candidate genes and single nucleotide polymorphisms (snps...

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Candidate genes and single nucleotidepolymorphisms (SNPs) in the study of humandisease

Stephen Chanock∗National Cancer Institute, Gaithersburg, MD 20877,USA

The genomic revolution has generated an extraordinary re-source, the catalog of variation within the human genome,for investigating biological, evolutionary and medical ques-tions. Together with new, more efficient platforms for high-throughput genotyping, it is possible to begin to dissect ge-netic contributions to complex trait diseases, specifically ex-amining common variants, such as the single nucleotide poly-morphism (SNP). At the same time, these tools will make itpossible to identify determinants of disease with the expec-tation of eventually, tailoring therapies based upon specificprofiles. However, a number of methodological, practicaland ethical issues must be addressed before the analysis ofgenetic variation becomes a standard of clinical medicine.The currents of variation in human biology are reviewed here,with a specific emphasis on future challenges and directions.

Keywords: Variation, genome, genetic, mutation, disease sus-ceptibility

1. Introduction

With the completion of the first composite map of thehuman genome, an extraordinary resource has becomeavailable to investigate the role of genetic variation inhuman diseases [1,2]. While the total number of genesis roughly 35,000, less than the initial estimates, it isnonetheless, a formidable task to organize and catalogthe differences between any two genomes. The conceptof a single human genome was useful for constructing

∗Address for correspondence: Steven Chanock, M.D., Immuno-compromised Host Section, Pediatric Oncology Branch and The Ad-vanced Technology Center, National Cancer Institute, 8717 Grove-mont Circle, Gaithersburg, MD 20877, USA. Tel.: +1 301 435 7559;Fax: +1 301 402 3134; E-mail:[email protected].

a first generation map, but now, it is clear that there isno such entity. In fact, any two human genomes areestimated to differ by approximately 0.1% or less. Re-markably, it is within this tiny fraction of a genome,namely the collection of sequence variations, that wefind an opportunity to decipher genetic determinantsof disease susceptibility and outcome. Furthermore,the catalog of variations represents an unprecedentedresource for investigating evolutionary and migratoryevents in human history. The combination of technicaladvances in genetic analyses coupled with the enor-mous resource of genetic information has set the stagefor a new age in the study of human disease.

2. Variation in the human genome: Thepredominance of SNPs

The most common sequence variation in the humangenome is the substitution of a single base, commonlyreferred to as a single nucleotide polymorphism (SNP).By definition, a SNP is a variation in sequence with afrequency of greater than 1% in at least one population.It has been estimated that the number of SNPs in an in-dividual numbers in the millions, reflecting enormoussequence diversity across all human chromosomes [3,4]. Initial estimates of the density of SNPs across thegenome suggest that the average frequency of SNPs isbetween 1 in 1.3 and 1.9 kb overall [4–6]. It is no-table that the density of SNPs varies between regionsof chromosomes, as well as between chromosomes [7,8]. These differences reflect a spectrum of selectivepressures on genes as well as complex rates of mutationand recombination, which, also vary greatly across thegenome. Less frequent variants (i.e., with less than 1%frequency) might also be informative in the mappingof complex-trait disorders as well as familial diseases.By definition, a mutation results in a significant pheno-type, whereas a SNP possesses mild or no phenotypicchanges. It is more than likely than some rarer variants

Disease Markers 17 (2001) 89–98ISSN 0278-0240 / $8.00 2001, IOS Press. All rights reserved

90 S. Chanock / Candidate genes and single nucleotide polymorphisms (SNPs) in the study of human disease

act more like SNPs, conferring a mild phenotypic dif-ference in rarer disorders. The challenge lies in iden-tifying infrequent variants from public databases andapplying them to directed studies of rarer diseases (i.e.childhood cancer). The study of SNPs, which are, bydefinition, stable genetic changes in the genome, per-mits us to look closely at the footprints of past gen-erations. Thus, the study of SNPs within families orpathways of genes also offers a perspective on priorevents in human evolution, ones that have shaped thediversity we appreciate today [9].

