Advertisement

Human Genetics

, Volume 133, Issue 2, pp 139–150 | Cite as

A unified GMDR method for detecting gene–gene interactions in family and unrelated samples with application to nicotine dependence

  • Guo-Bo Chen
  • Nianjun Liu
  • Yann C. Klimentidis
  • Xiaofeng Zhu
  • Degui Zhi
  • Xujing Wang
  • Xiang-Yang LouEmail author
Original Investigation

Abstract

Gene–gene and gene–environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene–gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring’s samples together as compared with individual analysis strategies and the Fisher’s combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.

Keywords

Nicotine Dependence Nuclear Family Unrelated Individual Multifactor Dimensionality Reduction Admix Population 
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.

Notes

Acknowledgments

This work was funded in part by the National Institutes of Health Grants DA025095, GM081488, GM077490, HG003054, and DK080100. Funding support for the Study of Addiction: Genetics and Environment (SAGE) was provided through the NIH Genes, Environment and Health Initiative (GEI) (U01 HG004422). The datasets used for the analyses described in this manuscript was obtained from the database of Genotypes and Phenotypes (dbGaP) found at http://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?study_id=phs000092.v1.p1 through dbGaP accession number phs000092.v1.p.

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

439_2013_1361_MOESM1_ESM.doc (1.1 mb)
Supplementary material 1 (DOC 1102 kb)

