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A single-nucleotide polymorphism tagging set for human drug metabolism and transport

Abstract

Interindividual variability in drug response, ranging from no therapeutic benefit to life-threatening adverse reactions, is influenced by variation in genes that control the absorption, distribution, metabolism and excretion of drugs1. We genotyped 904 single-nucleotide polymorphisms (SNPs) from 55 such genes in two population samples (European and Japanese) and identified a set of tagging SNPs that represents the common variation in these genes, both known and unknown. Extensive empirical evaluations, including a direct assessment of association with candidate functional SNPs in a new, larger population sample, validated the performance of these tagging SNPs and confirmed their utility for linkage-disequilibrium mapping in pharmacogenetics. The analyses also suggest that rare variation is not amenable to tagging strategies.

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Figure 1: Two procedures for evaluating the ability of tSNP sets to tag dropped SNPs, thus simulating SNPs not yet identified.
Figure 2: The CYP2C clusters.
Figure 3: Performance of tags selected from the full data set.
Figure 4: Performance of tags selected from the reduced data set (SNPs with MAFs < 5% excluded).
Figure 5: The effect of initial genotyping density on tag performance.
Figure 6: Performance of the selected tSNPs in representing candidate functional variation.

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References

  1. Goldstein, D.B., Tate, S.K. & Sisodiya, S.M. Pharmacogenetics goes genomic. Nat. Rev. Genet. 4, 937–947 (2003).

    Article  CAS  Google Scholar 

  2. Goldstein, D.B., Ahmadi, K.R., Weale, M.E. & Wood, N.W. Genome scans and candidate gene approaches in the study of common diseases and variable drug responses. Trends Genet. 19, 615–622 (2003).

    Article  CAS  Google Scholar 

  3. Weale, M.E. et al. Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: implications for linkage-disequilibrium gene mapping. Am. J. Hum. Genet. 73, 551–565 (2003).

    Article  CAS  Google Scholar 

  4. Kamatani, N. et al. Large-scale single-nucleotide polymorphism (SNP) and haplotype analyses, using dense SNP maps, of 199 drug-related genes in 752 subjects: the analysis of the association between uncommon SNPs within haplotype blocks and the haplotypes constructed with haplotype-tagging SNPs. Am. J. Hum. Genet. 75, 106–120 (2004).

    Article  Google Scholar 

  5. Pritchard, J.P. & Przeworski, M. Linkage disequilibrium in humans: models and data. Am. J. Hum. Genet. 69, 1–14 (2001).

    Article  CAS  Google Scholar 

  6. Chapman, J.M. et al. Detecting disease associations due to linkage disequilibrium: a class of tests and the determinants of statistical power. Hum. Hered. 56, 18–31 (2003).

    Article  Google Scholar 

  7. Carlson, C.S. et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am. J. Hum. Genet. 74, 106–120 (2004).

    Article  CAS  Google Scholar 

  8. Schulze, T.G. et al. Defining haplotype blocks and tag single-nucleotide polymorphisms in the human genome. Hum. Mol. Genet. 13, 335–342 (2004).

    Article  CAS  Google Scholar 

  9. Ke, X. et al. The impact of SNP density on fine-scale patterns of linkage disequilibrium. Hum. Mol. Genet. 13, 577–588 (2004).

    Article  CAS  Google Scholar 

  10. Gabriel, S.B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002).

    Article  CAS  Google Scholar 

  11. Salisbury, B.A. et al. SNP and haplotype variation in the human genome. Mutat. Res. 526, 53–61 (2003).

    Article  CAS  Google Scholar 

  12. Capelli, C. et al. A Y chromosome census of the British Isles. Curr. Biol. 27, 979–984 (2003).

    Article  Google Scholar 

  13. Taylor, J.D. et al. Flow cytometric platform for high-throughput single nucleotide polymorphism analysis. Biotechniques 30, 661–669 (2001).

    Article  CAS  Google Scholar 

  14. Qin, Z.S., Niu, T. & Liu, J.S. Partition-ligation-expectation-maximization algorithm for haplotype inference with single nucleotide polymorphisms. Am. J. Hum. Genet. 71, 1242–1247 (2002).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank D.H. Smart, T.T. Ashton, S.A. Shouse and B. Zheng for their contributions in bioinformatics, sequencing, SNP discovery and data management. K.R.A. and N.S. were supported by awards from the Leverhulme Trust to D.B.G. A University College London Hospitals trust clinical research and development committee grant to N.W.W. and D.B.G. is acknowledged. D.B.G. holds a Royal Society/Wolfson Research Merit Award.

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Correspondence to David B Goldstein.

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Supplementary information

Supplementary Fig. 1

Distribution of minor allele frequencies and marker characteristics for the 55 genes. (PDF 98 kb)

Supplementary Fig. 2

Number of high-LD blocks and percent sequence belonging to high-LD blocks, as a function of block size in the CEPH and the Japanese. (PDF 71 kb)

Supplementary Fig. 3

Initial sample size and performance of tags selected in the full data set. (PDF 90 kb)

Supplementary Fig. 4

Initial sample size and performance of tags selected in the reduced data set (SNPs excluded with MAF < 0.05). (PDF 140 kb)

Supplementary Fig. 5

Comparison of results obtained through bootstrapping (sampling with replacement) and splitting (sampling without replacement). (PDF 91 kb)

Supplementary Fig. 6

SNP-by-SNP performance of the tags selected for each gene or gene complex (SNPs excluded with MAF < 0.05) in the CEPH. (PDF 157 kb)

Supplementary Fig. 7

SNP-by-SNP performance of the tags selected for each gene or gene complex (SNPs excluded with MAF < 0.05) in the Japanese. (PDF 92 kb)

Supplementary Fig. 8

Plot of the minor allele frequency (MAF) of 69 SNPs in 238 individuals from Aberdeen against 64 CEPH individuals. (PDF 65 kb)

Supplementary Fig. 9

The effect of initial genotyping density on tag performance. (PDF 87 kb)

Supplementary Table 1

Summary information on all ADME gene clusters. (XLS 68 kb)

Supplementary Table 2

Summary information of all 904 SNPs studied as part of this study. (XLS 157 kb)

Supplementary Table 3

The tagging SNPs for the CEPH sample. (PDF 71 kb)

Supplementary Table 4

The tagging SNPs for the Japanese sample. (PDF 69 kb)

Supplementary Table 5

The cosmopolitan tagging SNP set. (PDF 73 kb)

Supplementary Table 6

List of the 9 functional variants included in the analyses. (PDF 86 kb)

Supplementary Table 7

List of the variants genotyped in 238 individuals from Aberdeen for direct evaluation of the utility of tSNPs. (PDF 60 kb)

Supplementary Table 8

The list of genes organized into clusters to quantify the effect of long-range LD. (PDF 48 kb)

Supplementary Table 9

The list of genes used in the density experiments. (PDF 46 kb)

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Ahmadi, K., Weale, M., Xue, Z. et al. A single-nucleotide polymorphism tagging set for human drug metabolism and transport. Nat Genet 37, 84–89 (2005). https://doi.org/10.1038/ng1488

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