Host Genomics of HIV-1

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1 4 th International Workshop on HIV & Aging Host Genomics of HIV-1 Paul McLaren École Polytechnique Fédérale de Lausanne - EPFL Lausanne, Switzerland

2 Complex trait genetics Phenotypic variation

3 Complex trait genetics Genetic variation (single nucleotide polymorphisms) Phenotypic variation

4 Complex trait genetics Environmental influences Genetic variation (single nucleotide polymorphisms) Phenotypic variation

5 Key developments in human genetics

6 <<<<<1% Allele frequency >5% Paradigm of complex trait genetics Clinical impact

7 <<<<<1% Allele frequency >5% Paradigm of complex trait genetics Genome-wide association studies Clinical impact

8 <<<<<1% Allele frequency >5% Sequencing studies Paradigm of complex trait genetics Genome-wide association studies Clinical impact

9 GWAS: A primer Common variants (>5% frequency) that cause (or tag variants that cause) disease can be detected by comparing variant frequencies in large samples of cases and controls General workflow: 1) Recruit patients and controls 2) Genotype at a large number of sites (>1M) 3) Compare variant frequency (controlling for confounders) 4) Find friends with similar samples and share data

10 The history and success of GWAS 2007 Data: Slide courtesy of S Ripke and S Pulit

11 The history and success of GWAS 2008 Data: Slide courtesy of S Ripke and S Pulit

12 The history and success of GWAS 2009 Data: Slide courtesy of S Ripke and S Pulit

13 The history and success of GWAS 2010 Data: Slide courtesy of S Ripke and S Pulit

14 The history and success of GWAS 2011 Data: Slide courtesy of S Ripke and S Pulit

15 The history and success of GWAS 2012 Data: Slide courtesy of S Ripke and S Pulit

16 >1,500 associations published for 240 traits Through

17 HIV host genetic studies: clinical phenotypes Disease progression Acquisition McLaren et al PLoS Path 2013 Infection

18 HIV host genetic studies: clinical phenotypes Disease progression Acquisition HIV Control McLaren et al PLoS Path 2013 Infection

19

20 Genome-wide SNP information acquired on 3,622 patients 1KG 16.8M SNPs 25.4M 1KG SNPs 15.6M 1KG SNPs After imputation test each SNP for association with HIV-1 control/progression

21 MHC is the major genetic determinant of host control

22 MHC is the major genetic determinant of host control

23 MHC is the major genetic determinant of host control Science 1996 Sep 27;273(5283):

24 Multiple independent signals in the MHC Pereyra, Jia and McLaren et al, Science 2010

25 Multiple independent signals in the MHC Pereyra, Jia and McLaren et al, Science 2010

26 Multiple HLA-B alleles impact control/progression

27 HLA alleles are defined by amino acid sequence

28 HLA alleles are defined by amino acid sequence Progression Control Testing for association per position rather than per allele can identify key residues

29 Amino acid positions are more strongly associated than classical alleles Pereyra, Jia and McLaren et al, Science 2010

30 Amino acid positions are more strongly associated than classical alleles Pereyra, Jia and McLaren et al, Science 2010

31 Multiple alleles at key positions associate with host control and determine set point IHCS SHCS Pereyra, Jia and McLaren et al, Science 2010

32 Top associated amino acid positions map to the peptide binding cleft Pereyra, Jia and McLaren et al, Science 2010

33 Amino acid associations in HLA-B African Americans B*57:03 B*81:01 McLaren et al, Hum Mol Genet 2012

34 Amino acid associations in HLA-B African Americans B*57:03 B*81:01 McLaren et al, Hum Mol Genet 2012

35 Positions in and outside of the binding groove contribute to HIV control McLaren et al, Hum Mol Genet 2012

36 Science 1996 Sep 27;273(5283):

37 Science 1996 Sep 27;273(5283): Science 2010 Dec 10;330(6010): McLaren et al, Hum. Mol. Genet [In revision]

38 Conclusions: HIV-1 control Genome wide scan of common genetic variation shows strong effects in the MHC Functional fine-mapping implicates amino acid positions within HLA-B as key to determining HIV control These explain most, but not all, of the SNP association signal Apps et al Science 2013 Next step: Meta-analysis of HIV-1 progression data from worldwide cohorts

39 G2G: Closing the loop Human genetic variation 1 GWAS Viral load

40 G2G: Closing the loop Human genetic variation 1 GWAS Viral load 2077 GWASs (1 per variable HIV amino acid) HIV-1 sequence variation

41 G2G: Closing the loop Human genetic variation 1 GWAS Viral load 2077 GWASs (1 per variable HIV amino acid) HIV-1 sequence variation 1 proteome-wide association study

42 HIV-1 genome-to-genome study 1,100 study participants Caucasians infected with subtype B HIV-1 Paired genetic data: Human: genome-wide genotypes from GWAS HIV-1: full-length consensus sequence

43 Human SNPs Viral Load HIV sequence mutations Science 2007 Aug 17;317(5840):944-7

44 Human SNPs Viral Load HIV sequence mutations Science 2007 Aug 17;317(5840):944-7 Human SNPs Viral Load HIV sequence mutations 2013 Oct 29; 2:e01123

45 SNPs, HLA and CTL epitopes

46 Conclusions Host genetics has a role to play in understanding infectious disease biology The MHC region is the major host genetic determinant of HIV outcome Majority of the genetic signal maps to the binding groove of HLA-B Integrating virus with host genetic data is a powerful new method for infectious disease association studies

47 Prospects for the future More samples GWAS data on >10,000 HIV patients obtained through collaboration ICGH More variants Rare and structural variants not covered by GWAS Deep sequencing More phenotypes Viral load is the tip of the phenotypic iceberg Where possible leverage existing data to answer new questions

48 Jacques Fellay Istvan Bartha Paul de Bakker Stephan Ripke Xiaoming Jia Amalio Telenti Bruce Walker Florencia Pereyra All collaborators in the IHCS, ICGH and G2G consortia

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