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Snps, Haplotypes, and Cancer: Applications in Molecular Epidemiology

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Snps, Haplotypes, and Cancer: Applications in Molecular Epidemiology Cancer Epidemiology, Biomarkers & Prevention 681 Meeting Report SNPs, Haplotypes, and Cancer: Applications in Molecular Epidemiology Timothy R. Rebbeck,1 Christine B. Ambrosone,2 Douglas A. Bell,3 Stephen J. Chanock,4 Richard B. Hayes,4 Fred F. Kadlubar,5 and Duncan C. Thomas6 1School of Medicine and Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania; 2Roswell Park Cancer Institute, Buffalo, New York; 3Environmental Genomics Section, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina; 4National Cancer Institute, Bethesda, Maryland; 5National Center for Toxicological Research, Jefferson, Arizona; and 6University of Southern California, Los Angeles, California Motivation The ongoing discovery of single nucleotide polymor- 2. Laboratory and Genotype Data: What are appropriate phisms (SNPs) and characterization of haplotypes in laboratory methods? How can adequate quality con- human populations is having a fundamental impact on trol be achieved? What is the role of public database molecular epidemiology. While likely common poly- information? morphic variants interact with exposures to cause 3. Study Design and Analysis: What study design and human cancer, the ability to evaluate the role of SNPs analysis approaches are required to achieve reproduc- in human disease is limited by available methodologies. ibility among studies? What issues need to be faced Numerous association studies of SNPs or haplotypes when dealing with population genetics structure, have been published to elucidate the etiology of cancer, including haplotype blocks and ethnic variability? but there has been inconsistency in the ability to replicate results. Therefore, a major goal of the field is to develop the analytical tools needed to examine the Choosing Genes and Haplotypes for Association explosion of genetic information available for relating Studies genetic variants to well-defined epidemiological end points. Inconsistent methodology and results from Approximately ten million SNPs exist in the human association studies have deflected attention away from genome, with an estimated two common missense var- the need to establish sound methodologies for both iants per gene (e.g., Ref. 1). At least 5 million SNPs have execution and interpretation of association study data. already been reported in public databases (2). However, Therefore, we must optimize epidemiological, statistical, the ability to apply these SNPs in association studies is and laboratory approaches to achieve credible outcomes limited by problems validating a SNP’s identity, charac- in association studies. terizing its occurrence in relevant populations, and The goal of the AACR-sponsored conference ‘‘SNPs, understanding its function. Likely, only a small subset Haplotypes, and Cancer: Applications in Molecular (perhaps 50,000–250,000) of the total number of SNPs Epidemiology’’ was to address methodological develop- in the human genome will actually confer small to mod- ments for epidemiological studies investigating complex erate effects on phenotypes that are causally related to interrelationships of SNPs, haplotypes, and environmen- disease risk (3). tal factors with cancer. As summarized below, the At the meeting, Stephen Sherry (National Center for content of this meeting included discussions related to Biotechnology Information) suggested several classes of the following: variants to consider, including SNPs, deletion/insertion 1. Gene Choice: What genes and variants should be polymorphisms (DIP), simple tandem repeat (STR) poly- studied and how? What are the relative merits and morphisms, named polymorphisms (e.g., Alu/À dimor- phisms), and multinucleotide polymorphisms (MNP). Of costs of candidate gene versus genome-wide associa- f tion studies? How should information about SNP these, 3 million SNPs are estimated to be within or function be incorporated into association studies? 2 kb upstream or downstream from a gene. Sherry re- ported a ‘‘snapshot’’ of gene-centric SNPs in the dbSNP database (http://www.ncbi.nlm.nih.gov/SNP) as of September 2003. The distribution of these variants was Cancer Epidemiol Biomarkers Prev 2004;13(5):681 – 7 63% intronic, 11% untranslated region, 1% nonsynon- Received 12/8/03; revised 1/14/04; accepted 1/22/04. ymous, 1% synonymous, 24% locus region, <1% splice The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement site, and <1% unknown coding variant. Recent surveys in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. of human genetic diversity have estimated that there are Note: Report of a conference held at Key Biscayne, FL, September 13 – 17, 2003. about 100,000–300,000 SNPs in protein coding sequences Requests for reprints: Timothy R. Rebbeck, Department of Biostatistics and (cSNPs) of the entire human genome (1). cSNPs are of Epidemiology, School of Medicine, University of Pennsylvania, 904 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021. Phone: (215) 898-1793; particular interest because some of them, termed non- Fax: (215) 573-2265. E-mail: [email protected] synonymous SNPs (nsSNPs) or missense variants, Cancer Epidemiol Biomarkers Prev 2004;13(5). May 2004 Downloaded from cebp.aacrjournals.org on September 30, 2021. © 2004 American Association for Cancer Research. 