4 th International Workshop on HIV & Aging Host Genomics of HIV-1 Paul McLaren École Polytechnique Fédérale de Lausanne - EPFL Lausanne, Switzerland paul.mclaren@epfl.ch
Complex trait genetics Phenotypic variation
Complex trait genetics Genetic variation (single nucleotide polymorphisms) Phenotypic variation
Complex trait genetics Environmental influences Genetic variation (single nucleotide polymorphisms) Phenotypic variation
Key developments in human genetics
<<<<<1% Allele frequency >5% Paradigm of complex trait genetics +++ ++ + Clinical impact
<<<<<1% Allele frequency >5% Paradigm of complex trait genetics Genome-wide association studies +++ ++ + Clinical impact
<<<<<1% Allele frequency >5% Sequencing studies Paradigm of complex trait genetics Genome-wide association studies +++ ++ + Clinical impact
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
The history and success of GWAS 2007 Data: www.genome.gov/gwastudies Slide courtesy of S Ripke and S Pulit
The history and success of GWAS 2008 Data: www.genome.gov/gwastudies Slide courtesy of S Ripke and S Pulit
The history and success of GWAS 2009 Data: www.genome.gov/gwastudies Slide courtesy of S Ripke and S Pulit
The history and success of GWAS 2010 Data: www.genome.gov/gwastudies Slide courtesy of S Ripke and S Pulit
The history and success of GWAS 2011 Data: www.genome.gov/gwastudies Slide courtesy of S Ripke and S Pulit
The history and success of GWAS 2012 Data: www.genome.gov/gwastudies Slide courtesy of S Ripke and S Pulit
>1,500 associations published for 240 traits Through 07-2012 http://www.genome.gov/gwastudies/
HIV host genetic studies: clinical phenotypes Disease progression Acquisition McLaren et al PLoS Path 2013 Infection
HIV host genetic studies: clinical phenotypes Disease progression Acquisition HIV Control McLaren et al PLoS Path 2013 Infection
www.hivcontrollers.org
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
MHC is the major genetic determinant of host control
MHC is the major genetic determinant of host control
MHC is the major genetic determinant of host control Science 1996 Sep 27;273(5283):1856-62
Multiple independent signals in the MHC Pereyra, Jia and McLaren et al, Science 2010
Multiple independent signals in the MHC Pereyra, Jia and McLaren et al, Science 2010
Multiple HLA-B alleles impact control/progression
HLA alleles are defined by amino acid sequence
HLA alleles are defined by amino acid sequence Progression Control Testing for association per position rather than per allele can identify key residues
Amino acid positions are more strongly associated than classical alleles Pereyra, Jia and McLaren et al, Science 2010
Amino acid positions are more strongly associated than classical alleles Pereyra, Jia and McLaren et al, Science 2010
Multiple alleles at key positions associate with host control and determine set point IHCS SHCS Pereyra, Jia and McLaren et al, Science 2010
Top associated amino acid positions map to the peptide binding cleft Pereyra, Jia and McLaren et al, Science 2010
Amino acid associations in HLA-B African Americans B*57:03 B*81:01 McLaren et al, Hum Mol Genet 2012
Amino acid associations in HLA-B African Americans B*57:03 B*81:01 McLaren et al, Hum Mol Genet 2012
Positions in and outside of the binding groove contribute to HIV control McLaren et al, Hum Mol Genet 2012
Science 1996 Sep 27;273(5283):1856-62
Science 1996 Sep 27;273(5283):1856-62 Science 2010 Dec 10;330(6010):1551-7 McLaren et al, Hum. Mol. Genet [In revision]
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
G2G: Closing the loop Human genetic variation 1 GWAS Viral load
G2G: Closing the loop Human genetic variation 1 GWAS Viral load 2077 GWASs (1 per variable HIV amino acid) HIV-1 sequence variation
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
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
Human SNPs Viral Load HIV sequence mutations Science 2007 Aug 17;317(5840):944-7
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
SNPs, HLA and CTL epitopes
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
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
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