Ioannidis, John P. A.

Telenti, Amalio

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4 • JID 2006:193 (1 January) • EDITORIAL COMMENTARYE D I T O R I A L C O M M E N T A R YSusceptibility to HIV Infection—Disentangling Host Geneticsand Host BehaviorAmalio Telenti1 and John P. A. Ioannidis21Institute of Microbiology and University Hospital, University of Lausanne, Lausanne, Switzerland; 2Department of Hygiene and Epidemiology, University of IoanninaSchool of Medicine, Ioannina, Greece(See the article by Shrestha et al., on pages 16–26.)Received 20 July 2005; accepted 20 July 2005; electronicallypublished 22 November 2005.Financial support: Swiss National Science Foundation (toA.T.); General Secretariat for Research and Technology, Greece(to J.P.A.I.).Potential conflicts of interest: none reported.Reprints or correspondence: Dr. Amalio Telenti, Institute ofMicrobiology, Centre Hospitalier Universitaire Vaudois, 1011Lausanne, Switzerland (amalio.telenti@chuv.ch); or Dr. John P.A. Ioannidis (jioannid@cc.uoi.gr).The Journal of Infectious Diseases 2006;193:4–6 2005 by the Infectious Diseases Society of America. Allrights reserved.0022-1899/2006/19301-0002$15.00In 1874, Francis Galton, a cousin ofCharles Darwin, coined the idiom “natureand nurture” to cover all of the influencesthat determine an adult’s constitution—the sum total of their particular physicaland behavioral characteristics. In this is-sue of the Journal, Shrestha et al. [1] ap-ply state-of-the-art genetics along withexplicit modeling of an “environmental”component (high risk exposure) to dis-entangle the nature and nurture of anindividual’s susceptibility to HIV-1. Thestudy is an example of the rapidly evolvingfield of host genetics in infectious diseases.Nature offers 2 experiments on HIV-1susceptibility: the uniqueness of long-termnonprogressors and the highly exposeduninfected individual. Both represent sit-uations that hide a large amount of het-erogeneity in mechanisms and biology.However, the condition of being “exposeduninfected” appears to be particularly in-tractable, because it grafts a multifactorialtrait onto the poor efficiency of transmis-sion of HIV-1 and the rarity of cohorts ofindividuals that are characterized as high-ly exposed and persistently seronegative.Particular studies have examined hetero-sexual couples with discordant HIV sero-status, highly exposed sex workers, andhighly exposed men having sex with men.The mechanisms identified or invoked tomodulate susceptibility to infection in thevarious reports emphasize the relevance ofdifferences in acquired immunity, throughthe role of protective cytotoxic T lympho-cyte responses and NK cell activity in thecontext of specific HLA class I alleles (re-viewed in [2]), as well as differences inhumoral responses at mucosal surfaces [3].The other area of research has includedassessment of expression of chemokinereceptors and their ligands, as well as therole of genetic variants of those molecules(reviewed in [2]). In addition, CD4 Tcells from healthy blood donors differmarkedly in their susceptibility to HIV-1 infection [4], and a relative resistanceto HIV-1 infection of CD4 T cells fromexposed uninfected individuals has beendescribed [5].The study by Shrestha et al. [1] aimedat freeing itself from the constraint of thelow numbers of the above unique pop-ulations—and, thus, the constraint oflimited statistical power—by using astandardized cumulative risk exposuremeasurement on a larger number of in-dividuals ( ). They applied thisnp 789approach to the evaluation of genetic var-iants of 9 genes involved in HIV-1 entryand replication, with a focus on the bi-ology of CCR5-mediated pathways. Notincluded was CCL3L1, coding for theCCR5 ligand macrophage inflammatoryprotein 1 (MIP1)–a-P and present in thehuman genome at variable copy num-bers, which define different levels of riskfor infection [6].Most genetic epidemiology studies todate have been unable to deal efficientlywith environmental factors [7]. Althoughtaking genetic “measurements” (i.e., ge-notyping) is straightforward, measuringenvironmental exposures can be difficultand imprecise. Usually, we have to rely ontraditional epidemiological methods (e.g.,questionnaires), with all of their short-comings. Shrestha et al. use an innova-tive approach to incorporate a multivar-iate propensity score for cumulative highrisk exposure. Propensity methods wereoriginally developed to estimate which pa-rameters are associated with the “risk” ofreceiving