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Genomic screening and replication using the same data set in family-based association testing


The Human Genome Project and its spin-offs are making it increasingly feasible to determine the genetic basis of complex traits using genome-wide association studies. The statistical challenge of analyzing such studies stems from the severe multiple-comparison problem resulting from the analysis of thousands of SNPs. Our methodology for genome-wide family-based association studies, using single SNPs or haplotypes, can identify associations that achieve genome-wide significance. In relation to developing guidelines for our screening tools, we determined lower bounds for the estimated power to detect the gene underlying the disease-susceptibility locus, which hold regardless of the linkage disequilibrium structure present in the data. We also assessed the power of our approach in the presence of multiple disease-susceptibility loci. Our screening tools accommodate genomic control and use the concept of haplotype-tagging SNPs. Our methods use the entire sample and do not require separate screening and validation samples to establish genome-wide significance, as population-based designs do.

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Figure 1: Plots of power versus the number of top trait-marker combination retained after first-level screening under different screening scenarios.


  1. Sherry, S.T., Ward, M. & Sirotkin, K. dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res. 9, 677–679 (1999).

    CAS  PubMed  Google Scholar 

  2. International SNP Map Working Group. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409, 928–933 (2001).

  3. Marnellos, G. High-throughput SNP analysis for genetic association studies. Curr. Opin. Drug Discov. Devel. 6, 317–321 (2003).

    CAS  PubMed  Google Scholar 

  4. Bonferroni, C.E. Teoria statistica delle classi e calcolo delle probabilita. In Volume in Onore di Ricarrdo dalla Volta, Universita di Firenza, 1–62 (1937).

    Google Scholar 

  5. Hochberg, Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75, 800–802 (1988).

    Article  Google Scholar 

  6. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  7. Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).

    Article  Google Scholar 

  8. Lange, C., DeMeo, D.L., Silverman, E., Weiss, S. & Laird, N.M. Using the noninformative families in family-based association tests: a powerful new testing strategy. Am. J. Hum. Genet. 73, 801–811 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lange, C. et al. A new powerful non-parametric two-stage approach for testing multiple phenotypes in family-based association studies. Hum. Hered. 56, 10–17 (2003).

    Article  PubMed  Google Scholar 

  10. Lange, C., DeMeo, D.L., Silverman, E.K., Weiss, S.T. & Laird, N.M. PBAT: Tools for family-based association studies. Am. J. Hum. Genet. 74, 367–369 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Van Steen, K. & Lange, C. PBAT: a comprehensive software package for genome-wide association analysis of complex family-based studies. Hum. Genomics 2, 70–74 (2005).

    Article  Google Scholar 

  12. Childhood Asthma Management Program Research Group. The childhood asthma management program (CAMP): design, rationale, and methods. Control. Clin. Trials 20, 91–120 (1999).

  13. Lyon, H. et al. IL10 gene polymorphisms are associated with asthma phenotypes in children. Genet. Epidemiol. 26, 155–165 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lange, C. & Laird, N.M. On a general class of conditional tests for family-based association studies in genetics: the asymptotic distribution, the conditional power, and optimality considerations. Genet. Epidemiol. 23, 165–180 (2002).

    Article  PubMed  Google Scholar 

  15. Laird, N., Horvath, S. & Xu, X. Implementing a unified approach to family based tests of association. Genet. Epidemiol. 19 Suppl 1, S36–S42 (2000).

    Article  PubMed  Google Scholar 

  16. Kennedy, G.C. et al. Large-scale genotyping of complex DNA. Nat. Biotechnol. 21, 1233–1237 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Schaid, D.J. et al. Comparison of microsatellites versus single nucleotide polymorphisms by a genome linkage screen for prostate cancer susceptibility loci. Am. J. Hum. Genet. 75, 948–965 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lange, C. & Laird, N.M. Power calculations for a general class of family-based association tests: Dichotomous traits. Am. J. Hum. Genet. 71, 575–584 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Lange, C., DeMeo, D.L. & Laird, N.M. Power calculations for a general class of family-based association tests: Quantitative traits. Am. J. Hum. Genet. 71, 1330–1341 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  PubMed  Google Scholar 

  21. Duffy, D.L., Martin, N.G., Battistutta, D., Hopper, J.L. & Mathews, J.D. Genetics of asthma and hay fever in Australian twins. Am. Rev. Respir. Dis. 142, 1351–1358 (1990).