Several factors in combination have shaped the di-versity we now observe as SNPs, fixed variations ingene sequence. One of the major contributing factorsis selective pressure in response to challenges withindiscrete populations. Random mutation at each base inthe 3.1 billion base pair genome is not equally toler-ated; in the extreme, some changes are not compatiblewith survival (i.e., pre-terminal stop codons in criticalgenes). Since the rate of random mutational rate isroughly 2×10−8, it is expected that each base has beenmutated many times in the history of human evolution.So far, initial surveys indicate that transitional changesare more common than transversions, suggesting thatdeamination contributes to a higher likelihood of mu-tation, especially at sites of CpG dinucleotides [10].Based on these assumptions and the data emanatingfrom the SNP Consortium, it has been posited that thereare as many 11 million SNPs per person [3].

Historically, the first single nucleotide polymor-phisms were identified during ‘gene-centric’ studies,detected by restriction fragment length polymorphism(RFLP) and used to analyze single genes in population-based studies. The utility of the RFLP was replacedby the use of the simple tandem repeat (STR) as thepreferred unit for genetic studies. STRs are highlypolymorphic allelic repeats of 2, 3 and 4 nucleotideunits strewn evenly through-out the genome, but withsubstantially lesser frequency than SNPs [11]. Rarervariations, namely large scale deletions, substitutionsor duplications arise with lower frequency and are use-ful for study of informative genes [12]. In some cases,a SNP is in linkage with a more complex, stable vari-ant. Interestingly, some of the strongest associationsidentified so far have been between complex variantsand disease outcomes, and not simple SNPs, thoughmany use the term SNP to indicate more than a singlenucleotide variation.

Adequately spaced STRs have been be used to scanthe whole genome for markers in linkage disequilib-rium with a trait. Using PCR amplification technology,

it is possible to amplify a collection of STRs distributedacross the genome and map monogenic disorders to aunique locus using linkage analysis in family pedigrees.The success of this approach has been employed in thesearch for rare, familial mutations with high penetrance(in other words, when the phenotypic expression of therare mutation is quite significant).

For more complex disorders, in which multiple geneseach contribute a small effect, the utility of the wholegenome scan approach has been more problematic forseveral reasons. The ability to discriminate the ef-fect of single genes is difficult in complex disorders,which, by definition, are characterized by interactionsbetween multiple genes. So far, inadequate coverage ofthe genome with mapped SNPs together with technicalchallenges have undermined the effectiveness of thisapproach. Previously, a whole genome scan identifiedone or more genetic markers within a specific regionof a chromosome, but does not pinpoint the specificgene nor the significant change(s) in a gene’s sequence,which ultimately alters the phenotype.

Recently, many investigators have turned back toSNPs, planning to capitalize on several important fea-tures. One, SNPs are more abundant than STRs. Sec-ond, they are more stable compared to short nucleotiderepeats. The high degree of variation in STRs repre-sents a formidable barrier to population-based studies,as is frequently employed in candidate genetic asso-ciation studies. Lastly, a subset of SNPs (see below)effect the biological properties or expression of a geneproduct, thus providing a functional component thatcan neatly tie in with in vitro studies. In the future,the catolog of ‘functional’ SNPs will be useful in map-ping complex trait diseases, while at the same time,providing insights into the mechanisms of disease ortreatment outcomes.

3. A book of past lives: The catalog of SNPs

It is safe to say that we are still in the initial phaseof discovery with respect to SNPs. There are a num-ber of web-based tools designed to interrogate publicdatabases in search of SNPs (see Table 1). The NationalCenter for Biotechnology Information is curating apublic website, db-SNP, for deposition of SNPs and tag-ging the SNPs to other webtools useful for investigat-ing genetic information in silico [13]. Already, nearly1.4 million possible SNPs have been deposited in theSNP Consortium Database for public use with more ex-pected in the months to come (http://snp.cshl.org/) [4].

