Human Genetics

, Volume 129, Issue 2, pp 177–188

Uncovering hidden variance: pair-wise SNP analysis accounts for additional variance in nicotine dependence

Authors

    • Division of General Medical Sciences, Department of MedicineWashington University
    • Division of BiostatisticsWashington University
  • Nancy L. Saccone
    • Department of GeneticsWashington University
  • Jerry A. Stitzel
    • Department of Integrative PhysiologyUniversity of Colorado
  • Jen C. Wang
    • Department of PsychiatryWashington University
  • Joseph H. Steinbach
    • Department of Anesthesiology Basic Science ResearchWashington University
  • Alison M. Goate
    • Department of GeneticsWashington University
    • Department of PsychiatryWashington University
  • Tae-Hwi Schwantes-An
    • Department of GeneticsWashington University
  • Richard A. Grucza
    • Department of PsychiatryWashington University
  • Victoria L. Stevens
    • Department of Epidemiology and Surveillance ResearchAmerican Cancer Society
  • Laura J. Bierut
    • Department of PsychiatryWashington University
Original Investigation

DOI: 10.1007/s00439-010-0911-7

Cite this article as:
Culverhouse, R.C., Saccone, N.L., Stitzel, J.A. et al. Hum Genet (2011) 129: 177. doi:10.1007/s00439-010-0911-7

Abstract

Results from genome-wide association studies of complex traits account for only a modest proportion of the trait variance predicted to be due to genetics. We hypothesize that joint analysis of polymorphisms may account for more variance. We evaluated this hypothesis on a case–control smoking phenotype by examining pairs of nicotinic receptor single-nucleotide polymorphisms (SNPs) using the Restricted Partition Method (RPM) on data from the Collaborative Genetic Study of Nicotine Dependence (COGEND). We found evidence of joint effects that increase explained variance. Four signals identified in COGEND were testable in independent American Cancer Society (ACS) data, and three of the four signals replicated. Our results highlight two important lessons: joint effects that increase the explained variance are not limited to loci displaying substantial main effects, and joint effects need not display a significant interaction term in a logistic regression model. These results suggest that the joint analyses of variants may indeed account for part of the genetic variance left unexplained by single SNP analyses. Methodologies that limit analyses of joint effects to variants that demonstrate association in single SNP analyses, or require a significant interaction term, will likely miss important joint effects.

Supplementary material

439_2010_911_MOESM1_ESM.doc (128 kb)
Supplementary material 1 (DOC 128 kb)
439_2010_911_MOESM2_ESM.doc (89 kb)
Supplementary material 2 (DOC 89 kb)

Copyright information

© Springer-Verlag 2010