A statistical model for high-resolution mapping of quantitative trait loci determining HIV dynamics

Zouheng Wang, Rongling Wu

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Are there specific genes that control the pathogenesis of HIV infection? This question, which is of fundamental importance in designing personalized strategies of gene therapy to control HIV infection, can be examined by genetic mapping approaches. In this article, we present a new statistical model for unravelling the genetic mechanisms for the dynamic change of HIV that causes AIDS by marker-based linkage disequilibrium (LD) analyses. This new model is the extension of our functional mapping theory to integrate viral load trajectories within a genetic mapping framework. Earlier studies of HIV dynamics have led to various mathematical functions for modelling the kinetic curves of plasma virions and CD4 lymphocytes in HIV patients. Through incorporating these functions into the LD-based mapping procedure, we can identify and map individual quantitative trait loci (or QTL) responsible for viral pathogenesis. We derive a closed-form solution for estimating QTL allele frequency and marker-QTL linkage disequilibrium in the context of EM algorithm and implement the simplex algorithm to estimate the mathematical parameters describing the curve shapes of HIV pathogenesis. We performed different simulation scenarios based on currently used clinical designs in AIDS/HIV research to illustrate the utility and power of our model for genetic mapping of HIV dynamics. The implications of our model for genetic and genomic research into AIDS pathogenesis are discussed.

Original languageEnglish (US)
Pages (from-to)3033-3051
Number of pages19
JournalStatistics in Medicine
Volume23
Issue number19
DOIs
StatePublished - Oct 15 2004

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'A statistical model for high-resolution mapping of quantitative trait loci determining HIV dynamics'. Together they form a unique fingerprint.

Cite this