S. Chanock / Candidate genes and single nucleotide polymorphisms (SNPs) in the study of human disease 91

Table 1Selected public resources for SNP analysis

Public databases of SNPsPubMed Published Literature (search by gene)(www. ncbi.nlm.nih.gov/entrez/query.fcgi) – Resource for searching published datadB SNP NCBI Database of deposited SNPs(www.ncbi.nlm.nih.gov/SNP/) – Central repository of SNPsThe SNP Consortium Database of predicted SNPs(snp.cshl.org/) – Deposited in db-SNP

Web-based tools for identifying SNPsCancer Genome Anatomy Project- GAI NCI-based SNP Discovery Project(lpg.nci.nih.gov/GAI/) – Gene lists, tools for SNP analysis including predicting

non-synonymous SNPsSNP pipeline CGAP-GAI search of EST/Unigene(lpgws.nci.nih.gov:82/perl/snp/snp cgi.p) – Search EST sequences for SNPsLeelab SNP Database UCLA search of EST/Unigene(www.bioinformatics.ucla.edu/snp/) – Search EST sequences for SNPs

Databases of SNPs with annotationHG Base International Database(Hgbase.cgr.ki.se/) – Repository for SNPImunology -SNP database Curated collection of immunologically significant SNPs(www-dcs.nci.nih.gov/pedonc/ISNP/) – SNP database of known genes with SNPsUniversity of Utah Genome Center GeneSNPs Curated collection of SNPs derived from public databases(http://www.genome.utah.edu/genesnps old/)

The importance of validating SNPs can not be over-emphasized. Sophisticated web-tools have been devel-oped to search public databases of expressed sequencetags (ESTs) in search of putative SNPs [14,15]. Re-cently, it has been suggested that roughly three-fourthsof the SNPs in two large public databases can be val-idated at a high frequency (> 20% for the minor al-lele) [14–16]. Others have reported that using in silicotools yields relatively high specificity, but low sensi-tivity in SNP validation [17]. Still, candidate SNPsneed to be validated in genomic assays that amplify thespecific locus of interest from a unique chromosomallocation, determine that the variant exists by one ofseveral technologies (see below) and demonstrate itsMendelian inheritance. So far, this approach has beenbiased towards SNPs that arise in unique regions of thegenome. On the other hand, using PCR technology, it isdifficult to amplify regions replete with a high densityof redundancies, where informative SNPs (or STRs)could be still positioned.

While most SNPs are neutral and do not alter thephenotype, there is a subset of SNPs, which are pre-dicted to change the phenotype of the gene. These arethe most interesting targets for investigating the con-tribution of SNPs to disease. Traditionally, SNPs inthe coding region have been of particular interest be-cause an amino acid substitution can alter the functionof a protein. These are known as non-synonymouscoding SNPs and are less common than synonymousSNPs in the coding region (i.e., those that do not re-

sult in the substitution of an amino acid). Early stud-ies have confirmed that non-synonymous coding SNPsare rarer, and thus, support the hypothesis that greaterselective pressure is required to effect a functionallysignificant amino acid substitution [5,6]. The same canbe argued on behalf of variants critical for the regu-lation of a gene’s expression. In the rush to catalogSNPs, many groups have concentrated on identifyingnon-synonymous SNPs and in some cases, develop-ing sophisticated web-tools to browse public databasesto display in silico translation predictions of validatedSNPs (http://lpgws.nci.nih.gov:82/perl/snp2ref). Onthe other hand, it is harder to identify SNPs that alterthe regulation of a gene, namely its expression. Thisclass of SNPs are particularly important in complexpathways, such as an immunological cytokine networkor the coagulation cascade, where small differences inexpression can have pleotropic effects downstream, ineither amplification or dampening of a response path-way [18]. Since the field of promoter analysis and generegulation is highly specific to unique sequence motifs,it is unlikely that a comparable search program willfacilitate identification of informative, promoter SNPs.

As mentioned above, there is great interest in thecataloging of “functionally” important SNPs, namely,those SNPs that might directly alter the phenotypic ex-pression of the gene. The field is beginning to addressthe importance of correlating phenotype with SNPs,specifically within pathways or collections of genesthat fit a biological paradigm. Based upon the num-


