Autistic Disorder and Chromosome 15q11–q13: Construction and Analysis of a BAC/PAC Contig

Autistic disorder (AD) is a neurodevelopmental disorder that affects approximately 2–10/10,000 individuals. Chromosome 15q11–q13 has been implicated in the genetic etiology of AD based on (1) cytogenetic abnormalities; (2) increased recombination frequency in this region in AD versus non-AD families; (3) suggested linkage with markers D15S156, D15S219, and D15S217; and (4) evidence for significant association with polymorphisms in the γ-aminobutyric acid receptor subunit B3 gene (GABRB3). To isolate the putative 15q11–q13 candidate AD gene, a genomic contig and physical map of the approximately 1.2-Mb region from the GABA receptor gene cluster to the OCA2 locus was generated. Twenty-one bacterial artificial chromosome (BAC) clones, 32 P1-derived artificial chromosome (PAC) clones, and 2 P1 clones have been isolated using the markers D15S540, GABRB3, GABRA5, GABRG3, D15S822, and D15S217, as well as 34 novel markers developed from the end sequences of BAC/PAC clones. In contrast to previous findings, the markers D15S822 and D15S975 have been localized within the GABRG3 gene, which we have shown to be approximately 250 kb in size. NotI and numerous EagI restriction enzyme cut sites were identified in this region. The BAC/PAC genomic contig can be utilized for the study of genomic structure and the identification and characterization of genes and their methylation status in this autism candidate gene region on human chromosome 15q11–q13.     

fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control

MOTIVATION: Most de novo motif identification methods optimize the motif model first and then separately test the statistical significance of the motif score. In the first stage, a motif abundance parameter needs to be specified or modeled. In the second stage, a Z-score or P-value is used as the test statistic. Error rates under multiple comparisons are not fully considered. Methodology: We propose a simple but novel approach, fdrMotif, that selects as many binding sites as possible while controlling a user-specified false discovery rate (FDR). Unlike existing iterative methods, fdrMotif combines model optimization [e.g. position weight matrix (PWM)] and significance testing at each step. By monitoring the proportion of binding sites selected in many sets of background sequences, fdrMotif controls the FDR in the original data. The model is then updated using an expectation (E)- and maximization (M)-like procedure. We propose a new normalization procedure in the E-step for updating the model. This process is repeated until either the model converges or the number of iterations exceeds a maximum. RESULTS: Simulation studies suggest that our normalization procedure assigns larger weights to the binding sites than do two other commonly used normalization procedures. Furthermore, fdrMotif requires only a user-specified FDR and an initial PWM. When tested on 542 high confidence experimental p53 binding loci, fdrMotif identified 569 p53 binding sites in 505 (93.2%) sequences. In comparison, MEME identified more binding sites but in fewer ChIP sequences than fdrMotif. When tested on 500 sets of simulated 'ChIP' sequences with embedded known p53 binding sites, fdrMotif, compared to MEME, has higher sensitivity with similar positive predictive value. Furthermore, fdrMotif is robust to noise: it selected nearly identical binding sites in data adulterated with 50% added background sequences and the unadulterated data. We suggest that fdrMotif represents an improvement over MEME. AVAILABILITY: C code can be found at: http://www.niehs.nih.gov/research/resources/software/fdrMotif/.