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

InfectionInfection

ExposureExposure

Genetics ofGenetics ofviral controlviral control

Genetics of Genetics of resistanceresistance

Genetics of Genetics of vaccine vaccine trialstrials

Host Genetics portfolioHost Genetics portfolio

Telenti A & Goldstein DB, Nat Rev Microbiol 2006

PhenotypePhenotype

IrsiCaixaIrsiCaixaBarcelona, SpainBarcelona, Spain

B. Clotet B. Clotet

Clinics HospitalClinics HospitalBarcelona, Spain Barcelona, Spain

J.M. Gatell J.M. Gatell Swiss HIV CohortSwiss HIV Cohort

University Hospital, LausanneUniversity Hospital, LausanneSwitzerland (coordinating center) Switzerland (coordinating center)

A. TelentiA. TelentiP. FrancioliP. Francioli

Danish CohortDanish Cohort DenmarkDenmark N. Obel N. Obel

San Raffaele HospitalSan Raffaele HospitalMilan, ItalyMilan, ItalyA. CastagnaA. Castagna

Modena CohortModena CohortModena, ItalyModena, ItalyA. CossarizzaA. Cossarizza

I.CO.NA CohortI.CO.NA CohortRome, Italy Rome, Italy A. De Luca A. De Luca

Guy Kings St.Thomas HospitalGuy Kings St.Thomas Hospital United KingdomUnited Kingdom P. EasterbrookP. Easterbrook

Royal Perth HospitalRoyal Perth Hospital Perth, AustraliaPerth, Australia

S. MallalS. Mallal

the EuroCHAVI consortium

WGAViewer: gene context annotation (HLA-C, HLA-B, HCP5)

Showing all SNPs genotyped in this region sorted by p-value or functionality

http://www.genome.duke.edu/centers/pg2/downloads/wgaviewer.php

HLA-C, rs9264942

HLA-B*5701/HCP5, rs2395029

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

Gene & SNP

P-value for association with HIV-1 viral load

at setpointN=2362

P-value for association with protection against

progression (CD4 <350)N=1071

HCP5 / HLA-B*5701rs2395029 4.5E-35 1.2E-11

HLA-Crs9264942 5.9E-32 7.4E-12

ZNRD1 / RNF39 rs9261174 1.1E-04 3.8E-08

CCR5 Δ32 hetrs333 1.7E-10 2.6E-06

CHAVI set point study: global results

Bonferroni threshold for genome-wide significance: 5E-08

Independence of the Independence of the HCP5HCP5 and and HLA-CHLA-C association signalsassociation 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 separatelynonetheless, 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 Independence of the ZNRD1 ZNRD1 association signalassociation 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

√ 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

√ HCP5/B*5701√ HLA-C√ ZNRD1: in haplotypes that contain HLA-A10

√ HCP5/B*5701√ ZNRD1

2009 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 24.

2008;3(11):e3636. Epub 2008 Nov 4.

Not yet published:

Mary Carrington’s labRasmi 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 associationsIndependent replications of associations

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-CBut 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

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

Importance of host genetics to a measure of Importance of host genetics to a measure of disease progressiondisease 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 delta32CCR2 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)

0.5

11.

52

dist

ribut

ion

and

kern

el d

ensi

ty

100 10'0001'000

time to progression (number of days, logarithmic scale)

The impact of common variantsThe impact of common variants

After study of 500 subjects, three common After study of 500 subjects, three common variants explain 14% of the variation in variants explain 14% of the variation in viral load at set pointviral load at set pointAnd…And…After study of 2600 subjects, three After study of 2600 subjects, three common variants explain 14% of the common variants explain 14% of the variation in viral load at setpointvariation in viral load at setpoint

HeightHeightMarc Gasol Pau Gasol

HeightHeight

Heritability is > .8Heritability is > .8The most important common variant, in The most important common variant, in HMGA2, explains one third of one percent HMGA2, explains one third of one percent of variation in height general populationof variation in height general population

– Weedon et al 2007 .Weedon et al 2007 .

0.1

.2.3

Effe

ct o

f the

Nth

snp

0 5 10 15 20Rank of each height snp

observed points least square fitted exponential

Height effect sizes and fitted exponentialHeight effect sizes and fitted exponential

How many SNPs to explain 80 How many SNPs to explain 80 percent of the variation in heightpercent of the variation in height??

1.1. Effect size of SNP N Effect size of SNP N =0.0008242+0.3502509*0.8912553^N =0.0008242+0.3502509*0.8912553^N

2.2. 80 = 80 = N*0.0008242+0.3502509*.8912553^N/LN(.8912N*0.0008242+0.3502509*.8912553^N/LN(.8912553) - 553) - 0.0008242+0.3502509*.8912553/LN(.8912553) 0.0008242+0.3502509*.8912553/LN(.8912553)

3.3. N=93,000N=93,000

Where to next?Where to next?