ber of genes in the human genome (roughly 35,000),a SNP density of 1 in 1.8 kb and the average size ofthe coding region, 5’ and 3’ UTRs (1.34 kb, 300 bpand 770 bp, respectively), it is possible to estimatethat there are perhaps as few as 55,000 or as manyas 250,000 functionally interesting SNPs. The chal-lenge is to find these SNPs and apply them in well-designed molecular epidemiology studies. A public ef-fort at the National Cancer Institute, the Genetic An-notation Initiative of Cancer Genome Anatomy Projectis re-sequencing the entire coding region and 5’/3’ un-translated regions of candidate genes of importance tocancer biology (http://lpg.nci.nih.gov/GAI/). This di-rected approach has been employed by other groupsin an effort to validate common SNPs in genes of im-mediate importance to one or more common complexdisorders (i.e., diabetes, neurodegenerative diseases orcancer) [5,6]. Taking the next step, several web-toolshave been developed to curate SNPs within biologi-cal pathways; one such example is the first genera-tion of the Immunology-SNP database (http://www-dcs.nci.nih.gov/pedonc/ISNP/), which catalogs poly-morphisms, primarily SNPs in genes of immunologi-cal importance. Comparable efforts are underway todevelop virtual links between in vitro biological ob-servations, SNPs and genetic association studies. Inparallel, an international web-based effort is underwayto annotate genes, linking them with function in spe-cific pathways and families; this is known as the GeneOntology Project (http://www.geneontology.org) [19].There is no doubt that this resource will be invaluablein the search to map SNPs, both as genetic markersand in informative cases, in which SNPs are defined asmodifiers of disease outcome.

The SNP Consortium has deliberately sought to val-idate over 100,000 SNPs throughout the genome, with-out a bias towards coding or regulatory SNPs [4]. Thiscollection will be invaluable for large scale linkagestudies, and in selected circumstances, genetic associ-ation studies. A formidable number of SNPs, at leastseveral hundred thousand are required to adequatelyprovide a SNP density of one every 3 kb; this is basedupon inferred linkage disequilibrium calculated for thewhole genome [20–22]. As mentioned above, linkagedisequilibrium may not be evenly present throughoutthe genome. Consequently, predictions for the numberof SNPs required to conduct whole genome scans inpopulation based studies have been decreased, but notby an order of magnitude. Another, more directed ap-proach is to annotate SNPs in the roughly 35,000 knowngenes. This approach, known as an ‘intelligent SNP’

scan, favors identifying functionally significant SNPs.However, this bias, as attractive as it is to interrogategenes, does not provide sufficient coverage of inter-genic regions for linkage analysis, which still could in-fluence expression or less likely, function. Nonethe-less, in the future, one could imagine a more refinedform of an ‘intelligent scan’ – one that uses validated‘functional’ SNPs to conduct scans of the genome inwell-defined population-based studies. Or for that mat-ter, one can envision studies that analyze SNPs derivedfrom biologically critical pathways only.

4. Applying SNPs to the study of disease: Thechanging landscape

There is ample evidence to suggest that in commoncomplex trait diseases, genetic factors contribute to thedisease process but, it is most likely that multiple genescontribute to disease susceptibility. Although the effectof any single variant is probably small, combinationsof SNPs, either as haplotypes or between distant genes,may coordinately contribute to disease risk. Still, thenet effect of each SNP is small, which making it es-pecially difficult to identify key contributors. Therehave been a few examples in which linkage studieshave localized a variant, later shown to confer risk (e.g.,APOE and Alzeihmers) [23,24]. Still, the utility of thewhole genome scans is limited by the low-penetranceof many common variants. Genetic association stud-ies have emerged as the primary method of studyingthe effect of SNPs on disease outcomes, whether it issusceptibility to a disease or differences in phenotypicexpression of a known gene.

A genetic association study is designed to evalu-ate the contribution of one or more SNPs to well de-fined, clinical endpoints. Success is predicated on ac-curate determination of clinical outcomes in a well-characterized population based case control or cohortstudy. In either a cohort or case control study, statis-tical significance is determined by comparison of thedistribution of genotypes in two groups, with one serv-ing as a “control” and evaluates the effect of a varianton a clinical endpoint. Usually, these are conducted inpopulation-based studies, consisting of unrelated sub-jects. Family-based analysis can be useful, too, espe-cially in transmission disequilibrium studies.

Until recently, genetic association studies have beenlimited to studies involving one or a few candidategenes using classical epidemiological tools designedto evaluate the effect of a gene and its variant(s) in a


population-based case control or cohort study. Typi-cally, an interesting variant was discovered in a geneand studied in a population-based study, if a plausiblemechanism could be advanced. The emerging field ofmolecular epidemiology has been driven by the can-didate gene approach, namely, designing studies withknown variants in which prior biology or associationstudies justified study. The landscape has changed dra-matically with the explosion of knowledge. The currentproblem lies in extracting useful and informative SNPsfrom the public databases, which include millions ofputative SNPs. In this regard, the gap between under-standing the biological significance of a variant and theidentification of a variant is widening at an acceleratedpace. It is the annotation and possible functional sig-nificance that highlights the importance of looking atcandidate SNPs that will be of immediate interest toinvestigators conducting population-based studies.