References

  1. Beuten J, Ma JZ, Payne TJ, Dupont RT, Quezada P, Huang W, Crews KM, Li MD (2005) Significant association of BDNF haplotypes in European–American male smokers but not in European–American female or African–American smokers. Am J Med Genet B Neuropsychiatr Genet 139:73–80CrossRefGoogle Scholar
  2. Bourgain C, Hoffjan S, Nicolae R, Newman D, Steiner L, Walker K, Reynolds R, Ober C, McPeek MS (2003) Novel case–control test in a founder population identifies P-selectin as an atopy-susceptibility locus. Am J Hum Genet 73:612–626PubMedCentralPubMedCrossRefGoogle Scholar
  3. Chen GB, Zhu J, Lou XY (2011) A faster pedigree-based generalized multifactor dimensionality reduction method for detecting gene–gene interactions. Stat Interface 4:295–304PubMedCentralPubMedCrossRefGoogle Scholar
  4. Choi Y, Wijsman EM, Weir BS (2009) Case–control association testing in the presence of unknown relationships. Genet Epidemiol 33:668–678PubMedCentralPubMedCrossRefGoogle Scholar
  5. Cordell HJ (2009) Detecting gene–gene interactions that underlie human diseases. Nat Rev Genet 10:392–404PubMedCentralPubMedCrossRefGoogle Scholar
  6. Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004PubMedCrossRefGoogle Scholar
  7. Feng Y, Niu T, Xing H, Xu X, Chen C, Peng S, Wang L, Laird N (2004) A common haplotype of the nicotine acetylcholine receptor alpha 4 subunit gene is associated with vulnerability to nicotine addiction in men. Am J Hum Genet 75:112–121PubMedCentralPubMedCrossRefGoogle Scholar
  8. Fisher AR (1954) Statistical methods for research workers, 12th edn. Hafner, New YorkGoogle Scholar
  9. Gravel S (2012) Population genetics models of local ancestry. Genetics 191:607–619PubMedCrossRefGoogle Scholar
  10. Li MD, Beuten J, Ma JZ, Payne TJ, Lou XY, Garcia V, Duenes AS, Crews KM, Elston RC (2005) Ethnic- and gender-specific association of the nicotinic acetylcholine receptor alpha4 subunit gene (CHRNA4) with nicotine dependence. Hum Mol Genet 14:1211–1219PubMedCrossRefGoogle Scholar
  11. Li MD, Lou XY, Chen G, Ma JZ, Elston RC (2008) Gene-gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence. Biol Psychiatry 64:951–957PubMedCentralPubMedCrossRefGoogle Scholar
  12. Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, Elston RC, Li MD (2007) A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet 80:1125–1137PubMedCentralPubMedCrossRefGoogle Scholar
  13. Lou XY, Chen GB, Yan L, Ma JZ, Mangold JE, Zhu J, Elston RC, Li MD (2008) A combinatorial approach to detecting gene–gene and gene–environment interactions in family studies. Am J Hum Genet 83:457–467PubMedCentralPubMedCrossRefGoogle Scholar
  14. Macgregor S (2008) Optimal two-stage testing for family-based genome-wide association studies. Am J Hum Genet 82:797–799 (author reply 799–800)PubMedCentralPubMedCrossRefGoogle Scholar
  15. Manolio T, Collins F, Cox N, Goldstein D, Hindorff L, Hunter D, McCarthy M, Ramos E, Cardon L, Chakravarti A, Cho J, Guttmacher A, Kong A, Kruglyak L, Mardis E, Rotimi C, Slatkin M, Valle D, Whittemore A, Boehnke M, Clark A, Eichler E, Gibson G, Haines J, Mackay T, McCarroll S, Visscher P (2009) Finding the missing heritability of complex diseases. Nature 461:747–753PubMedCentralPubMedCrossRefGoogle Scholar
  16. Martin ER, Ritchie MD, Hahn L, Kang S, Moore JH (2006) A novel method to identify gene–gene effects in nuclear families: the MDR-PDT. Genet Epidemiol 30:111–123PubMedCrossRefGoogle Scholar
  17. McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5:e1000686PubMedCentralPubMedCrossRefGoogle Scholar
  18. Motsinger AA, Ritchie MD (2006) The effect of reduction in cross-validation intervals on the performance of multifactor dimensionality reduction. Genet Epidemiol 30:546–555PubMedCrossRefGoogle Scholar
  19. Pirinen M, Donnelly P, Spencer CC (2012) Including known covariates can reduce power to detect genetic effects in case–control studies. Nat Genet 44:848–851PubMedCrossRefGoogle Scholar
  20. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909PubMedCrossRefGoogle Scholar
  21. Price AL, Zaitlen NA, Reich D, Patterson N (2010) New approaches to population stratification in genome-wide association studies. Nat Rev Genet 11:459–463PubMedCentralPubMedCrossRefGoogle Scholar
  22. Pritchard J, Stephens M, Rosenberg N, Donnelly P (2000) Association mapping in structured populations. Am J Hum Genet 67:170–181PubMedCentralPubMedCrossRefGoogle Scholar
  23. Rabinowitz D, Laird N (2000) A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum Hered 50:211–223PubMedCrossRefGoogle Scholar
  24. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69:138–147PubMedCentralPubMedCrossRefGoogle Scholar
  25. Skol AD, Scott LJ, Abecasis GR, Boehnke M (2007) Optimal designs for two-stage genome-wide association studies. Genet Epidemiol 31:776–788PubMedCrossRefGoogle Scholar
  26. Spielman RS, McGinnis RE, Ewens WJ (1993) Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 52:506–516PubMedCentralPubMedGoogle Scholar
  27. Wang X, Elston RC, Zhu X (2010) The meaning of interaction. Hum Hered 70:269–277PubMedCrossRefGoogle Scholar
  28. Wu C, DeWan A, Hoh J, Wang Z (2011) A comparison of association methods correcting for population stratification in case–control studies. Ann Hum Genet 75:418–427PubMedCentralPubMedCrossRefGoogle Scholar
  29. Zhu X, Li S, Cooper RS, Elston RC (2008) A unified association analysis approach for family and unrelated samples correcting for stratification. Am J Hum Genet 82:352–365PubMedCentralPubMedCrossRefGoogle Scholar
  30. Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci USA 109:1193–1198PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guo-Bo Chen
    • 1
  • Nianjun Liu
    • 1
  • Yann C. Klimentidis
    • 1
  • Xiaofeng Zhu
    • 2
  • Degui Zhi
    • 1
  • Xujing Wang
    • 3
  • Xiang-Yang Lou
    • 1
    Email author
  1. 1.Section on Statistical Genetics, Department of BiostatisticsUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.Department of Epidemiology and BiostatisticsCase Western Reserve UniversityClevelandUSA
  3. 3.The Bioinformatics and Systems Biology Core NHLBINational Institutes of HealthBethesdaUSA

Personalised recommendations