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Disease Markers
Volume 24 (2008), Issue 6, Pages 293-309

Candidate Gene Discovery Procedure after Follow-Up Confirmatory Analyses of Candidate Regions of Interests for Alzheimer’s Disease in the NIMH Sibling Dataset

Tesfaye M. Baye,1,2 Rodney T. Perry,2 Howard W. Wiener,2 Zuomin Chen,2 Lindy E. Harrell,3 and Rodney C. P. Go2

1Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
2Department of Epidemiology and International Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
3Department of Neurology, University of Alabama at Birmingham, Birmingham, AL 35294, USA

Received 23 July 2008; Accepted 23 July 2008

Copyright © 2008 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The objective of this research was to develop a procedure to identify candidate genes under linkage peaks confirmed in a follow-up of candidate regions of interests (CRIs) identified in our original genome scan in the NIMH Alzheimer’s diseases (AD) Initiative families (Blacker et al. [1]). There were six CRIs identified that met the threshold of multipoint lod score (MLS) of ≥ 2.0 from the original scan. The most significant peak (MLS = 7.7) was at 19q13, which was attributed to APOE. The remaining CRIs with ‘suggestive’ evidence for linkage were identified at 9q22, 6q27, 14q22, 11q25, and 3p26. We have followed up and narrowed the 9q22 CRI signal using simple tandem repeat (STR) markers (Perry et al. [2]). In this confirmatory project, we have followed up the 6q27, 14q22, 11q25, and 3p26 CRIs with a total of 24 additional flanking STRs, reducing the mean interval marker distance (MID) in each CRI, and substantially increase in the information content (IC). The linkage signals at 6q27, 14q22 and 11q25 remain ‘suggestive’, indicating that these CRIs are promising and worthy of detailed fine mapping and assessment of candidate genes associated with AD.

We have developed a bioinformatics approach for identifying candidate genes in these confirmed regions based on the Gene Ontology terms that are annotated and enriched among the systematic meta-analyzed genes, confirmed by at least three case-control samples, and cataloged in the “AlzGene database” as potential Alzheimer disease susceptibility genes (