The Annals of Applied Statistics

Replicability analysis for genome-wide association studies

Ruth Heller and Daniel Yekutieli

Full-text: Open access

Abstract

The paramount importance of replicating associations is well recognized in the genome-wide associaton (GWA) research community, yet methods for assessing replicability of associations are scarce. Published GWA studies often combine separately the results of primary studies and of the follow-up studies. Informally, reporting the two separate meta-analyses, that of the primary studies and follow-up studies, gives a sense of the replicability of the results. We suggest a formal empirical Bayes approach for discovering whether results have been replicated across studies, in which we estimate the optimal rejection region for discovering replicated results. We demonstrate, using realistic simulations, that the average false discovery proportion of our method remains small. We apply our method to six type two diabetes (T2D) GWA studies. Out of 803 SNPs discovered to be associated with T2D using a typical meta-analysis, we discovered 219 SNPs with replicated associations with T2D. We recommend complementing a meta-analysis with a replicability analysis for GWA studies.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 1 (2014), 481-498.

Dates
First available in Project Euclid: 8 April 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1396966295

Digital Object Identifier
doi:10.1214/13-AOAS697

Mathematical Reviews number (MathSciNet)
MR3191999

Zentralblatt MATH identifier
06302244

Keywords
Combined analysis empirical Bayes false discovery rate meta-analysis replication reproducibility type 2 diabetes

Citation

Heller, Ruth; Yekutieli, Daniel. Replicability analysis for genome-wide association studies. Ann. Appl. Stat. 8 (2014), no. 1, 481--498. doi:10.1214/13-AOAS697. https://projecteuclid.org/euclid.aoas/1396966295


