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  • Slide 1
  • Combining omics to study the host and the virus Jacques Fellay School of Life Sciences cole Polytechnique Fdrale de Lausanne - EPFL Lausanne, Switzerland IAS Workshop 2 July 2013
  • Slide 2
  • Slide 3
  • Genome-wide association studies Sequencing studies >5% 5x94.0% Call rate99.9% GWAS concordance99.0% Per sampleScore Total non-ref16,105 Non-synonymous8,122 Loss of function39 Ti/Tv3.21
  • Slide 16
  • Single variant results (MAF > 1%) MHC signal consistent with GWAS Can be explained by variation in HLA-B (B*57:01) and HLA-C (3 UTR)
  • Slide 17
  • No single variant associates with spVL after accounting for known signals Single variant results (MAF > 1%)
  • Slide 18
  • Burden testing siRNA Screens Interacting Proteins Gene-based (~20,000 tests) Set-based
  • Slide 19
  • HIV-specific sets from the literature I HIV dependency factors II HIV/Human PPI by MS III Interferon stimulated genes IV HIV interactome Union set = 2,791 Intersection (2 or more) = 292 Restrict analysis to non- synonymous and loss of function variants Burden testing No significant associations
  • Slide 20
  • 1. Good phenotypes are hard to get: -Long follow-up of patients -Close collaboration with clinicians -Its now unethical to observe the natural history of HIV infection 2. Clinical outcomes are quite far from potentially causal gene variants Host genomics of HIV disease: Limitations of clinical phenotypes
  • Slide 21
  • Host genomics Host-pathogen genomics
  • Slide 22
  • The principle of Genome-to-Genome analysis Escape mutations Host restriction factors leading to viral escape can be uncovered by searching for their imprints on viral genomes Genetic variants
  • Slide 23
  • HIV-1 genome-to-genome study 1100 study participants Caucasians infected with subtype B HIV-1 Paired genetic data: Human: genome-wide genotypes from GWAS HIV-1: full-length consensus sequence
  • Slide 24
  • 3 sets of genome-wide comparisons Human genetic variation HIV-1 amino acid variants Viral load 1 GWAS 1 proteome-wide association study (2077 linear regressions) 2077 GWAS (1 per variable HIV amino acid present in >20 samples)
  • Slide 25
  • Human SNPs HIV sequence mutations Viral Load Human SNPs HIV sequence mutations Viral Load
  • Slide 26
  • SNPs, HLA and CTL epitopes
  • Slide 27
  • Association of HIV-1 amino acids with VL Human SNPs HIV sequence mutations Viral Load No significant association Changes in VL for amino acid variants associated with rs2395029 / B*57:01 (p