    Article  CAS  PubMed  Google Scholar 

  22. Weiss, S.T. & Raby, B.A. Asthma genetics 2003. Hum. Mol. Genet. 13, R83–R89 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. Randolph, A.G. et al. The IL12B gene is associated with asthma. Am. J. Hum. Genet. 75, 709–715 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lange, C. et al. A family-based association test for repeatedly measured quantitative traits adjusting for unknown environmental and/or polygenic effects. Statistical Applications in Genetics and Molecular Biology [online] 3, Article 17 (2004).

  25. Holm, S. A simple sequentially rejective multiple testing procedure. Scand. J. Stat. 6, 65–70 (1979).

    Google Scholar 

  26. Sidak, Z. On probabilities of rectangles in multivariate Student distributions: their dependence on correlations. Ann. Math. Statist. 42, 169–175 (1971).

    Article  Google Scholar 

  27. Satagopan, J.M., Venkatraman, E.S. & Begg, C.B. Two-stage designs for gene-disease association studies with sample size constraints. Biometrics 60, 589–597 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  28. International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003).

  29. Zhai, W., Todd, M.J. & Nielsen, R. Is haplotype block identification useful for association mapping studies? Genet. Epidemiol. 27, 80–83 (2004).

    Article  PubMed  Google Scholar 

  30. Churchill, G.A. & Doerge, R.W. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Churchill, G.A. & Doerge, R.W. Permutation tests for multiple loci affecting a quantitative character. Genetics 142, 285–294 (1996).

    PubMed  PubMed Central  Google Scholar 

  32. Lin, S., Chakravarti, A. & Cutler, D.J. Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies. Nat. Genet. 36, 1181–1188 (2004).

    Article  CAS  PubMed  Google Scholar 

  33. Spielman, R.S., McGinnis, R.E. & Ewens, W.J. Transmission test for linkage disequilibrium: the insuline gene region and insulin-dependent diabetes mellitus (IDDM). Am. J. Hum. Genet. 52, 506–516 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Lange, C., Silverman, E., Weiss, S.T., Xu, X. & Laird, N.M. A multivariate family-based test using generalized estimating equations: FBAT-GEE. Biostatistics 4, 195–206 (2003).

    Article  PubMed  Google Scholar 

  35. Rabinowitz, D. & Laird, N.M. A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum. Hered. 50, 211–223 (2000).

    Article  CAS  PubMed  Google Scholar 

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We thank the families with CAMP for their participation in the CAMP Genetics Ancillary Study, supported by the US National Heart, Lung and Blood Institute; the CAMP investigators and research team, supported by the US National Heart, Lung and Blood Institute, for collection of CAMP Genetic Ancillary Study data; N.A. Beattie and J.T. Follweiler for editorial help; and D. Cutler for comments and suggestions. Additional support for this research came from the US National Heart Lung and Blood Institute. K.V.S. was supported by a grant from the US National Institutes of Health. All work undertaken in the CAMP Genetics Ancillary Study was done at the Channing Laboratory at the Brigham and Women's Hospital under appropriate CAMP policies and human subject protections. M.B.M. was supported by the National Research Service Award, Training Program in Psychiatric Epidemiology and Biostatistics.

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Correspondence to Kristel Van Steen.

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Supplementary information

Supplementary Fig. 1

Estimated lower bounds for PBAT's screening techniques accounting for genomic control. (PDF 154 kb)

Supplementary Note (PDF 65 kb)

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Van Steen, K., McQueen, M., Herbert, A. et al. Genomic screening and replication using the same data set in family-based association testing. Nat Genet 37, 683–691 (2005).

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