Population-based analysis of Alzheimer's disease risk alleles implicates genetic interactions

Mark T.W. Ebbert, Perry G. Ridge, Andrew R. Wilson, Aaron R. Sharp, Matthew Bailey, Maria C. Norton, Joann T. Tschanz, Ronald G. Munger, Christopher D. Corcoran, John S.K. Kauwe

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Background Reported odds ratios and population attributable fractions (PAF) for late-onset Alzheimer's disease (LOAD) risk loci (BIN1, ABCA7, CR1, MS4A4E, CD2AP, PICALM, MS4A6A, CD33, and CLU) come from clinically ascertained samples. Little is known about the combined PAF for these LOAD risk alleles and the utility of these combined markers for case-control prediction. Here we evaluate these loci in a large population-based sample to estimate PAF and explore the effects of additive and nonadditive interactions on LOAD status prediction performance. Methods 2419 samples from the Cache County Memory Study were genotyped for APOE and nine LOAD risk loci from AlzGene.org. We used logistic regression and receiver operator characteristic analysis to assess the LOAD status prediction performance of these loci using additive and nonadditive models and compared odds ratios and PAFs between AlzGene.org and Cache County. Results Odds ratios were comparable between Cache County and AlzGene.org when identical single nucleotide polymorphisms were genotyped. PAFs from AlzGene.org ranged from 2.25% to 37%; those from Cache County ranged from.05% to 20%. Including non-APOE alleles significantly improved LOAD status prediction performance (area under the curve =.80) over APOE alone (area under the curve =.78) when not constrained to an additive relationship (p <.03). We identified potential allelic interactions (p values uncorrected): CD33-MS4A4E (synergy factor = 5.31; p <.003) and CLU-MS4A4E (synergy factor = 3.81; p <.016). Conclusions Although nonadditive interactions between loci significantly improve diagnostic ability, the improvement does not reach the desired sensitivity or specificity for clinical use. Nevertheless, these results suggest that understanding gene-gene interactions may be important in resolving Alzheimer's disease etiology.

Original languageEnglish
Pages (from-to)732-737
Number of pages6
JournalBiological Psychiatry
Issue number9
StatePublished - May 1 2014

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health (Grant Nos. R01AG11380, R01AG21136, R01AG31272, and R01AG042611), the Alzheimer’s Association (Grant No. MNIRG-11-205368), the Utah Science, Technology, and Research initiative, the Utah State University Agricultural Experiment Station, and the Brigham Young University Gerontology Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


  • Alzheimer's disease
  • epistasis
  • genetic interactions
  • odds ratio
  • population attributable fraction
  • risk

ASJC Scopus subject areas

  • Biological Psychiatry


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