Linkage mapping of Beta 2 EEG waves via non-parametric regression

Saurabh Ghosh, Henri Begleiter, Berniee Porjesz, David B. Chorlian, Howard J. Edenberg, Tatiana Foroud, Alison Goate, Theodore Reich

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

Parametric linkage methods for analyzing quantitative trait loci are sensitive to violations in trait distributional assumptions. Non-parametric methods are relatively more robust. In this article, we modify the non-parametric regression procedure proposed by Ghosh and Majumder [2000: Am J Hum Genet 66:1046-1061] to map Beta 2 EEG waves using genome-wide data generated in the COGA project. Significant linkage findings are obtained on chromosomes 1, 4, 5, and 15 with findings at multiple regions on chromosomes 4 and 15. We analyze the data both with and without incorporating alcoholism as a covariate. We also test for epistatic interactions between regions of the genome exhibiting significant linkage with the EEG phenotypes and find evidence of epistatic interactions between a region each on chromosome 1 and chromosome 4 with one region on chromosome 15. While regressing out the effect of alcoholism does not affect the linkage findings, the epistatic interactions become statistically insignificant.

Original languageEnglish (US)
Pages (from-to)66-71
Number of pages6
JournalAmerican Journal of Medical Genetics - Neuropsychiatric Genetics
Volume118 B
Issue number1
StatePublished - Apr 1 2003

Keywords

  • Alcoholism
  • Epistatic interaction
  • Quantitative trait

ASJC Scopus subject areas

  • Genetics(clinical)
  • Neuropsychology and Physiological Psychology
  • Neuroscience(all)

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    Ghosh, S., Begleiter, H., Porjesz, B., Chorlian, D. B., Edenberg, H. J., Foroud, T., Goate, A., & Reich, T. (2003). Linkage mapping of Beta 2 EEG waves via non-parametric regression. American Journal of Medical Genetics - Neuropsychiatric Genetics, 118 B(1), 66-71.