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Genetic and clinical predictors of CD4 lymphocyte recovery during suppressive antiretroviral therapy: Whole exome sequencing and antiretroviral therapy response phenotypes

Author

Listed:
  • Ruth Greenblatt
  • Peter Bacchetti
  • Ross Boylan
  • Kord Kober
  • Gayle Springer
  • Kathryn Anastos
  • Michael Busch
  • Mardge Cohen
  • Seble Kassaye
  • Deborah Gustafson
  • Bradley Aouizerat
  • on behalf of the Women’s Interagency HIV Study

Abstract

Increase of peripheral blood CD4 lymphocyte counts is a key goal of combined antiretroviral therapy (cART); most, but not all, recipients respond adequately and promptly. A small number of studies have examined specific genetic factors associated with the extent of CD4 recovery. We report a genome-wide examination of factors that predict CD4 recovery in HIV-infected women. We identified women in in a cohort study who were on cART with viral load below 400 copies, and drew racially and ethnically matched samples of those with good CD4 response over 2 years or poor response. We analyzed the exomes of those women employing next generation sequencing for genes associated with CD4 recovery after controlling for non-genetic factors identified through forward stepwise selection as important. We studied 48 women with good CD4 recovery and 42 with poor CD4 recovery during virologically-suppressive cART. Stepwise logistic regression selected only age as a statistically significant (p

Suggested Citation

  • Ruth Greenblatt & Peter Bacchetti & Ross Boylan & Kord Kober & Gayle Springer & Kathryn Anastos & Michael Busch & Mardge Cohen & Seble Kassaye & Deborah Gustafson & Bradley Aouizerat & on behalf of th, 2019. "Genetic and clinical predictors of CD4 lymphocyte recovery during suppressive antiretroviral therapy: Whole exome sequencing and antiretroviral therapy response phenotypes," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-25, August.
    • Handle: RePEc:plo:pone00:0219201
    DOI: 10.1371/journal.pone.0219201
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    References listed on IDEAS

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    1. Dajiang J Liu & Suzanne M Leal, 2010. "A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions," PLOS Genetics, Public Library of Science, vol. 6(10), pages 1-14, October.
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