Most experts would agree that until the cost and ef-ficiency of high-throughput platforms for SNP analy-sis are markedly improved, the candidate gene(s) ap-proach will be preferred. Already, new technologieshave moved the field from the interrogation of singleSNPs to as many as several dozen SNPs in a well-defined population study. The choice of SNPs for a newstudy should take into consideration several importantfactors. One is the frequency of the SNP in the popula-tion of study. There is great diversity in the frequencyof common SNPs between different populations. Sec-ond, a plausible mechanism should underlie the choiceof genes or families of genes. In other words, a soundbiological basis is important in designing population-based genetic association studies.

The literature is filled with small pilot studies thatare not confirmed by subsequent, better designed stud-ies. Two major reasons account for this recurrent prob-lem, small cohorts and poorly tracked collection of datapoints, often retrospective in execution. Pilot studiesare useful to identify the likely candidate genes to bevalidated in larger, more focused studies. It can beargued that false positives in pilot studies are greatlypreferred to false negatives, mainly because subsequentstudies will determine the validity of a finding, as op-posed to missing a potentially informative SNP. A se-ries of stringent studies is required before the resultsof clinical association studies can be applied to clinicaldecision making [25].

It is also notable that common polymorphisms, es-pecially SNPs are population specific and have to beviewed in the context of a particular population. Re-cently, some groups have argued that population strati-

fication (i.e. heterogenous mixture of individuals) doesnot represent a major stumbling block to execution ofwell-designed case control studies [26]. Still, the im-portance of defining the population is evident in inter-preting the significance of a SNP. For example, indi-viduals who are heterozygous for the sickle cell mu-tation in the malaria belt of Western Africa are pro-tected from complications of the infection. In otherregions of the world, where the selective pressure ofmalaria no longer exists, it is viewed differently, as alife-threatening monogenic disorder. The context ofthe variant defines whether it is a ‘balanced polymor-phism’, as in regions of Africa, or a highly penetrant,deleterious mutation.

Genetic association studies can be used to addresstwo fundamentally different questions. The first seeksto identify genetic variants that influence susceptibil-ity to a complex, multi-factorial disorder. The secondtype addresses differences in outcomes within a diseasepopulation, including differences in responses to med-ications. The latter is known as pharmacogenomics, aburgeoning field that promises to revolutionize medicaltreatment in the future [25].

5. SNPs and susceptibility to disease

Candidate gene selection based upon a proposed rolefor the variant in disease susceptibility has assumed acentral role in conducting genetic association studies.More often than not, the genes are chosen a priori, basedupon a hypothesis and studied in a suitable populationbased study. The net effect of any single SNP, espe-cially for a common disorder is generally small, mak-ing it difficult to conduct linkage analysis, even withlow confidence levels [27]. Still, a number of impor-tant studies have demonstrated the utility of examiningcandidate genes in a range of diseases, including can-cer, and diabetes [28–30]. In some circumstances, thecontribution of a functionally important SNP is pop-ulation specific; this has been elegantly shown witha population-specific TNF SNP and susceptibility tomalaria in a region of West Africa [31].

Common diseases, such as cancer or hypertensionhave the advantage of the availability of sufficientlylarge enough cohorts to examine the question in differ-ent populations. In rarer disorders, the collection of ad-equate numbers of subjects with firm clinical endpointsis a daunting challenge. After a sufficient number ofstudies have been published, it is possible to conductcase-series meta-analyses to assess the net effect of the


variant. For example, the importance of two separategenetic variants has been shown in bladder cancer [29,32,33]. With respect to both genes, the odds ratio isgreater than one, but less than two, indicating a modest,but real effect.

