Investigating statistical epistasis in complex disorders

James C. Turton, James Bullock, Christopher Medway, Hui Shi, Kristelle Brown, Olivia Belbin, Noor Kalsheker, Minerva M. Carrasquillo, Dennis W. Dickson, Neill R. Graff-Radford, Ronald C. Petersen, Steven G. Younkin, Kevin Morgan

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The missing heritability exhibited by late-onset Alzheimer's disease is unexplained and has been partly attributed to epistatic interaction. Methods available to explore this are often based on logistic regression and allow for determination of deviation from an expected outcome as a result of statistical epistasis. Three such methodologies including Synergy Factor and the PLINK modules,-epistasis and-fast-epistasis, were applied to study an epistatic interaction between interleukin-6 and interleukin-10. The models analyzed consisted of two synergistic interactions (SF ≈ 4.2 and 1.6) and two antagonistic interactions (SF ≈ 0.9 and 0.6). As with any statistical test, power to detect association is paramount; and most studies will be underpowered for the task. However, the availability of large sample sizes through genome-wide association studies make it feasible to examine approaches for determining epistatic interactions. This study documents the sample sizes needed to achieve a statistically significant outcome from each of the methods examined and discusses the limitations/advantages of the chosen approaches.

Original languageEnglish (US)
Pages (from-to)635-644
Number of pages10
JournalJournal of Alzheimer's Disease
Issue number4
StatePublished - 2011


  • Complex disorders
  • LOAD
  • epistasis
  • modeling
  • synergy factor

ASJC Scopus subject areas

  • General Neuroscience
  • Clinical Psychology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health


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