Export citation

References

  • Benjamini, Y. and Heller, R. (2008). Screening for partial conjunction hypotheses. Biometrics 64 1215–1222.
  • Benjamini, Y., Heller, R. and Yekutieli, D. (2009). Selective inference in complex research. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 267 1–17.
  • Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57 289–300.
  • Benjamini, Y., Krieger, A. M. and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93 491–507.
  • Benjamini, Y. and Yekutieli, D. (2005). Quantitative trait Loci analysis using the false discovery rate. Genetics 171 783–790.
  • Bogomolov, M. and Heller, R. (2013). Discovering findings that replicate from a primary study of high dimension to a follow-up study. J. Amer. Statist. Assoc. 108 1480–1492.
  • Cox, D. R. and Reid, N. (2004). A note on pseudolikelihood constructed from marginal densities. Biometrika 91 729–737.
  • Efron, B. (2008). Microarrays, empirical Bayes and the two-groups model. Statist. Sci. 23 1–22.
  • Efron, B. (2010). Large-Scale Inference. Cambridge Univ. Press, Cambridge.
  • Efron, B. and Tibshirani, R. (2002). Empirical Bayes methods and false discovery rates for microarrays. Genet. Epidemiol. 23 70–86.
  • Hedges, L. V. and Olkin, I. (1985). Statistical Methods for Meta-Analysis. Academic Press, Orlando, FL.
  • Heller, R. and Yekutieli, D. (2014). Supplement to “Replicability analysis for genome-wide association studies.” DOI:10.1214/13-AOAS697SUPP.
  • Ioannidis, J. P. A. and Khoury, M. J. (2011). Improving validation practices in “omics” research. Science 334 1230–1232.
  • Jin, J. and Cai, T. T. (2007). Estimating the null and the proportional of nonnull effects in large-scale multiple comparisons. J. Amer. Statist. Assoc. 102 495–506.
  • Kraft, P., Zeggini, E. and Ioannidis, J. P. A. (2009). Replication in genome-wide association studies. Statist. Sci. 24 561–573.
  • McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A. and Hirschhorn, J. N. (2008). Genome-wide association studies for complex traits: Consensus, uncertainty and challenges. Nat. Rev. Genet. 9 356–369.
  • McLachlan, G. and Peel, D. (2000). Finite Mixture Models. Wiley, New York.
  • Muralidharan, O. (2010). An empirical Bayes mixture method for effect size and false discovery rate estimation. Ann. Appl. Stat. 4 422–438.
  • Natarajan, L., Pu, M. and Messer, K. (2012). Statistical tests for the intersection of independent lists of genes: Sensitivity, FDR, and type I error control. Ann. Appl. Stat. 6 521–541.
  • NCI-NHGRI (2007). Replicating genotype-phenotype associations. Nature 447 655–660.
  • Owen, A. B. (2009). Karl Pearson’s meta-analysis revisited. Ann. Statist. 37 3867–3892.
  • Skol, A. D., Scott, L. J., Abecasis, G. R. and Boehnke, M. (2006). Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat. Genet. 38 209–213.
  • Storey, J. D. (2003). The positive false discovery rate: A Bayesian interpretation and the $q$-value. Ann. Statist. 31 2013–2035.
  • Storey, J. D. (2007). The optimal discovery procedure: A new approach to simultaneous significance testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 69 347–368.
  • Storey, J. D. and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100 9440–9445.
  • Strimmer, K. (2008). A unified approach to false discovery rate estimation. BMC Bioinformatics 9 303.
  • Su, Z., Marchini, J. and Donnelly, P. (2011). HAPGEN2: Simulation of multiple disease SNPs. Bioinformatics 27 2304–2305.
  • Sun, W. and Cai, T. T. (2007). Oracle and adaptive compound decision rules for false discovery rate control. J. Amer. Statist. Assoc. 102 901–912.
  • Sun, W. and Wei, Z. (2011). Multiple testing for pattern identification, with applications to microarray time-course experiments. J. Amer. Statist. Assoc. 106 73–88.
  • The International HapMap Consortium (2003). The International Hapmap project. Nature 426 789–796.
  • Voight, B. F., Scott, L. J., Steinthorsdottir, V., Morris, A. P., Dina, C., Welch, R. P., Zeggini, E., Huth, C., Aulchenko, Y. S., Thorleifsson, G., McCulloch, L. J., Ferreira, T., Grallert, H., Amin, N., Wu, G., Willer, C. J., Raychaudhuri, S., McCarroll, S. A., Langenberg, C., Hofmann, O. M., Dupuis, J., Qi, L., Segrè, A. V., van Hoek, M., Navarro, P., Ardlie, K., Balkau, B., Benediktsson, R., Bennett, A. J., Blagieva, R., Boerwinkle, E., Bonnycastle, L. L., Bengtsson Boström, K., Bravenboer, B., Bumpstead, S., Burtt, N. P., Charpentier, G., Chines, P. S., Cornelis, M., Couper, D. J., Crawford, G., Doney, A. S., Elliott, K. S., Elliott, A. L., Erdos, M. R., Fox, C. S., Franklin, C. S., Ganser, M., Gieger, C., Grarup, N., Green, T., Griffin, S., Groves, C. J., Guiducci, C., Hadjadj, S., Hassanali, N., Herder, C., Isomaa, B., Jackson, A. U., Johnson, P. R., Jorgensen, T., Kao, W. H., Klopp, N., Kong, A., Kraft, P., Kuusisto, J., Lauritzen, T., Li, M., Lieverse, A., Lindgren, C. M., Lyssenko, V., Marre, M., Meitinger, T., Midthjell, K., Morken, M. A., Narisu, N., Nilsson, P., Owen, K. R., Payne, F., Perry, J. R., Petersen, A. K., Platou, C., Proença, C., Prokopenko, I., Rathmann, W., Rayner, N. W., Robertson, N. R., Rocheleau, G., Roden, M., Sampson, M. J., Saxena, R., Shields, B. M., Shrader, P., Sigurdsson, G., Sparsø, T., Strassburger, K., Stringham, H. M., Sun, Q., Swift, A. J., Thorand, B., Tichet, J., Tuomi, T., van Dam, R. M., van Haeften, T. W., van Herpt, T., van Vliet-Ostaptchouk, J. V., Walters, G. B., Weedon, M. N., Wijmenga, C., Witteman, J., Bergman, R. N., Cauchi, S., Collins, F. S., Gloyn, A. L., Gyllensten, U., Hansen, T., Hide, W. A., Hitman, G. A., Hofman A., Hunter, D. J., Hveem, K., Laakso, M., Mohlke, K. L., Morris, A. D., Palmer, C. N., Pramstaller, P. P., Rudan, I., Sijbrands, E., Stein, L. D., Tuomilehto, J., Uitterlinden, A., Walker, M., Wareham, N. J., Watanabe, R. M., Abecasis, G. R., Boehm, B. O., Campbell, H., Daly, M. J., Hattersley, A. T., Hu, F. B., Meigs, J. B., Pankow, J. S., Pedersen, O., Wichmann, H. E., Barroso, I., Florez, J. C., Frayling, T. M., Groop, L., Sladek, R., Thorsteinsdottir, U., Wilson, J. F., Illig, T., Froguel, P., van Duijn, C. M., Stefansson, K., Altshuler, D., Boehnke, M., McCarthy, M. I., MAGIC investigators and GIANT Consortium (2010). Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nature Genetics 42 579–589.
  • Wakefield, J. (2007). A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81 208–227.
  • Zeggini, E., Weedon, M. N., Lindgren, C. M., Frayling, T. M., Elliott, K. S., Lango, H., Timpson, N. J., Perry, J. R. B., Rayner, N. W. et al. (2007). Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316 1336–1341.

Supplemental materials

  • Supplementary material: Supplementary material for replicability analysis for genome-wide association studies. Supplementary material includes the proof of Proposition 3.1, additional numerical examples that demonstrate the difference between optimal rejection regions and the loss in power that occurs when the rejection region is chosen suboptimally based on $p$-values, discussion of the necessity to specify the direction of the alternative for estimation of the local Bayes FDRs, technical details of the EM algorithm, the full table of results for the T2D example, the figure of empirical $z$-scores for the T2D studies example, and an additional figure of simulation results.