6. Understanding outcomes in a disease: SNPs asdisease modifiers

It has long been appreciated that among individu-als with a monogenic disorder, such as cystic fibrosisor chronic granulomatous disease, there can be hetero-geneity in the disease course. Even within families,outcomes vary greatly between members with an iden-tical primary mutation, suggesting that other geneticfactors modify outcomes. Already, a number of semi-nal studies have identified modifying SNPs in differentpopulations afflicted with a life-threatening monogenicdisorder. If this trend is confirmed in further studies,our notion of a monogenic disorder will have to “mod-ified” to include an appreciation of secondary genes,which influence outcomes. In the future, genetic coun-seling might include analysis of secondary modifyinggenes, particularly in an effort to institute preventivemeasures to intercede and avoid or ameliorate seriouscomplications.

It is notable that under stress conditions, such as aprimary immunodeficiency, common polymorphisms,mainly SNPs, can act as modifying genes for specificoutcomes. In chronic granulomatous disease, a pri-mary immunodeficiency of innate immunity (i.e., neu-trophils and monocytes fail to effectively kill invadingorganisms), individuals who inherit a variant of a lowaffinity Fc gamma receptors (FCGR2A or FCGR3B) orthe myeloperoxidase gene (MPO) are at increased riskfor immunologically mediated gastrointestinal compli-cations [34]. In a preliminary analysis, combinationsof the three informative variants conferred a higher riskfor this type of complication. This pilot study illus-trates the possible significance of the phenotype of theSNP in a pathway not directly affected by the primarymutation is intensified and provides an opportunity toinvestigate the contribution of genes in vivo [35]. Cer-tainly, it is possible that we will better appreciate therelative importance of genes within a pathway or bio-logical process by examining its effect. Simply put, itis possible to observe the relative importance of spe-cific genes by studying the effects of their variations inlarge population-based studies.

By no means, should this paradigm be restricted tomonogenic disorders. The study of host genetic factorshas been extremely informative in dissecting the molec-ular events associated with both susceptibility and pro-gression of infection with HIV-1. Interestingly, thesame variants that increase the likelihood of acquiringinfection, also appear to accelerate the course of diseaseoverall. Much of this work has focused on chemokines,namely, CCR2, CCR5, SDF1 and RANTES which arecritical co-factors for infection with HIV [36–39]. In-terestingly, disease progression has also been associ-ated with specific polymorphisms of the MHC com-plex. In particular, heterogeneity in class one MHC,namely HLA-A, B and C loci, was shown to provide aselective advantage, thus slowing progression of HIVinfection [40]. Furthermore, genetic factors that alterthe risk for developing one of several life-threateningcomplications of HIV-1 infection have been identifiedby genetic association studies. For example, a com-mon variant of the low affinity Fc gamma receptor,FCGR3A increases the risk for developing Kaposi’sSarcoma (KS) in HIV infected men, also co-infectedwith the Kaposi’s Sarcoma herpes virus, KSHV [41,42]. KS afflicted roughly 20% of infected men in theUSA prior to the development of highly active anti-retroviral therapy.

7. Pharmacogenomics

The term, pharmacogenomics refers to field of studyseeking an association between pharmacological phe-notypes and common genetic variations, namely SNPs.The underlying principle is that genetic variations canaccount for differences in response to drugs. This isnot to ignore other reasons for adverse or inadequateresponse to a drug. Certainly, the failure to respond canalso be attributed to allergies, drug-drug interactions,and erroneous prescriptions (either inappropriate dos-ings or medications). Still, the SNP revolution offersan opportunity to generate genetic profiles that will beuseful in the choice of medications. This pertains tonot only choosing the best drug, based upon a geneticpattern, but also avoiding selected drugs in individualsin whom the risk for a serious adverse event is high.From a practical point of view, the applicability of thisapproach has been demonstrated in principle, with sev-eral key examples. Dissecting the role of different stepsat the genetic level has been guided by deconstructionof the fate of medicinal drugs. These include the drugtarget, transport, and metabolism. So far, pilot studies


have not evaluated the drug as a foreign antigen, ora possible allergen. For instance, it has been shownthat patients homozygous for the G17R variant in theB2 adrenergic receptor are at risk for exacerbation ofasthmatic attacks, when using albuterol treatment [43,44]. An example of a variant with a frequency of lessthan 1% but with important clinical consequences is theTPMT variant (roughly, 0.3%). Rare individuals whoare homozygous for the TPMT variant develop severetoxicity when treated with azothioprine therapy for ei-ther leukemia or auto-immune diseases [45,46]. Thus,clinical decisions can be based upon knowledge of thegenetic profile, and in the future, allowing physiciansto tailor therapies on an individual basis.

