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Determinants of host response to HIV-1: the role of rare and common variants.

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Presentation on theme: "Determinants of host response to HIV-1: the role of rare and common variants."— Presentation transcript:

1 Determinants of host response to HIV-1: the role of rare and common variants

2 Infection Exposure Genetics of viral control Genetics of resistance Genetics of vaccine trials Host Genetics portfolio

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4 Telenti A & Goldstein DB, Nat Rev Microbiol 2006 Phenotype

5 IrsiCaixa Barcelona, Spain B. Clotet Clinics Hospital Barcelona, Spain J.M. Gatell Swiss HIV Cohort University Hospital, Lausanne Switzerland (coordinating center) A. Telenti P. Francioli Danish Cohort Denmark Denmark N. Obel N. Obel San Raffaele Hospital Milan, Italy A. Castagna Modena Cohort Modena, Italy A. Cossarizza I.CO.NA Cohort Rome, Italy A. De Luca Guy Kings St.Thomas Hospital United Kingdom United Kingdom P. Easterbrook P. Easterbrook Royal Perth Hospital Perth, Australia Perth, Australia S. Mallal S. Mallal the EuroCHAVI consortium

6 WGAViewer: gene context annotation (HLA-C, HLA-B, HCP5) Showing all SNPs genotyped in this region sorted by p-value or functionality HLA-C, rs HLA-B*5701/HCP5, rs

7 WGAViewer: SNP annotation (HLA-C, rs ) Showing all HapMap SNPs not genotyped in this region sorted by r 2 or functionality

8 Gene & SNP P-value for association with HIV-1 viral load at setpoint N=2362 P-value for association with protection against progression (CD4 <350) N=1071 HCP5 / HLA-B*5701 rs E-351.2E-11 HLA-C rs E-327.4E-12 ZNRD1 / RNF39 rs E-043.8E-08 CCR5 Δ32 het rs E-102.6E-06 CHAVI set point study: global results Bonferroni threshold for genome-wide significance: 5E-08

9 Independence of the HCP5 and HLA-C association signals The HCP5 and HLA-C variants are in partial LD (r2=0.06, D=0.86) the combined strength of their associations is less than the sum of the signals measured separately nonetheless, a nested regression model clearly demonstrates that each of these variants is independently genome-wide significant: rs : p=1.8E-23 rs : p=2.4E-20

10 Independence of the ZNRD1 association signal The variants in the ZNRD1 region, 1Mb away from HLA-B and HLA-C, are not in LD with the top 2 SNPs The strength of their association signal is the same in models including the HCP5/HLA-C SNPs The identified association signal is likely to be synthetic (high LD in a 150kb region that includes 12 genes or pseudogenes, notably HLA-A)

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12 Independent replications of associations HCP5/B*5701 HLA-C: rs was not genotyped, but is in LD with the top hit, rs , which also associates with HLA-C expression HCP5/B*5701 HLA-C ZNRD1: in haplotypes that contain HLA-A10 HCP5/B*5701 ZNRD Jan 2;23(1):19-28 HCP5/B*5701 HLA-C 2008 Dec 30. [Epub ahead of print] 2008;3(12):e3907. Epub 2008 Dec ;3(11):e3636. Epub 2008 Nov 4.

13 Not yet published: Mary Carringtons lab Rasmi Thomas et al., in revision International HIV Controllers Study Paul de Bakker, manuscript in preparation HLA-C, including protein expression HCP5/B*5701 HLA-C ZNRD1 Independent replications of associations

14 The HLA-C –35 C allele associates with better control of HIV To help understand how, Frank Kirchhoff elegantly tested whether the HIV-1 accessory protein Nef can neutralize the C-related protective effect, by comparing –35 CC subjects with low vs. high viral loads Results : high VLs in subjects with the CC genotype do not associate with an increase in Nef-mediated downmodulation of HLA-C But they associate with enhanced potency in other Nef functions that impair antigen-dependent T cell activation Nef counteracts HLA-C mediated immune control of HIV-1

15 Anke Specht, Frank Kirchhoff et al., in preparation HIV-1 Nef functions possibly contributing to high viral loads in individuals that have a protective HLA-C -35 CC genotype