a specific treatment in nonran-domized databases [8]. Patients may havehad different reasons for being given oneor another treatment, and propensity mod-els offered a way to balance the compar-ison of these treatments for such uneventreatment preferences. In the genetic set-EDITORIAL COMMENTARY • JID 2006:193 (1 January) • 5ting, genotypes are the equivalent of“treatments.” Typically, we rely on Men-delian randomization [9] and assumethat high risk exposure and confoundingfactors would also be randomly assignedto different genotypes. This means thatwe accept that exposure would be simi-lar regardless of genotype. One could goeven a step further and examine whetherthere is any interaction between risk ex-posure and genetic profile. However, dem-onstration of interaction effects would re-quire even larger sample sizes.There are more challenges to be over-come in modeling environmental factors.Multivariate models have shortcomings,and predictive models also need valida-tion, both internally (through bootstrap-ping, for example) and externally (in dif-ferent populations). There is increasingevidence of the limitations of appraisingprognostic factors [10]. Moreover, the dis-criminatory ability of predictive modelsmay still be limited, even if several risksare considered. For example, even in Shres-tha et al.’s state-of-the-art study, the high-risk quintile, compared with the low-riskquintile, had only a 2-fold difference in risk(49% vs. 24%).Even if we ignore the complexity con-ferred by the interaction of the host ge-netic background with risk behavior, thesample size required to detect modest ge-netic effects (i.e., odds ratios in the 1.1–1.8 range) can be quite large. Most genet-ic epidemiology studies conducted in thepast have been underpowered [11]. An ad-vantage of the study by Shrestha et al. isthat, given its sample size, modest geneticeffects for relatively common alleles wouldnot be missed. However, even with 789subjects, there is not enough power to pur-sue polymorphisms with allele frequen-cies !10%. It is unknown how much suchuncommon polymorphisms contribute tothe genetic background of HIV-1 suscep-tibility. For example, the strongest pro-tective factor known to date, homozy-gosity for the CCR5-D32 allele, occurs inonly 1% of white individuals and is prac-tically absent in people of African descent.Multiple testing can also lead to spu-rious findings that are not validated bysubsequent studies [12]. Of the 50 poly-morphisms tested by Shrestha et al., 2 or3 would be expected to show signifi-cant associations ( ) just by chance.P ! .05With the advent of discovery-orientedapproaches [13], it is possible to screenhundreds of thousands of genetic vari-ants across relatively large data sets. Atfirst pass, a typical whole-genome asso-ciation study may yield a few thousand“promising” polymorphisms for any com-plex trait. Evidently, all of these tentativeassociations need further validation inindependent data sets. Thus, proposedgenetic associations should be consideredto be works in progress [14]. Haplotypeanalyses, such as those utilized by Shres-tha et al., are also becoming the rule. TheHapMap project [15] aims to describemost existing genetic variability by typingonly a limited number of polymorphismsper gene. However, haplotypes may notalways adequately represent the full var-iability in diverse populations, and com-paring selected haplotypes rather than allpossible haplotypes without clear justi-fication is a dangerous practice.Another major threat to the validity ofgenetic epidemiology studies is the selec-tive publication and selective reporting ofthe analyses being performed. Shrestha etal. provide an excellent example of howall tested polymorphism associations shouldbe reported, regardless of whether theylead to significant results. The availabilityof electronic online supplements shouldobviate problems related to the mass ofcurrently assembled databases, even forthe most data-rich analyses. In publica-tions that present the discovery of a fewformally statistically significant associa-tions, it is unknown whether the inves-tigators tested many others but are re-porting only the most significant ones.Such selective reporting may lead to adistorted literature, and replication of theclaimed findings would be uncommon[12]. Formal statistical significance shouldnot be used as a criterion for disseminationof research in genetic epidemiology.Finally, we still need more empirical evi-dence on how to link