Increasing Power of Groupwise Association Test with Likelihood Ratio Test

  • Jae Hoon Sul
  • Buhm Han
  • Eleazar Eskin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6577)


Sequencing studies have been discovering a numerous number of rare variants, allowing the identification of the effects of rare variants on disease susceptibility. As a method to increase the statistical power of studies on rare variants, several groupwise association tests that group rare variants in genes and detect associations between groups and diseases have been proposed. One major challenge in these methods is to determine which variants are causal in a group, and to overcome this challenge, previous methods used prior information that specifies how likely each variant is causal. Another source of information that can be used to determine causal variants is observation data because case individuals are likely to have more causal variants than control individuals. In this paper, we introduce a likelihood ratio test (LRT) that uses both data and prior information to infer which variants are causal and uses this finding to determine whether a group of variants is involved in a disease. We demonstrate through simulations that LRT achieves higher power than previous methods. We also evaluate our method on mutation screening data of the susceptibility gene for ataxia telangiectasia, and show that LRT can detect an association in real data. To increase the computational speed of our method, we show how we can decompose the computation of LRT, and propose an efficient permutation test. With this optimization, we can efficiently compute an LRT statistic and its significance at a genome-wide level. The software for our method is publicly available at


Likelihood Ratio Test Minor Allele Frequency Permutation Test Prior Information Rare Variant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Corder, E.H., Saunders, A.M., Strittmatter, W.J., Schmechel, D.E., Gaskell, P.C., Small, G.W., Roses, A.D., Haines, J.L., Pericak-Vance, M.A.: Gene dose of apolipoprotein e type 4 allele and the risk of alzheimer’s disease in late onset families. Science 261(5123), 921–923 (1993)CrossRefGoogle Scholar
  2. 2.
    Bertina, R.M., Koeleman, B.P.C., Koster, T., Rosendaal, F.R., Dirven, R.J., de Ronde, H., Van Der Velden, P.A., Reitsma, P.H., et al.: Mutation in blood coagulation factor v associated with resistance to activated protein c. Nature 369(6475), 64–67 (1994)CrossRefGoogle Scholar
  3. 3.
    Altshuler, D., Hirschhorn, J.N., Klannemark, M., Lindgren, C.M., Vohl, M.C., Nemesh, J., Lane, C.R., Schaffner, S.F., Bolk, S., Brewer, C., et al.: The common pparγ pro12ala polymorphism is associated with decreased risk of type 2 diabetes. Nature Genetics 26(1), 76–80 (2000)CrossRefGoogle Scholar
  4. 4.
    Gorlov, I.P., Gorlova, O.Y., Sunyaev, S.R., Spitz, M.R., Amos, C.I.: Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am. J. Hum. Genet. 82(1), 100–112 (2008)CrossRefGoogle Scholar
  5. 5.
    Kryukov, G.V., Pennacchio, L.A., Sunyaev, S.R.: Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am. J. Hum. Genet. 80(4), 727–739 (2007)CrossRefGoogle Scholar
  6. 6.
    Cohen, J.C., Kiss, R.S., Pertsemlidis, A., Marcel, Y.L., McPherson, R., Hobbs, H.H.: Multiple rare alleles contribute to low plasma levels of hdl cholesterol. Science 305(5685), 869–872 (2004)CrossRefGoogle Scholar
  7. 7.
    Fearnhead, N.S., Wilding, J.L., Winney, B., Tonks, S., Bartlett, S., Bicknell, D.C., Tomlinson, I.P.M., Mortensen, N.J.M., Bodmer, W.F.: Multiple rare variants in different genes account for multifactorial inherited susceptibility to colorectal adenomas. Proc. Natl. Acad. Sci. USA 101(45), 15992–15997 (2004)CrossRefGoogle Scholar
  8. 8.
    Ji, W., Foo, J.N., O’Roak, B.J., Zhao, H., Larson, M.G., Simon, D.B., Newton-Cheh, C., State, M.W., Levy, D., Lifton, R.P.: Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat. Genet. 40(5), 592–599 (2008)CrossRefGoogle Scholar
  9. 9.
    Bodmer, W., Bonilla, C.: Common and rare variants in multifactorial susceptibility to common diseases. Nature Genetics 40(6), 695–701 (2008)CrossRefGoogle Scholar
  10. 10.
    Romeo, S., Pennacchio, L.A., Fu, Y., Boerwinkle, E., Tybjaerg-Hansen, A., Hobbs, H.H., Cohen, J.C.: Population-based resequencing of angptl4 uncovers variations that reduce triglycerides and increase hdl. Nat. Genet. 39(4), 513–516 (2007)CrossRefGoogle Scholar
