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

Am J Med Genet B Neuropsychiatr Genet. 2003 Apr 1;118B(1):66-71. doi: 10.1002/ajmg.b.10057.


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.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Child
  • Chromosome Mapping / methods*
  • Electroencephalography*
  • Female
  • Genome, Human
  • Humans
  • Male
  • Microsatellite Repeats
  • Middle Aged
  • Models, Genetic
  • Regression Analysis
  • Statistics, Nonparametric