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Investigating Statistical Epistasis in Complex Disorders

Article type: Research Article

Authors: Turton, James C.a; 1 | Bullock, Jamesa; 1 | Medway, Christophera | Shi, Huia | Brown, Kristellea | Belbin, Oliviaa | Kalsheker, Noora | Carrasquillo, Minerva M.b | Dickson, Dennis W.b | Graff-Radford, Neill R.b; c | Petersen, Ronald C.d; e | Younkin, Steven G.b | Morgan, Kevina; *

Affiliations: [a] Human Genetics, School of Molecular Medical Sciences, Queens Medical Centre, University of Nottingham, Nottingham, UK | [b] Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL, USA | [c] Department of Neurology, Mayo Clinic College of Medicine, Jacksonville, FL, USA | [d] Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN, USA | [e] Mayo Alzheimer Disease Research Centre, Mayo Clinic College of Medicine, Rochester, MN, USA

Correspondence: [*] Correspondence to: Professor Kevin Morgan, Professor of Human Genomics and Molecular Genetics, Institute of Genetics, School of Molecular Medical Sciences, A Floor, West Block, Room 1306, Queens Medical Centre, Nottingham NG7 2UH, UK. Tel.: 0115 8230724; E-mail: kevin.morgan@nottingham.ac.uk.

Note: [1] These authors contributed equally to this work.

Keywords: Complex disorders, epistasis, LOAD, modeling, PLINK, synergy factor

DOI: 10.3233/JAD-2011-110197

Journal: Journal of Alzheimer's Disease, vol. 25, no. 4, pp. 635-644, 2011

Accepted 4 March 2011
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Published: 5 August 2011
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Abstract

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.

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