1. Combining –omics to study the host and the virus Jacques Fellay School of Life Sciences École Polytechnique Fédérale de Lausanne - EPFL Lausanne, Switzerland IAS Workshop 2 July 2013

3. Genome-wide association studies Sequencing studies >5% <<<<<1% Allele frequency of variant +++ + ++ Clinical impact How to look for associations with DNA variants?

4. Infection Exposure Viral control - disease progression Resistance - acquisition HIV host genetic studies: clinical phenotypes

5. Science 2010 Dec 10;330(6010):1551-7 Science 2007 Aug 17;317(5840):944-7 Science 1996 Sep 27;273(5283):1856-62

6. 1. More common variants?  Meta-analysis of GWAS data 2. Rare functional variants?  Sequencing 3. Host impact on viral sequence?  “Genome-to-genome” interaction analysis Where do we go from here?

7. More common variants?

8. International Collaboration for the Genomics of HIV Objective: combine existing GWAS data from HIV+ cohorts to conduct joint analyses: -of viral control and/or disease progression -of HIV susceptibility: After QC and imputation, comparison between 6300 HIV infected cases and 7300 population controls over 5x10 6 variants

9. B*57:01 B*27:05

10. Frailty bias Due to their shorter survival time, patients with rapid disease progression are underrepresented in “chronic” cohorts, while individuals with prolonged disease-free survival times are more likely to be included rs4418214 p=0.01 Analysis restricted to patients with known date of infection

11.  HIV acquisition: no significant associations (after accounting for survivor bias), with the exception of CCR5Δ32 homozygosity: p=3E- 13. No replication of all other previously reported associations (N=22)  McLaren et al., PLoS Pathogens, in press  HIV control: analyses are ongoing International Collaboration for the Genomics of HIV

12. Rare functional variants?

13.  Polymorphisms of strong effect are kept at low frequency by evolutionary forces  Rare, functional variants are not well represented by GWAS  Sequencing has been highly successful for uncovering causes of rare Mendelian diseases

14. DNA extraction and quantity normalization Target enrichmentVariant calling and frequency estimation DNA pooling and bar-coding Sequencing (paired end reads) Alignment and base quality recalibration T/C Association testing with HIV VL Single variant Gene burden www.broadinstitute.org/gatk Patient sample N=400 Variant annotation snpeff.sourceforge.net CAA GTA AAC ATA GGA CTT CTT CAA GTA AAC ATA GGA CAT CTT

15. Exome sequencing performance MetricScore Mean coverage73x % Covered >5x94.0% Call rate99.9% GWAS concordance99.0% Per sampleScore Total non-ref16,105 Non-synonymous8,122 Loss of function39 Ti/Tv3.21

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)

17. No single variant associates with spVL after accounting for known signals Single variant results (MAF > 1%)

18. Burden testing siRNA Screens Interacting Proteins Gene-based (~20,000 tests) Set-based

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

20. 1. Good phenotypes are hard to get: -Long follow-up of patients -Close collaboration with clinicians -It’s 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

21. Host genomics Host-pathogen genomics

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

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

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)

25. Human SNPs HIV sequence mutations Viral Load Human SNPs HIV sequence mutations Viral Load

26. SNPs, HLA and CTL epitopes

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<0.001)

28. Conclusions Using viral variation as an intermediate phenotype can be a sensitive method for detecting host associations Can be applied to other infectious diseases

29. HIV host genetics – the way forward 1.More samples –Host genetics of infectious disease outcome still lags far behind other complex traits in terms of power 2.More variants –Current technologies still do not provide a complete picture of human genetic variation 3.More phenotypes –Easily measured, intermediate phenotypes can provide a potentially powerful method for detection of important loci

30. All ICGH collaborators University of LausanneAmalio Telenti Microsoft ResearchDavid Heckerman Duke UniversityDavid Goldstein Genomic Technologies FacilityKeith Harshman Vital-IT Computing CenterIoannis Xenarios Paul McLaren Istvan Bartha Thomas Junier Samira Asgari Ana Bittencourt