We have conducted a genome screen of autism, by linkage analysis in an initial set of 90 multiplex sibships, with parents, containing 97 independent affected sib pairs (ASPs), with follow-up in 49 additional multiplex sibships, containing 50 ASPs. In total, 519 markers were genotyped, including 362 for the initial screen, and an additional 157 were genotyped in the follow-up. As a control, we also included in the analysis unaffected sibs, which provided 51 discordant sib pairs (DSPs) for the initial screen and 29 for the follow-up. In the initial phase of the work, we observed increased identity by descent (IBD) in the ASPs (sharing of 51.6%) compared with the DSPs (sharing of 50.8%). The excess sharing in the ASPs could not be attributed to the effect of a small number of loci but, rather, was due to the modest increase in the entire distribution of IBD. These results are most compatible with a model specifying a large number of loci (perhaps >/=15) and are less compatible with models specifying

Transcriptional promoters comprise one of many classes of eukaryotic transcriptional regulatory elements. Identification and characterization of these elements are vital to understanding the complex network of human gene regulation. Using full-length cDNA sequences to identify transcription start sites (TSS), we predicted more than 900 putative human transcriptional promoters in the ENCODE regions, representing a comprehensive sampling of promoters in 1% of the genome. We identified 387 fragments that function as promoters in at least one of 16 cell lines by measuring promoter activity in high-throughput transient transfection reporter assays. These positive functional results demonstrate widespread use of alternative promoters. We show a strong correlation between promoter activity and the corresponding endogenous RNA transcript levels, providing the first experimental quantitative estimate of promoter contribution to gene regulation. Finally, we identified functional regions within a randomly selected subset of 45 promoters using deletion analyses. These experiments showed that, on average, the sequence −300 to −50 bp of the TSS positively contributes to core promoter activity. Interestingly, putative negative elements were identified −1000 to −500 bp upstream of the TSS for 55% of genes tested. These data provide the largest and most comprehensive view of promoter function in the human genome.[Supplemental material is available online at www.genome.org.]The regulation of human gene expression is a critical, highly coordinated, and complex process. Gene regulation plays a crucial role in virtually every biological process from coordinating cell division to responding to extracellular stimuli and directing transcription during development (Pirkkala et al. 2001;Ahituv et al. 2004;Blais and Dynlacht 2004). While knowledge of regulation at the level of individual genes is progressing, global characterization of gene regulation currently represents one of the major challenges and fundamental goals for biomedical research. An initial step in achieving this goal is the comprehensive identification of transcriptional regulatory elements in the human genome. Towards this end, the ENCODE (Encyclopedia of DNA Elements) project began in 2004 as a collective effort of many laboratories to identify the functional elements in 1% of the human genome (The ENCODE Project Consortium 2004). In this paper, we describe our efforts to identify and study the transcriptional promoters in the ENCODE regions.Promoters are the best-characterized transcriptional regulatory sequences in complex genomes because of their predictable location immediately upstream of transcription start sites (TSS). They are often described as having two separate segments: core and extended promoter regions. The core promoter is generally within 50 bp of the TSS, where the preinitiation complex forms and the general transcription machinery assembles. The extended promoter can contain specific regulatory sequences that control spatial and temporal expression of...

To investigate the role of DNA methylation during human development, we developed Methyl-seq, a method that assays DNA methylation at more than 90,000 regions throughout the genome. Performing Methyl-seq on human embryonic stem cells (hESCs), their derivatives, and human tissues allowed us to identify several trends during hESC and in vivo liver differentiation. First, differentiation results in DNA methylation changes at a minimal number of assayed regions, both in vitro and in vivo (2%-11%). Second, in vitro hESC differentiation is characterized by both de novo methylation and demethylation, whereas in vivo fetal liver development is characterized predominantly by demethylation. Third, hESC differentiation is uniquely characterized by methylation changes specifically at H3K27me3-occupied regions, bivalent domains, and low density CpG promoters (LCPs), suggesting that these regions are more likely to be involved in transcriptional regulation during hESC differentiation. Although both H3K27me3-occupied domains and LCPs are also regions of high variability in DNA methylation state during human liver development, these regions become highly unmethylated, which is a distinct trend from that observed in hESCs. Taken together, our results indicate that hESC differentiation has a unique DNA methylation signature that may not be indicative of in vivo differentiation.

Enhancers are essential gene regulatory elements whose alteration can lead to morphological differences between species, developmental abnormalities, and human disease. Current strategies to identify enhancers focus primarily on noncoding sequences and tend to exclude protein coding sequences. Here, we analyzed 25 available ChIP-seq data sets that identify enhancers in an unbiased manner (H3K4me1, H3K27ac, and EP300) for peaks that overlap exons. We find that, on average, 7% of all ChIPseq peaks overlap coding exons (after excluding for peaks that overlap with first exons). By using mouse and zebrafish enhancer assays, we demonstrate that several of these exonic enhancer (eExons) candidates can function as enhancers of their neighboring genes and that the exonic sequence is necessary for enhancer activity. Using ChIP, 3C, and DNA FISH, we further show that one of these exonic limb enhancers, Dync1i1 exon 15, has active enhancer marks and physically interacts with Dlx5/6 promoter regions 900 kb away. In addition, its removal by chromosomal abnormalities in humans could cause split hand and foot malformation 1 (SHFM1), a disorder associated with DLX5/6. These results demonstrate that DNA sequences can have a dual function, operating as coding exons in one tissue and enhancers of nearby gene(s) in another tissue, suggesting that phenotypes resulting from coding mutations could be caused not only by protein alteration but also by disrupting the regulation of another gene.

The most widely used method for detecting genome-wide protein–DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and “spike-ins” comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols, and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated.

This study is the first comprehensive, integrated approach to examine grade-specific changes in gene expression along the entire neoplastic spectrum of cervical intraepithelial neoplasia (CIN) in the process of cervical carcinogenesis. This was accomplished by identifying gene expression signatures of disease progression using cDNA microarrays to analyze RNA from laser-captured microdissected epithelium and underlying stroma from normal cervix, graded CINs, cancer, and patientmatched normal cervical tissues. A separate set of samples were subsequently validated using a linear mixed model that is ideal to control for interpatient gene expression profile variation, such as age and race. These validated genes were ultimately used to propose a genomically based model of the early events in cervical neoplastic transformation. In this model, the CIN 1 transition coincides with a proproliferative/immunosuppression gene signature in the epithelium that probably represents the epithelial response to human papillomavirus infection. The CIN 2 transition coincides with a proangiogenic signature, suggesting a cooperative signaling interaction between stroma and tumor cells. Finally, the CIN 3 and squamous cell carcinoma antigen transition coincide with a proinvasive gene signature that may be a response to epithelial tumor cell overcrowding. This work strongly suggests that premalignant cells experience a series of microenvironmental stresses at the epithelium/ stroma cell interface that must be overcome to progress into a transformed phenotype and identifies the order of these events in vivo and their association with specific CIN transitions. [Cancer Res 2007;67(15):7113-23]

In obese patients evaluated before operation by PSG before bariatric surgery and managed accordingly, the severity of OSA, as assessed by the AHI, was not associated with the rate of perioperative complications. These results cannot determine whether unrecognized and untreated OSA increases risk.

Key messageNon-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance.AbstractIn the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.