American Journal of Epidemiology Advance Access originally published online on August 21, 2006
American Journal of Epidemiology 2006 164(8):794-804; doi:10.1093/aje/kwj269
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Practice of Epidemiology |
Candidate Single Nucleotide Polymorphism Selection using Publicly Available Tools: A Guide for Epidemiologists
1 Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
2 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
3 National Institute on Drug Abuse, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
4 Laboratory of Population Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
Correspondence to Dr. Alice Sigurdson, Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 6120 Executive Boulevard, EPS 7060, MSC 7238, Bethesda, MD 20892-7238 (e-mail: sigurdsa{at}mail.nih.gov).
Single nucleotide polymorphisms (SNPs) are the most common form of human genetic variation, with millions present in the human genome. Because only 1% might be expected to confer more than modest individual effects in association studies, the selection of predictive candidate variants for complex disease analyses is formidable. Technologic advances in SNP discovery and the ever-changing annotation of the genome have led to massive informational resources that can be difficult to master across disciplines. A simplified guide is needed. Although methods for evaluating nonsynonymous coding SNPs are known, several other publicly available computational tools can be utilized to assess polymorphic variants in noncoding regions. As an example, the authors applied multiple methods to select SNPs in DNA double-strand break repair genes. They chose to evaluate SNPs that occurred among a preexisting set of 57 validated assays and to justify new assay development for 83 potential SNPs in the DNA-dependent protein kinase catalytic subunit. Of the 140 SNPs, the authors eliminated 119 variants with low or neutral predictions. The existing computational methods they used and the semiquantitative relative ranking strategy they developed can be adapted to a priori SNP selection or post hoc evaluation of variants identified in whole genome scans or within haplotype blocks associated with disease. The authors show a "real world" application of some existing bioinformatics tools for use in large epidemiologic studies and genetic analyses. They also reviewed alternative approaches that provide related information.
amino acid sequence; base sequence; epidemiologic methods; genetic predisposition to disease; polymorphism, single nucleotide
Abbreviations: DNAPKcs, catalytic subunit of the DNA protein kinase; ESE, exonic splicing enhancer; mRNA, messenger RNA; NCBI, National Center for Biotechnology Information; SIFT, sorting intolerant from tolerant; SNP, single nucleotide polymorphism; TFBS, transcription factor binding site; UCSC, University of California, Santa Cruz; UTR, untranslated region
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