682 SNPs, Haplotypes, and Cancer introduce amino acid changes into their encoded pro- genes based on sequence homology or gene family; and teins. nsSNPs constitute about 1% of all SNPs. The rarity (c) SNP haplotype studies that start with a ‘‘simple’’ of nsSNPs may be a consequence of selective pressures. haplotypes (often including known nsSNPs or rSNPs), However, a significant fraction of functionally important which can be expanded to increase the density of SNPs molecular diversity in the human population likely is across the haplotype. Regardless of the approach for attributable to the effects on protein function caused by choosing markers, validation of associations in both nsSNPs or alterations in the regulation of genes (known comparable and different genetic backgrounds will be as rSNPs). For example, the kinetic parameters of en- required. At the same time, the working hypothesis will zymes, the DNA binding properties of proteins that likely become increasingly complex as knowledge of regulate transcription, the signal transduction activities interrelated pathways is considered to account for of transmembrane receptors, and the architectural roles relevant biological interactions. of structural proteins are all susceptible to perturbation William Evans (St. Jude’s Children’s Hospital), Gareth by nsSNPs and their associated amino acid polymor- Morgan (University of Leeds), and Richard Weinshil- phisms. Similarly, John Potter (Fred Hutchinson Cancer boum (Mayo Clinic) presented the pharmacogenetics and Research Center) argued that perturbations in the reg- pharmacogenomics paradigm for studies of candidate ulation of key elements of a pathway could influence gene and SNP identification, gene discovery, and the risk for cancer outcomes either directly or through genotype-environment interaction. Pharmacogenetics interacting pathways. Tom Hudson (McGill University) and pharmacogenomics are excellent paradigms for added that a major challenge of future studies will be studies that extend beyond etiology to studies of to identify and characterize regulatory variants. The re- treatment response, gene expression changes, survival, searcher will have to ask whether the regulatory SNP side effects or toxicities relating to specific agents, timing itself or the haplotype in which it may be imbedded is of later events, and dosing. With respect to candidate really the functional unit of interest. genes, Weinshilboum stated that the paradigm for The identification of biologically meaningful, disease- functional gene discovery in pharmacogenetics began causing variants from among this large amount of by using the distribution of phenotypic traits to infer genomic variability is a key challenge for association genetic effects. More recently, it has been possible to studies. Joel Hirschhorn (Massachusetts Institute of relate functionally significant DNA sequence variation to Technology) invoked the common disease, common clinically important variability. Both of these approaches gene hypothesis (4, 5) using methods that rely on are complementary and should be both done to un- knowledge of candidate genes or methods that rely on derstand the functional significance of genes and SNPs. linkage disequilibrium (LD). Other publications have Evans also stated that gene expression profiling can be espoused the importance of rare variants as determi- valuable to identify and characterize candidate genes nants for common diseases (4). Candidate gene ap- (e.g., for treatment response). Evans also presented proaches have the advantage of maximizing inferences examples in which genetic profiles differed by exposure about biological plausibility and disease causality. (i.e., where combinations of drug treatments did not However, candidate approaches are limited by the evoke the same expression profile as each treatment amount of information that is available about the individually). Therefore, expression profile approaches function of the gene in a specific disease process. Al- may be useful for identification of novel genes, charac- ternatively, genome-wide approaches have the advan-
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