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2008•American Journal of Medical Genetics Part B Neuropsychiatric GeneticsOpen access

Meta‐analysis of genome‐wide linkage scans of attention deficit hyperactivity disorder

Kaixin Zhou, Astrid Dempfle, Mauricio Arcos‐Burgos, Steven C. Bakker, Tobias Banaschewski, Joseph Biederman, Jan Buitelaar, F. Xavier Castellanos, Alysa E. Doyle, Richard P. Ebstein, Jenny M. Ekholm, Paola Forabosco, Barbara Franke, Christine Margarete Freitag, Susann Friedel, Michael Gill, Johannes Hebebrand, Anke Hinney, Christian Jacob, Klaus‐Peter Lesch, Sandra K. Loo, Francisco Javier Lopera, James T. McCracken, James J. McGough, Jobst Meyer, Eric O. Mick, Ana Lucía Miranda, Maximilian Muenke, Fernando Mulas, Stanley F. Nelson, Trang T. H. Nguyen, Robert D. Oades, Matthew N. Ogdie, Juan David Palacio, David A. Pineda, Andreas Reif, Tobias Renner, Herbert Roeyers, Marcel Romanos, Aribert Rothenberger, H. -J. Schäfer, Joseph A. Sergeant, Richard J. Sinke, Susan L. Smalley, Edmund J. S. Sonuga-Barke, Hans‐Christoph Steinhausen, Emma van der Meulen, Susanne Walitza, Andreas Warnke, Cathryn M. Lewis, Stephen V. Faraone, Philip J. Asherson

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Abstract

Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P(SR) = 0.00034, P(OR) = 0.04) was identified on chromosome 16 between 64 and 83 Mb. In addition there are nine other genomic regions from the GSMA showing nominal or suggestive evidence of linkage. All these linkage results may be informative and focus the search for novel ADHD susceptibility genes.

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What this paper is about

Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P(SR) = 0.00034, P(OR) = 0.04) was identified on chromosome 16 between 64 and 83 Mb. In addition there are nine other genomic regions from the GSMA showing nominal or suggestive evidence of linkage. All these linkage results may be informative and focus the search for novel ADHD susceptibility genes.

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Available abstract

Genetic contribution to the development of attention deficit hyperactivity disorder (ADHD) is well established. Seven independent genome-wide linkage scans have been performed to map loci that increase the risk for ADHD. Although significant linkage signals were identified in some of the studies, there has been limited replications between the various independent datasets. The current study gathered the results from all seven of the ADHD linkage scans and performed a Genome Scan Meta Analysis (GSMA) to identify the genomic region with most consistent linkage evidence across the studies. Genome-wide significant linkage (P(SR) = 0.00034, P(OR) = 0.04) was identified on chromosome 16 between 64 and 83 Mb. In addition there are nine other genomic regions from the GSMA showing nominal or suggestive evidence of linkage. All these linkage results may be informative and focus the search for novel ADHD susceptibility genes.

Key concepts: Linkage (software), Attention deficit hyperactivity disorder, Genome, Genetic linkage, Genetics, Psychology, Computational biology, Biology

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