RNA sequencing (RNA-seq) is a powerful technology for studying human transcriptome variation. We introduce PAIRADISE (Paired Replicate Analysis of Allelic Differential Splicing Events), a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, we formulate ASAS detection using PAIRADISE as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates. PAIRADISE outperforms alternative statistical models in simulation studies. Applying PAIRADISE to replicate RNA-seq data of a single individual and to population-scale RNA-seq data across many individuals, we detect ASAS events associated with genome-wide association study (GWAS) signals of complex traits or diseases. Additionally, PAIRADISE ASAS analysis detects the effects of rare variants on alternative splicing. PAIRADISE provides a useful computational tool for elucidating the genetic variation and phenotypic association of alternative splicing in populations.

Despite regulatory approval of several immune-based treatments for cancer in the past decade, a number of barriers remain to be addressed in order to fully harness the therapeutic potential of the immune system and provide benefits for patients with cancer. As part of the Cancer Moonshot initiative, the Immuno-Oncology Translational Network (IOTN) was established to accelerate the translation of basic discoveries to improve immunotherapy outcomes across the spectrum of adult cancers and to develop immune-based approaches that prevent cancers before they occur. The IOTN currently consists of 32 academic institutions in the USA. By leveraging cutting-edge preclinical research in immunotherapy and immunoprevention, open data and resource sharing, and fostering highly collaborative team science across the immuno-oncology ecosystem, the IOTN is designed to accelerate the generation of novel mechanism-driven immune-based cancer prevention and therapies, and the development of safe and effective personalized immuno-oncology approaches.

We sought to define the landscape of alternative pre-mRNA splicing in prostate cancers and the relationship of exon choice to known cancer driver alterations. To do so, we compiled a metadataset composed of 876 RNA-sequencing (RNA-Seq) samples from five publicly available sources representing a range of prostate phenotypes from normal tissue to drug-resistant metastases. We subjected these samples to exon-level analysis with rMATS-turbo, purpose-built software designed for large-scale analyses of splicing, and identified 13,149 high-confidence cassette exon events with variable incorporation across samples. We then developed a computational framework, pathway enrichment-guided activity study of alternative splicing (PEGASAS), to correlate transcriptional signatures of 50 different cancer driver pathways with these alternative splicing events. We discovered that Myc signaling was correlated with incorporation of a set of 1,039 cassette exons enriched in genes encoding RNA binding proteins. Using a human prostate epithelial transformation assay, we confirmed the Myc regulation of 147 of these exons, many of which introduced frameshifts or encoded premature stop codons. Our results connect changes in alternative pre-mRNA splicing to oncogenic alterations common in prostate and many other cancers. We also establish a role for Myc in regulating RNA splicing by controlling the incorporation of nonsense-mediated decay-determinant exons in genes encoding RNA binding proteins.

Methods are proposed for selecting smoothing parameters of kernel classifiers in the presence of missing covariates. Here the missing covariates can appear in both the data and in the unclassified observation that has to be classified. The proposed methods are quite straightforward to implement. Exponential performance bounds will be derived for the resulting classifiers. Such bounds, in conjunction with the Borel–Cantelli lemma, provide various strong consistency results. Several numerical examples are presented to illustrate the effectiveness of the proposed procedures.

This paper improves upon an existing extreme precipitation monitoring system based on the Tropical Rainfall Measuring Mission (TRMM) daily product (3B42) using new statistical models. The proposed system utilizes a regional modeling approach, where data from similar locations are pooled to increase the quality of the resulting model parameter estimates to compensate for the short data record. The regional analysis is divided into two stages. First, the region defined by the TRMM measurements is partitioned into approximately 28,000 non-overlapping clusters using a recursive k-means clustering scheme. Next, a statistical model is used to characterize the extreme precipitation events occurring in each cluster. Instead of applying the block-maxima approach used in the existing system, where the Generalized Extreme Value probability distribution is fit to the annual precipitation maxima at each site separately, the present work adopts the peak-over-threshold method of classifying points as extreme if they exceed a pre-specified threshold. Theoretical considerations motivate using the Point Process framework for modeling extremes. The fitted parameters are used to estimate trends and to construct simple and intuitive average recurrence interval (ARI) maps which reveal how rare a particular precipitation event is. This information could be used by policy makers for disaster monitoring and prevention. The new methodology eliminates much of the noise that was produced by the existing models due to a short data record, producing more reasonable ARI maps when compared with NOAA's long-term Climate Prediction Center ground-based observations. Furthermore, the proposed methodology can be applied to other extreme climate records.

A number of results related to statistical classification on convex sets are presented. In particular, the focus is on the case where some of the covariates in the data and observation being classified can be missing. The form of the optimal classifier is derived when the class-conditional densities are uniform over convex regions. In practice, the underlying convex sets are often unknown and must be estimated with a set of data. In this case, the convex hull of a set of points is shown to be a consistent estimator of the underlying convex set. The problem of estimation is further complicated since the number of points in each convex hull is itself a random variable. The corresponding plug-in version of the optimal classifier is derived and shown to be Bayes consistent.

List of ASED RNA editing sites with associated SNPs. (XLSX 45 kb)

BACKGROUND:A-to-I RNA editing is an important step in RNA processing in which specific adenosines in some RNA molecules are post-transcriptionally modified to inosines. RNA editing has emerged as a widespread mechanism for generating transcriptome diversity. However, there remain significant knowledge gaps about the variation and function of RNA editing.RESULTS:In order to determine the influence of genetic variation on A-to-I RNA editing, we integrate genomic and transcriptomic data from 445 human lymphoblastoid cell lines by combining an RNA editing QTL (edQTL) analysis with an allele-specific RNA editing (ASED) analysis. We identify 1054 RNA editing events associated with cis genetic polymorphisms. Additionally, we find that a subset of these polymorphisms is linked to genome-wide association study signals of complex traits or diseases. Finally, compared to random cis polymorphisms, polymorphisms associated with RNA editing variation are located closer spatially to their respective editing sites and have a more pronounced impact on RNA secondary structure.CONCLUSIONS:Our study reveals widespread cis variation in RNA editing among genetically distinct individuals and sheds light on possible phenotypic consequences of such variation on complex traits and diseases.

Although stimulant dependence is highly heritable, few studies have examined genetic influences on methamphetamine dependence. We performed a candidate gene study of 52 SNPs and pretreatment methamphetamine use frequency among 263 methamphetamine dependent Hispanic and Non-Hispanic White participants of several methamphetamine outpatient clinical trials in Los Angeles. One SNP, rs7591784 was significantly associated with pretreatment methamphetamine use frequency following Bonferroni correction (p < 0.001) in males but not females. We then examined rs7591784 and methamphetamine urine drug screen results during 12 weeks of outpatient treatment among males with treatment outcome data available (N = 94) and found rs7591784 was significantly associated with methamphetamine use during treatment controlling for pretreatment methamphetamine use. rs7591784 is near CREB1 and in a linkage disequilibrium block with rs2952768, previously shown to influence CREB1 expression. The CREB signaling pathway is involved in gene expression changes related to chronic use of multiple drugs of abuse including methamphetamine and these results suggest that variability in CREB signaling may influence pretreatment frequency of methamphetamine use as well as outcomes of outpatient treatment. Medications targeting the CREB pathway, including phosphodiesterase inhibitors, warrant investigation as pharmacotherapies for methamphetamine use disorders.