Linkage mapping of beta 2 EEG waves via non-parametric regression.
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.
Ghosh
Saurabh
S
Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid, Campus Box 8134, St. Louis, MO 63110-1093, USA. saurabh@silver.wustl.edu
Begleiter
Henri
H
Porjesz
Bernice
B
Chorlian
David B
DB
Edenberg
Howard J
HJ
Foroud
Tatiana
T
Goate
Alison
A
Reich
Theodore
T
D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002874:Chromosome Mapping / Q000379:methods*; D004569:Electroencephalography; D005260:Female; D015894:Genome, Human; D006801:Humans; D008297:Male; D018895:Microsatellite Repeats; D008875:Middle Aged; D008957:Models, Genetic; D012044:Regression Analysis; D018709:Statistics, Nonparametric