16 Importance of host genetics to a measure of disease progression ZNRD1/RNF39 ( Genome-wide significant determinant of progression) HCP5 ( Genome wide significant determinant of progression and viremia) HLA-C ( Genome wide significant determinant of viremia) CCR5 delta32 CCR2 V64I ( Widely accepted functional variants, not currently genome wide significant) Progression was defined on the basis of observed or predicted drop in CD4 counts to below 350 for individuals with and without protective alleles: - in blue the average time to CD4 drop is 2 years for individuals without any protective alleles - in red the average time is 8 years for subjects with 1 or 2 protective allele(s) in at least 4 of those variants Data from Fellay et al. Science 2007 & the Euro-CHAVI Consortium, part of the Center for HIV/AIDS Vaccine Immunology (CHAVI)

17 The impact of common variants After study of 500 subjects, three common variants explain 14% of the variation in viral load at set point And… After study of 2600 subjects, three common variants explain 14% of the variation in viral load at setpoint

18 Height Marc Gasol Pau Gasol

19 Height Heritability is >.8 The most important common variant, in HMGA2, explains one third of one percent of variation in height general population –Weedon et al 2007.

20 Height effect sizes and fitted exponential

21 How many SNPs to explain 80 percent of the variation in height ? 1. Effect size of SNP N = * ^N = N* * ^N/LN ( ) * /LN( ) 3. N=93,000

22 Where to next? Other racial/ethnic groups New cohorts (to assess acquisition) Screens for rare variants (structural and single site)

23 Malawi EU Study 500 positives/1000 negatives (exposure) –Will add another 250 positives Exposure criteria –Visited STD clinic –Older than 23 No genome-wide significant p-values for SNP association –Still evaluating results CNV analysis currently being run

24 Structural Variants WGA screen for structural variants –EuroCHAVI –MACS Deletions and duplications were inferred by using publically available intensity software (PennCNV) CNV region on chromosome 19 showed association with setpoint and progression Rare: 2.8% deletion Rare: 2.8% deletion 3.3% duplication 3.3% duplication

25 Viral load setpoint decreases with chr19 CNV state n=2 n=72 n=1977n=86 n=2 n=2 n=72 n=1977n=86 n=2

26 KIR: Killer Cell Immunoglobulin-like Receptor Methods in Molecular Biology, Martin & Carrington, Multiple known haplotypes with different combinations of KIR genes -Most common duplication -Most common deletion

27 p=0.5p=6E

28 Complete resequencing of individuals with extreme phenotypes

29 Extreme traits resequencing: proposed framework 1. WG resequencing of a few individuals with extreme phenotypes - likely to be enriched for rare causal variants 2. Selection of a subset of the identified variants (bioinformatics: genetic function, candidate genes…) 3. Genotyping of the best candidates in large populations

30 Hemophilia project Study design: case/control study up to 1000 patients intravenously exposed to HIV between up to 1000 patients intravenously exposed to HIV between HIV infected individuals already analyzed in other Host Genetics projects HIV infected individuals already analyzed in other Host Genetics projects Exposure: The high prevalence of CCR5 d32 homozygosity in exposed, yet uninfected haemophilia patients (known to be 15-25%) proves a very high rate of effective exposure to HIV in this population:

31 SequenceVariantAnalyzer, a dedicated software infrastructure to manage, annotate, and analyze the large number of very unique variants detected from a resequencing project.

32 32 Processed variant data including genomic coordinates (single site, small and large copy number changes) SVA GUI application RefSeq Ensembl core database Ensembl variation database RefSeq Ensembl core database Ensembl variation database HapMap & Illumina Variation sets External SIFT program KEGG pathway database Exon-level prediction of variant function Functional impact of NS SNPs on proteins Pathway filter Presence in existing databases In-house statistical module Binary output Fishers exact test Load test for association with phenotype Fishers exact test Load test for association with phenotype

33 The big question … Is whether the causal variants are recognizable

34 With thanks to NIH (CHAVI) – NIAID, DAIDS, OAR Bill & Melinda Gates Foundation

35 Dr. Jacques Fellay Dr. Kevin Shianna Dr. Dongliang Ge Dr. Woohyun Yoon Dr. TJ Urban Dr Anna Need Liz Cirulli Nicole Walley Curtis Gumbs Kiim Pelak Dr. Amalio Telenti Dr. Sara Colombo Dr. Bart Haynes Dr. Norm Letvin Dr. Andrew McMichael Dr. Lucy Dorrell Dr. Seph Borrow Dr. Mary Carrington Dr. Mary Carrington Dr. Nelson Michael Dr. Nelson Michael Dr. Amy Weintrob Dr. Amy Weintrob


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