It is conceivable that SNP studies will also definesuitable targets for therapeutic intervention. Thoughearly in its genesis, it is notable that studies in mono-genic disorders could generate important insights thatgive rise to specific therapies. For example, in cystic fi-brosis, several groups have confirmed that the presenceof a common, non-synonymous SNP in a gene locatedon a separate chromosome from the CFTR gene, themannose binding lectin, MBL2, is an adverse risk fac-tor for pulmonary outcomes. Informative MBL2 vari-ants (clustered in exon 1) have an overall frequency ofapproximately 35% and alter both function and circu-lating levels of the protein, a C-type collectin criticalfor recognition of pathogens. In the setting of CF, aphenotypically insignificant variant in the normal hostimpacts pulmonary defenses against pathogenic bacte-ria [47]. Based on the available data, recombinant man-nose binding lectin could be given to CF patients whoco-inherit one of the common MBL2 variants. Direct-ing therapy, even in a rare, monogenic disorder holdstremendous potential for treatment in the future.

8. The labor of SNP detection

The technical platforms for detecting SNPs arerapidly changing. Currently, half a dozen differentassay systems are commercially available to discrim-inate single base changes following amplification ofa unique amplicon. The flanking region and the in-formative SNP are amplified by PCR technology, usu-ally from genomic DNA. Unlike cDNA array stud-ies, which capture the full complement of messengerRNA using a common oligo dT primer, a unique set ofoligonuceotide primers is required for amplifying eachindividual SNP. In this regard, each assay has to be op-timized, even before multiplexing, making large scale

analysis more cumbersome in development and execu-tion. A number of promising platforms (see Table 2)have been developed that can increase the throughputof SNP detection and, in some cases, analyze multipleSNPs in one reaction (but not at a scale comparable tothe 50,000 messages analyzed in cDNA array studies).Price and effort have both been streamlined, but stillare not sufficiently economical to support large scalegenome wide studies, which require 100,000 or moreunique SNPs. Consequently, to analyze this number ofSNPs in a population studies, a million or more geno-types would be required. Therefore, it is not surpris-ing that until now and for the foreseeable future, thecandidate gene(s) approach will be favored.

In response to the technical challenges of large scaleSNP analysis, many groups have turned to an approachcalled DNA pooling. Instead of analyzing individualSNPs in each sample, pooled DNA (e.g., equal aliquotsof DNA from a large number of subjects) is analyzedfor allelic frequency [48–50]. Naturally, labor and costare substantially reduced, which is particularly advan-tageous in a pilot study, seeking to identify excellentcandidate SNPs for confirmation in validation studies.At the same time, large scale screening can provide suf-ficient power to minimize the likelihood of identifyingfalse positives. Pooling studies have the disadvantageof analyzing only the allelic frequency of SNPs, and inpools defined by the initial design. Thus, it is difficultto analyze more complex questions of outcomes or sub-groups, unless chosen a priori in the design of the pool-ing studies. Still, this approach is extremely promisingfor the study of complex, multi-genic common disor-ders [49,51]. In this regard, pooling is an effective ap-proach for screening candidate genes that could influ-ence susceptibility to an exposure or a disease (e.g., inlarge scale cancer cohort studies).

The strategy of interrogating pools of DNA has beensuccessfully reported in identifying SNPs contribut-ing to complex diseases and the identification of rare,Mendelian mutations [21,52]. Since we are still in theearly phase of developing this approach, there is in-tense interest in optimization of the number of subjectspooled in conjunction with the platform used to ana-lyze SNPs. In addition, aliquoting equal amounts ofgenomic DNA is a major technical challenge, which isfurther magnified by amplification-based technologiesfor SNP detection. Therefore, the margin for error issmall in the execution of DNA pooling studies, espe-cially when differences in the distribution of allelic fre-quencies might be relatively small, a common findingin genetic association studies.