  11. 11.
    Blauw, H.M., Veldink, J.H., van Es, M.A., van Vught, P.W., Saris, C.G.J., van der Zwaag, B., Franke, L., Burbach, J.P.H., Wokke, J.H., Ophoff, R.A., van den Berg, L.H.: Copy-number variation in sporadic amyotrophic lateral sclerosis: a genome-wide screen. Lancet Neurol. 7(4), 319–326 (2008)CrossRefGoogle Scholar
  12. 12.
    Consortium, I.S.: Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature 455(7210), 237–241 (2008)CrossRefGoogle Scholar
  13. 13.
    Xu, B., Roos, J.L., Levy, S., Van Rensburg, E.J., Gogos, J.A., Karayiorgou, M.: Strong association of de novo copy number mutations with sporadic schizophrenia. Nature Genetics 40(7), 880–885 (2008)CrossRefGoogle Scholar
  14. 14.
    Walsh, T., McClellan, J.M., McCarthy, S.E., Addington, A.M., Pierce, S.B., Cooper, G.M., Nord, A.S., Kusenda, M., Malhotra, D., Bhandari, A., Stray, S.M., Rippey, C.F., Roccanova, P., Makarov, V., Lakshmi, B., Findling, R.L., Sikich, L., Stromberg, T., Merriman, B., Gogtay, N., Butler, P., Eckstrand, K., Noory, L., Gochman, P., Long, R., Chen, Z., Davis, S., Baker, C., Eichler, E.E., Meltzer, P.S., Nelson, S.F., Singleton, A.B., Lee, M.K., Rapoport, J.L., King, M.C.C., Sebat, J.: Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science 320(5875), 539–543 (2008)CrossRefGoogle Scholar
  15. 15.
    Morgenthaler, S., Thilly, W.G.: A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (cast). Mutat. Res. 615(1-2), 28–56 (2007)CrossRefGoogle Scholar
  16. 16.
    Li, B., Leal, S.M.: Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83(3), 311–321 (2008)CrossRefGoogle Scholar
  17. 17.
    Madsen, B.E., Browning, S.R.: A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5(2), e1000384 (2009)CrossRefGoogle Scholar
  18. 18.
    Price, A.L., Kryukov, G.V., de Bakker, P.I.W., Purcell, S.M., Staples, J., Wei, L.J.J., Sunyaev, S.R.: Pooled association tests for rare variants in exon-resequencing studies. Am. J. Hum. Genet. 86(6), 832–838 (2010)CrossRefGoogle Scholar
  19. 19.
    Sul, J.H., Han, B., He, D., Eskin, E.: An optimal weighted aggregated association test for identification of rare variants involved in common diseases. Genetics (in press) Google Scholar
  20. 20.
    Tavtigian, S.V., Deffenbaugh, A.M., Yin, L., Judkins, T., Scholl, T., Samollow, P.B., de Silva, D., Zharkikh, A., Thomas, A.: Comprehensive statistical study of 452 brca1 missense substitutions with classification of eight recurrent substitutions as neutral. J. Med. Genet. 43(4), 295–305 (2006)CrossRefGoogle Scholar
  21. 21.
    Ng, P.C., Henikoff, S.: Sift: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 31(13), 3812–3814 (2003)CrossRefGoogle Scholar
  22. 22.
    Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S., Sunyaev, S.R.: A method and server for predicting damaging missense mutations. Nature Methods 7(4), 248–249 (2010)CrossRefGoogle Scholar
  23. 23.
    Tavtigian, S.V., Oefner, P.J., Babikyan, D., Hartmann, A., Healey, S., Le Calvez-Kelm, F., Lesueur, F., Byrnes, G.B., Chuang, S.C.C., Forey, N., Feuchtinger, C., Gioia, L., Hall, J., Hashibe, M., Herte, B., McKay-Chopin, S., Thomas, A., Vallée, M.P., Voegele, C., Webb, P.M., Whiteman, D.C., Sangrajrang, S., Hopper, J.L., Southey, M.C., Andrulis, I.L., John, E.M., Chenevix-Trench, G.: Rare, evolutionarily unlikely missense substitutions in atm confer increased risk of breast cancer. Am. J. Hum. Genet. 85(4), 427–446 (2009)CrossRefGoogle Scholar
  24. 24.
    Pritchard, J.K., Cox, N.J.: The allelic architecture of human disease genes: common disease-common variant..or not? Hum. Mol. Genet. 11(20), 2417–2423 (2002)CrossRefGoogle Scholar
  25. 25.
    Pritchard, J.K.: Are rare variants responsible for susceptibility to complex diseases? The American Journal of Human Genetics 69(1), 124–137 (2001)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wright, S.: Evolution in mendelian populations. Genetics 16(2), 97–159 (1931)Google Scholar
  27. 27.
    Ewens, W.J.: Mathematical population genetics, 2nd edn. Springer, Heidelberg (2004)CrossRefMATHGoogle Scholar
  28. 28.
    Han, B., Kang, H.M., Eskin, E.: Rapid and accurate multiple testing correction and power estimation for millions of correlated markers. PLoS Genet. 5(4) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jae Hoon Sul
    • 1
  • Buhm Han
    • 1
  • Eleazar Eskin
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA

Personalised recommendations