Other racial/ethnic groupsOther racial/ethnic groupsNew cohorts (to assess acquisition)New cohorts (to assess acquisition)Screens for rare variants (structural and Screens for rare variants (structural and single site)single site)

Malawi EU StudyMalawi EU Study500 positives/1000 negatives (exposure)500 positives/1000 negatives (exposure)– Will add another 250 positivesWill add another 250 positivesExposure criteriaExposure criteria– Visited STD clinicVisited STD clinic– Older than 23Older than 23

No genome-wide significant p-values for SNP No genome-wide significant p-values for SNP associationassociation– Still evaluating resultsStill evaluating resultsCNV analysis currently being runCNV analysis currently being run

Structural VariantsStructural VariantsWGA screen for structural variantsWGA screen for structural variants– EuroCHAVIEuroCHAVI– MACSMACS

Deletions and duplications were inferred by using Deletions and duplications were inferred by using publically available intensity software (PennCNV)publically available intensity software (PennCNV)

CNV region on chromosome 19 showed association with CNV region on chromosome 19 showed association with setpoint and progressionsetpoint and progression Rare: 2.8% deletionRare: 2.8% deletion

3.3% duplication3.3% duplication

Viral load setpoint decreases with chr19 CNV stateViral load setpoint decreases with chr19 CNV state

4.66755

4.262294.07537

3.83744

3.10918

01

23

45

mea

n of

set

poin

t

0 1 2 3 4

Setpoint by CNV state

CNV

n=2n=2 n=72 n=72 n=1977 n=1977 n=86n=86 n=2 n=2

KIR: KIR: Killer Cell Immunoglobulin-like ReceptorKiller Cell Immunoglobulin-like Receptor

Methods in Molecular Biology, Martin & Carrington, 2008

-Multiple known haplotypes with different combinations of KIR genes

-Most common duplicationMost common duplication-Most common deletionMost common deletion

4.474.32 4.30

4.67

4.304.04

3.59

3.11

2

3

4

5

0 1 2 3 4 0 1 2 3 4

Bw6 only at least one HLA Bw4m

ean

of s

etpo

int

KIR copy number variants

p=0.5 p=6E-05

1616 401401 2121 3838 734734 464622 22

Complete resequencing of Complete resequencing of individuals with ‘extreme individuals with ‘extreme

phenotypes’phenotypes’

Extreme traits resequencing: Extreme traits resequencing: proposed frameworkproposed framework

1.1. WG resequencing of a few individuals with WG resequencing of a few individuals with extreme phenotypes - likely to be enriched extreme phenotypes - likely to be enriched for rare causal variantsfor rare causal variants

2.2. Selection of a subset of the identified Selection of a subset of the identified variants (bioinformatics: genetic function, variants (bioinformatics: genetic function, candidate genes…)candidate genes…)

3.3. Genotyping of the best candidates in large Genotyping of the best candidates in large populationspopulations

Hemophilia projectHemophilia projectStudy designStudy design: case/control study: case/control study up to 1000 patients intravenously exposed to HIV between 1979-1984up to 1000 patients intravenously exposed to HIV between 1979-1984 HIV infected individuals already analyzed in other Host Genetics projectsHIV infected individuals already analyzed in other Host Genetics projects

ExposureExposure: : The high prevalence of CCR5 d32 homozygosity in “exposed, The high prevalence of CCR5 d32 homozygosity in “exposed, yet uninfected” haemophilia patients (known to be 15-25%) proves a very yet uninfected” haemophilia patients (known to be 15-25%) proves a very high rate of effective exposure to HIV in this population: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.

3232

Processed variant data including genomic coordinates (single site, small and large copy number changes)

SVA GUI application

RefSeqEnsembl 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 filterPresence in

existing databases

In-house statistical module

Binary output

Fisher’s exact test“Load ” test

for association with phenotype

The big question …

• Is whether the causal variants are ‘recognizable’

With thanks to

• NIH (CHAVI)– NIAID, DAIDS, OAR

• Bill & Melinda Gates Foundation

Dr. Jacques FellayDr. Jacques FellayDr. Kevin ShiannaDr. Kevin Shianna Dr. Dongliang GeDr. Dongliang GeDr. Woohyun YoonDr. Woohyun YoonDr. TJ UrbanDr. TJ UrbanDr Anna NeedDr Anna NeedLiz CirulliLiz CirulliNicole WalleyNicole WalleyCurtis GumbsCurtis GumbsKiim PelakKiim Pelak

Dr. Amalio TelentiDr. Amalio TelentiDr. Sara ColomboDr. Sara ColomboDr. Bart HaynesDr. Bart HaynesDr. Norm LetvinDr. Norm LetvinDr. Andrew McMichaelDr. Andrew McMichaelDr. Lucy DorrellDr. Lucy DorrellDr. Seph BorrowDr. Seph Borrow

Dr. Mary CarringtonDr. Mary Carrington Dr. Nelson MichaelDr. Nelson Michael Dr. Amy WeintrobDr. Amy Weintrob