American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 118B:66–71 (2003) Linkage Mapping of Beta 2 EEG Waves via Non-Parametric Regression Saurabh Ghosh, 1 * Henri Begleiter, 2 Bernice Porjesz, 2 David B. Chorlian, 2 Howard J. Edenberg, 3,4 Tatiana Foroud, 3 Alison Goate, 1 and Theodore Reich 1,5 1 Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 2 Department of Psychiatry, SUNY HSC at Brooklyn, New York 3 Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana 4 Department of Biochemistry and Molecular Biology, Indiana University, Indianapolis, Indiana 5 Department of Genetics, Washington University School of Medicine, St. Louis, Missouri Parametric linkage methods for analyzing quantitative trait loci are sensitive to viola- tions 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 alco- holism 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 insignifi- cant. ß 2003 Wiley-Liss, Inc. KEY WORDS: quantitative trait; alcohol- ism; epistatic interaction INTRODUCTION The Collaborative Study on the Genetics of Alcoholism (COGA) is a multicenter research program to detect and map susceptibility genes for alcohol dependence and related phenotypes. Studies have revealed that many regions on the genome have significant linkage with alcohol related phenotypes [Reich et al., 1998; Foroud et al., 2000] like event-related potentials (ERP) [Begleiter et al., 1998; Williams et al., 1998; Almasy et al., 2001] and maximum number of drinks in a 24 h period [Saccone et al., 2000]. Electroencephalogram (EEG) is a term used to describe recordings of potential difference fluctuations in the brain that can be detected via electrodes attached to the scalp. EEG waves reflect the mean excitation of pools of neurons. Excitatory inputs at synapses generate electric currents that flow in closed loops within the recipient neuron towards its axon, across the cell membrane into the extracellular space and, in that space, back to the synapse. Inhibitory inputs generate loops moving in the opposite direction. The cell body summates all the inputs and, if the threshold is reached, fires an action potential. Electrodes placed on the scalp record these currents after they leave the cell. Beta 2 EEG waves are associated with an alert state of mind [Porjesz et al., 2002]. An increase in the frequency of these waves can be detected when the attention of a person is focussed. In this article, we analyze genome-wide scan data generated in the COGA project to identify regions which show evidence of linkage with Beta 2 EEG phenotypes. We also investigate the presence of epistatic interac- tions between regions exhibiting significant linkage. We modify the non-parametric regression method proposed by Ghosh and Majumder [2000] to perform our linkage scan. The advantage of the method is that it does not assume any probability distribution for the trait values Grant sponsor: NIH (from the National Institute on Alcohol Abuse and Alcoholism (NIAAA)); Grant numbers: U10AA08403, U10AA08401; Grant sponsor: NIH; Grant number: 1D43TW05811; Grant sponsor: K02 award grant (from NIAAA); Grant number: AA00285. *Correspondence to: Dr. Saurabh Ghosh, Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid, Campus Box 8134, St. Louis, MO 63110-1093. E-mail: saurabh@silver.wustl.edu Received 29 April 2002; Accepted 8 October 2002 DOI 10.1002/ajmg.b.10057 ß 2003 Wiley-Liss, Inc.