype and viral RNA. Details of the MDL approach and more extended analyses of HLA and viral RNA are described here. Variation in progression is strongly associated with HLA-B. Allele associations with viral levels support and extend previous studies. In particular, individuals without B58s supertype alleles average viral RNA levels 3.6-fold greater than individuals with them. Mechanisms for these associations include variation in epitope specificity and selection that favors rare alleles.