Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

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40Understanding the genetics of gene regulation provides information on the cellular mechanisms 41 through which genetic variation influences complex traits. Expression quantitative trait loci, or 42 eQTLs, are enriched for polymorphisms that have been found to be associated with disease risk. 43 While most analyses of human data has focused on regulation of expression by nearby variants 44 (cis-eQTLs), distal or trans-eQTLs may have broader effects on the transcriptome and important 45 phenotypic consequences, necessitating a comprehensive study of the effects of genetic variants 46 on distal gene transcription levels. In this work, we identify trans-eQTLs in the Genotype Tissue 47 Expression (GTEx) project data 1 , consisting of 449 individuals with RNA-sequencing data 48 across 44 tissue types. We find 81 genes with a trans-eQTL in at least one tissue, and we 49 demonstrate that trans-eQTLs are more likely than cis-eQTLs to have effects specific to a single 50 tissue. We evaluate the genomic and functional properties of trans-eQTL variants, identifying 51 strong enrichment in enhancer elements and Piwi-interacting RNA clusters. Finally, we describe 52 three tissue-specific regulatory loci underlying relevant disease associations: 9q22 in thyroid that 53 has a role in thyroid cancer, 5q31 in skeletal muscle, and a previously reported master regulator 54 near KLF14 in adipose. These analyses provide a comprehensive characterization of trans-eQTLs 55 across human tissues, which contribute to an improved understanding of the tissue-specific 56 cellular mechanisms of regulatory genetic variation. 57 Introduction 58Variation in the human genome influences complex disease risk through changes at a cellular 59 level. Many disease-associated variants are also associated with gene expression levels through 60 which they mediate disease risk. The majority of expression quantitative trait locus (eQTL) 61 studies 1-6 thus far have focused on local-or cis-eQTLs because of the relative simplicity of 62 association mapping in human for both statistical and biological reasons 7, or 63 genetic variants that affect gene expression levels of distant target genes, have received much 64 less attention in comparison to cis-eQTLs, in part due to the considerable multiple hypotheses 65 testing burden 9 . Far fewer replicable, strong effect trans-eQTLs have been discovered in human 66 data as compared to cis-eQTLs, unlike in model organisms such as Saccharomyces cerevisiae or 67 Arabidopsis thaliana 7,10,11 . However, a handful of replicable trans-eQTLs have now been 68 identified in human tissues 3,12,13 . Additionally, recent work has suggested that trans-eQTLs 69 contribute substantially to the genetic regulation of complex diseases 12 , motivating a careful 70 examination of the role of trans-eQTLs across human tissues in disease etiology. 71Here, we identify trans-eQTLs in the Genotype-Tissue Expression (GTEx) v6 data, which 72 include 449 individuals with imputed genotypes and RNA-seq data across 44 tissues for a total 7...

A sonification is a rendering of audio in response to data, and is used in instances where visual representations of data are impossible, difficult, or unwanted. Designing sonifications often requires knowledge in multiple areas as well as an understanding of how the end users will use the system. This makes it an ideal candidate for end-user development where the user plays a role in the creation of the design. We present a model for sonification that utilizes user-specified examples and data to generate cross-domain mappings from data to sound. As a novel contribution we utilize soundscapes (acoustic scenes) for these user-selected examples to define a structure for the sonification. We demonstrate a proof of concept of our model using sound examples and discuss how we plan to build on this work in the future.

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