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Determinants of host response to HIV-1: the role of rare and common variants
Host Genetics portfolio Genetics of vaccine trials Genetics of viral control Genetics of resistance Exposure Infection
Phenotype Telenti A & Goldstein DB, Nat Rev Microbiol 2006
Danish Cohort Denmark N. Obel Royal Perth Hospital Perth, Australia S. Mallal Guy Kings St.Thomas Hospital United Kingdom P. Easterbrook Swiss HIV Cohort University Hospital, Lausanne Switzerland (coordinating center) A. Telenti P. Francioli San Raffaele Hospital Milan, Italy A. Castagna Modena Cohort Modena, Italy A. Cossarizza I.CO.NA Cohort Rome, Italy A. De Luca the EuroCHAVI consortium Clinics Hospital Barcelona, Spain J.M. Gatell IrsiCaixa Barcelona, Spain B. Clotet
WGAViewer: gene context annotation (HLA-C, HLA-B, HCP5) HLA-B*5701/HCP5, rs2395029 HLA-C, rs9264942 Showing all SNPs genotyped in this region sorted by p-value or functionality http://www.genome.duke.edu/centers/pg2/downloads/wgaviewer.php
WGAViewer: SNP annotation (HLA-C, rs9264942) Showing all HapMap SNPs not genotyped in this region sorted by r2 or functionality http://www.genome.duke.edu/centers/pg2/downloads/wgaviewer.php
CHAVI set point study: global results Bonferroni threshold for genome-wide significance: 5E-08
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: • rs2395029: p=1.8E-23 • rs9264942: p=2.4E-20
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)
Independent replications of associations 2008;3(12):e3907. Epub 2008 Dec 24. √ HCP5/B*5701 √ HLA-C: rs9264942 was not genotyped, but is in LD with the top hit, rs10484554, which also associates with HLA-C expression 2008 Dec 30. [Epub ahead of print] √ HCP5/B*5701 √ ZNRD1 √HCP5/B*5701 √ HLA-C √ ZNRD1: in haplotypes that contain HLA-A10 2008;3(11):e3636. Epub 2008 Nov 4. 2009 Jan 2;23(1):19-28 √HCP5/B*5701 √ HLA-C
Independent replications of associations Not yet published: Mary Carrington’s 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
Nef counteracts HLA-C mediated immune control of HIV-1 • 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
HIV-1 Nef functions possibly contributing to high viral loads in individuals that have a ‘protective’ HLA-C -35 CC genotype Anke Specht, Frank Kirchhoff et al., in preparation
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)
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
Height Pau Gasol Marc Gasol
Height • Heritability is > .8 • The most important common variant, in HMGA2, explains one third of one percent of variation in height general population • Weedonet al 2007 .
How many SNPs to explain 80 percent of the variation in height? • Effect size of SNP N =0.0008242+0.3502509*0.8912553^N • 80 = N*0.0008242+0.3502509*.8912553^N/LN(.8912553) - 0.0008242+0.3502509*.8912553/LN(.8912553) • N=93,000
Where to next? • Other racial/ethnic groups • New cohorts (to assess acquisition) • Screens for rare variants (structural and single site)
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
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 3.3% duplication
Viral load setpoint decreases with chr19 CNV state n=2 n=72 n=1977 n=86 n=2
KIR: Killer Cell Immunoglobulin-like Receptor Methods in Molecular Biology, Martin & Carrington, 2008 • Multiple known haplotypes with different combinations of KIR genes • Most common duplication • Most common deletion
p=0.5 p=6E-05 16 401 21 2 38 734 46 2
Extreme traits resequencing: proposed framework • WG resequencing of a few individuals with extreme phenotypes - likely to be enriched for rare causal variants • Selection of a subset of the identified variants (bioinformatics: genetic function, candidate genes…) • Genotyping of the best candidates in large populations
Hemophilia project • Study design: case/control study • up to 1000 patients intravenously exposed to HIV between 1979-1984 • 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:
SequenceVariantAnalyzer, a dedicated software infrastructure to manage, annotate, and analyze the large number of very unique variants detected from a resequencing project.
How does it work? Processed variant data including genomic coordinates (single site, small and large copy number changes) SVA GUI application In-house statistical module External SIFT program RefSeq Ensembl core database Ensembl variation database HapMap & Illumina Variation sets KEGG pathway database Exon-level prediction of variant function Presence in existing databases Pathway filter Functional impact of NS SNPs on proteins Fisher’s exact test “Load ” test for association with phenotype Binary output
The big question … • Is whether the causal variants are ‘recognizable’
With thanks to • NIH (CHAVI) • NIAID, DAIDS, OAR • Bill & Melinda Gates Foundation
Dr. Jacques Fellay Dr. Kevin Shianna Dr. DongliangGe Dr. Woohyun Yoon Dr. TJ Urban Dr Anna Need Liz Cirulli Nicole Walley Curtis Gumbs KiimPelak Dr. AmalioTelenti Dr. Sara Colombo Dr. Bart Haynes Dr. Norm Letvin Dr. Andrew McMichael Dr. Lucy Dorrell Dr. Seph Borrow Dr. Mary Carrington Dr. Nelson Michael Dr. Amy Weintrob