epidemiological evi-dence with data on the biological andfunctional significance of postulated gene-disease associations [16]. Of the 4 candi-date protective polymorphisms identifiedby Shrestha et al., one is in a noncodingregion, another is synonymous (no aminoacid change), and a third is at odds withprevious reports. Only 1 polymorphism,CCR2 64I, has a biological rationale tosupport its role [17] and concurrent evi-dence for protection against disease pro-gression [18]. Nevertheless, this polymor-phism has not been associated with riskof HIV-1 transmission via the vertical route[19]. Squaring epidemiology with biologycan be difficult. However, we should keepan open mind, since there is much we donot know about the intricacies of the hu-man genome and its function. For example,polymorphisms in noncoding regions mayhave important indirect biological effects.Overall, the extensive work of Shresthaet al. yielded a modest output—4 “hits”among 50 genetic variants in 9 genes. Isthis a satisfactory outcome for a large andcostly experiment? The answer is a sober“yes.” Sooner or later, all genes linked tothe pathogenesis of a given disease will begenotyped, and all putative genetic mark-ers (associated with a study phenotype)will be followed by using the appropriategenetic tools to identify linked functionalvariants, by establishing the appropriatebiological experiments, and by validationacross several populations and cohorts.For susceptibility to HIV-1 infection, thelist of candidates is long, because limitedwork has been done on variants of genesthat are needed for viral replication in thecell [20, 21]. The list is longer if we movefrom acquired immunity to the innateimmunity or to genes coding for cellu-lar antiretroviral defense mechanisms.Species-specific single–amino acid dif-ferences in apolipoprotein B mRNA-ed-iting enzyme, catalytic polypeptide-like3G (APOBEC3G) [22, 23], associated6 • JID 2006:193 (1 January) • EDITORIAL COMMENTARYwith hypermutation of the viral DNA,and in tripartite motif–containing pro-tein 5 (TRIM5)–a [24, 25], an antiret-roviral protein acting after HIV-1 infectsthe cell, are enough to provide adequateprotection against HIV-1 in some pri-mates, whereas the human versions areineffective. Characterization of variantsof these genes in humans has just started[26, 27].Against a background of “hype” andexcitement around all things genomic,there is one more lesson to learn frommost studies trying to make sense of thecomplex biology behind interindividualdifferences. It is true that some “big” dis-coveries and breakthroughs will happen.For example, the identification of theCCR5-D32 mutation not only resulted ina paradigm shift in our understanding ofpathogenesis, but it also helped in thedevelopment of a new class of antiret-roviral agents. However, much work ingenetics and genomics is unavoidablyantlike, going through scores of data to,piece by piece, reconstruct the pathogen-host interaction—this time, starting fromthe host side. It is unlikely that the iden-tification of multigene effects will changethe management of HIV-1 disease in thenear future. It would even be erroneousto suggest that genetic tests that, in iso-lation, confer small quanta of informa-tion should be integrated into patientcare. One has to verify first that they areindeed true and reproducible, possible tostandardize for clinical use, amenable touse by information-overwhelmed clini-cians, readily interpretable, able to im-prove outcomes, and cost-effective. Noneof the validated or postulated geneticmarkers of HIV-1 disease susceptibilityor progression meets all of these criteriafor clinical use.How should infectious-diseases spe-cialists react to the growing number ofstudies on host genetics? Over the past 20years, they have been asked to learn a greatdeal of microbiology, then basic immu-nology, molecular biology, and biostatis-tics. Now they will need to learn geneticsand genetic nomenclature and to build ge-netic cohorts [28] and collaborations be-tween cohorts working on the same field[29]. Large challenges lie ahead.References1. Shrestha S, Strathdee SA, Galai N, et al. Be-havioral risk exposure and host genetics ofsusceptibility to HIV-1 infection. J Infect Dis2006; 193:16–26 (in this issue).2. Kaslow RA, Dorak T, Tang JJ. Influence of hostgenetic variation on susceptibility to HIV type1 infection. J Infect Dis 2005; 191(Suppl 1):S68–77.3. Mazzoli S, Trabattoni D, Lo CS, et al. 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