Table 2Current technical platforms for SNP detection

Generic method High-throughput platform Type of analysis

Direct sequence analysisa Generally not Qualitative onlySingle base extension Promising if multiplexedSingle strand sequence Limited to single sites

Hybridization methodsa Variable Quantitative + QualitativeTarget amplificationb Moderately efficientSignal amplificationc Highly efficient

Microarraya Highly efficient Quantitative + Qualitative

Restriction enzyme analysis Not efficient Qualitative only

Conformational analysisa Moderately efficient Qualitative only

These platforms are also applicable to use for detection of rare, highly penetrant mutations.aRelies upon amplification technology (i.e., polymerase chain reaction, PCR) to generateamplicon for analysis.bExample of platform is real-time polymerase chain reaction.cExample of platform is the chip-based matrix associated laser desorption/ionization time-of-flight (MADLI-TOF).

9. The challenges in SNP analysis that lie ahead

The study of SNPs in human disease is a rich resourcefor dissecting the genetic contribution to complex-traitdiseases and modifiers of monogenic disorders. Theextraordinary spectrum of variation is also its weak-est link, because each study has to be interpreted inthe context of the population examined. The litera-ture is filled with examples of informative SNPs thatdo not reproduce in different settings. This conun-drum underscores the importance of combinations ofSNPs, which for the purpose of any population-basedstudy of unrelated individuals assumes no contributionfrom the background of SNPs. This problem raisesdifficult questions for identifying and validating gene-environment interactions as well as gene-gene inter-actions. Despite the fact that the effect of a SNP ismeasured over an extended period of time, it is diffi-cult to dissect the temporal relationship between SNPswithout a sound biological model. One of the majorhurdles of the future is to develop suitable systems toanalyze the complex interactions of SNPs and createa suitable (and reproducible model) that will be usefulfor clinical implementation of genetic risk factors. Thefield is moving towards examining collections of SNPs,which are derived from biological pathways or fami-lies of genes. An extension of this approach, knownas ‘neighboring’ SNPs, expands the genes under studyto include those that interact up and downstream fromthe core set. By saturating a pathway, one can eval-uate changes in a pathway, one that might dampen oramplify a cascade (i.e. complement cascade).

We are early in the study of SNPs, which , forthe purpose of initial studies, are viewed as individ-

ual units. However, SNPs form haplotypes and it isthe investigation of haplotypes that will probably bemost informative- both as genetic markers and as toolsto correlate genetic variation with functional outcomes(i.e. clinical states). How many haplotypes exist for agiven cluster of SNPs, either in one gene or in a groupof closely positioned genes remains to be determined,but it is critical to pursue this approach [27,53]. Sincehaplotypes are defined by the blocks of genes whichmaintain variants already in place, such as SNPs, thecataolog of SNPs will be an invaluable tool in defininghaplotypes, especially ones that include variants thatfunctionally alter the gene or gene product. If indeed,large scale sequencing is possible in the near future,then the field will have to analyze (and re-analyze cur-rent studies when possible) haplotypes. The technicalplatforms will need to be more flexible and extend fur-ther distances between variants to capture the informa-tive components of haplotypes.

Lastly, the most difficult task will be to consider theimplementation of SNPs in clinical decision making,particularly as it relates to providing recommendationsfor interventional or preventional measures, based uponthe concept of “risk”. Together with ethicists, a dia-logue must begin to address how and in what mannerto use genetic information, especially when the con-sequences of the information have pleotrpoic implica-tions for personal security, insurance and health.

10. Conclusion

We are at the beginning of an era when we can inves-tigate the functional implications of single nucleotide


polymorphisms, SNPs, and other rarer variants. Thepotential usefulness in medicine is unprecedented. Todefine risk factors for disease and pharmacological out-comes based upon genetic profiles of SNPs could rev-olutionize medical care. It also comes at a danger-ous cost of potential political and philosophical chal-lenges, which must be addressed in parallel, or actu-ally in advance, if we are to protect the rights and willof the individual. Still, these small differences cumu-latively have a staggering effect, creating the individ-uality that we recognize in each person, while at thesame time, reflect the changes that have taken placeover generations, many in response to environmentaland pathogenic challenges. The opportunity to anno-tate the differences between individuals has providedan extraordinarily rich resource for investigating com-plex genetic events, particularly as they relate to diseasesusceptibility and population genetics.

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