Positional Cloning Utilizing Genomic DNA Microarrays: The Niemann–Pick Type C Gene as a Model System Dietrich A. Stephan,* ,1 Yidong Chen,* Yuan Jiang,* Lindsay Malechek,* Jessie Z. Gu,† Christiane M. Robbins,* Michael L. Bittner,* Jill A. Morris,‡ Eugene Carstea,‡ Paul S. Meltzer,* Karl Adler,§ Russell Garlick,§ Jeffrey M. Trent,* and Melissa A. Ashlock† *Cancer Genetics Branch and †Genetics and Molecular Biology Branch, National Human Genome Research Institute, and ‡Developmental and Metabolic Neurology Branch, National Institute of Neurologic Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892; and §NEN Life Science Products, Inc., P.O. Box 199151, Boston, Massachusetts 02119 Received January 31, 2000, and in revised form March 16, 2000 A major obstacle in positional cloning is identify- ing the specific mutated gene from within a large physical contig. Here we describe the application of DNA microarray technology to a defined genomic region (physical map) to identify: (i) exons without a priori sequence data and (ii) the disease gene based on differential gene expression in a recessive disorder. The feasibility was tested using resources from the positional cloning of the Neimann–Pick Type C (NP-C) disease gene, NPC1. To identify NPC1 exons and optimize the technology, an array was generated from genomic fragments of the 110-kb bacterial artificial chromosome, 108N2, which encodes NPC1. First, as a test case for blindly identifying exons, fluorescently labeled NPC1 cDNA identified 108N2 fragments that contained NPC1 ex- ons, many of which also contained intronic se- quences and could be used to determine part of the NPC1 genomic structure. Second, to demonstrate that the NPC1 disease gene could be identified based upon differential gene expression, subarrays of 108N2 fragments were hybridized with fluores- cently labeled cDNA probes generated from total RNA from hamster cell lines differentially express- ing NPC1. A probe derived from the NP-C cell line CT60 did not detect NPC1 exons or other genomic fragments from 108N2. In contrast, several NPC1 exons were detected by a probe generated from the non-NP-C cell line 911D5A13, which was derived from CT60, and expressed NPC1 as a consequence of stable transduction with a YAC that contains NPC1 and encompasses 108N2. Thus, the array technology identified NPC1 as a candidate gene based on a physical contig and differential NPC1 expression between NP-C and non-NP-C cells. This technique should facilitate gene identification when a physi- cal contig exists for a region of interest and muta- tions result in changes in the mRNA level of the disease gene or portions thereof. © 2000 Academic Press High-density polymorphic marker maps and the nearly complete yeast artificial chromosome (YAC) contig map of the human genome have facilitated assembly of physical maps of candidate regions. However, the subsequent step of gene identification has often been tedious and expensive, usually in- volving exon trapping (1–3), EST database searches (4), cDNA selection (5–7), and sequencing. As a con- sequence, only a small subset of linked loci have yielded etiological genes. In addition, the expecta- tion that the entire human genome will be se- quenced within a few years has incited technologic advances to more efficiently exploit these sequence data and the associated physical resources to iden- tify additional human disease genes. Here we de- scribe a technique to facilitate positional cloning (even in the presence of sequence data) that takes advantage of a physical map and the decrease in mutant transcript amounts in recessive disorders. The majority of recessive disorders result from a protein truncating mutation or loss of the entire protein, secondary to deletions, splice errors, or pre- mature termination codons (PTCs). Until recently, 1 To whom reprint requests should be addressed at current address: Research Center for Genetic Medicine, Children’s Na- tional Medical Center, 111 Michigan Avenue, NW, Washington, DC 20010. Fax: (202) 884-6014. E-mail: dstephan@nhgri.nih.gov. Molecular Genetics and Metabolism 70, 10 –18 (2000) doi:10.1006/mgme.2000.2989, available online at http://www.idealibrary.com on 10 1096-7192/00 $35.00 Copyright © 2000 by Academic Press All rights of reproduction in any form reserved.