Production of a reference transcriptome and transcriptomic database (PocilloporaBase) for the cauliflower coral, Pocillopora damicornis

<p>Abstract</p> <p>Background</p> <p>Motivated by the precarious state of the world's coral reefs, there is currently a keen interest in coral transcriptomics. By identifying changes in coral gene expression that are triggered by particular environmental stressors, we can begin to characterize coral stress responses at the molecular level, which should lead to the development of more powerful diagnostic tools for evaluating the health of corals in the field. Furthermore, the identification of genetic variants that are more or less resilient in the face of particular stressors will help us to develop more reliable prognoses for particular coral populations. Toward this end, we performed deep mRNA sequencing of the cauliflower coral, <it>Pocillopora damicornis</it>, a geographically widespread Indo-Pacific species that exhibits a great diversity of colony forms and is able to thrive in habitats subject to a wide range of human impacts. Importantly, <it>P. damicornis </it>is particularly amenable to laboratory culture. We collected specimens from three geographically isolated Hawaiian populations subjected to qualitatively different levels of human impact. We isolated RNA from colony fragments ("nubbins") exposed to four environmental stressors (heat, desiccation, peroxide, and hypo-saline conditions) or control conditions. The RNA was pooled and sequenced using the 454 platform.</p> <p>Description</p> <p>Both the raw reads (n = 1, 116, 551) and the assembled contigs (n = 70, 786; mean length = 836 nucleotides) were deposited in a new publicly available relational database called PocilloporaBase <url>http://www.PocilloporaBase.org</url>. Using BLASTX, 47.2% of the contigs were found to match a sequence in the NCBI database at an E-value threshold of ≤.001; 93.6% of those contigs with matches in the NCBI database appear to be of metazoan origin and 2.3% bacterial origin, while most of the remaining 4.1% match to other eukaryotes, including algae and amoebae.</p> <p>Conclusions</p> <p><it>P. damicornis </it>now joins the handful of coral species for which extensive transcriptomic data are publicly available. Through PocilloporaBase <url>http://www.PocilloporaBase.org</url>, one can obtain assembled contigs and raw reads and query the data according to a wide assortment of attributes including taxonomic origin, PFAM motif, KEGG pathway, and GO annotation.</p

Characterization of the Core Elements of the NF-κB Signaling Pathway of the Sea Anemone Nematostella vectensis ▿ ‡

The sea anemone Nematostella vectensis is the leading developmental and genomic model for the phylum Cnidaria, which includes anemones, hydras, jellyfish, and corals. In insects and vertebrates, the NF-κB pathway is required for cellular and organismal responses to various stresses, including pathogens and chemicals, as well as for several developmental processes. Herein, we have characterized proteins that comprise the core NF-κB pathway in Nematostella, including homologs of NF-κB, IκB, Bcl-3, and IκB kinase (IKK). We show that N. vectensis NF-κB (Nv-NF-κB) can bind to κB sites and activate transcription of reporter genes containing multimeric κB sites or the Nv-IκB promoter. Both Nv-IκB and Nv-Bcl-3 interact with Nv-NF-κB and block its ability to activate reporter gene expression. Nv-IKK is most similar to human IKKɛ/TBK kinases and, in vitro, can phosphorylate Ser47 of Nv-IκB. Nv-NF-κB is expressed in a subset of ectodermal cells in juvenile and adult Nematostella anemones. A bioinformatic analysis suggests that homologs of many mammalian NF-κB target genes are targets for Nv-NF-κB, including genes involved in apoptosis and responses to organic compounds and endogenous stimuli. These results indicate that NF-κB pathway proteins in Nematostella are similar to their vertebrate homologs, and these results also provide a framework for understanding the evolutionary origins of NF-κB signaling

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase