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      Genetic control of RNA splicing and its distinct role in complex trait variation

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Most genetic variants identified from genome-wide association studies (GWAS) in humans are noncoding, indicating their role in gene regulation. Previous studies have shown considerable links of GWAS signals to expression quantitative trait loci (eQTLs) but the links to other genetic regulatory mechanisms, such as splicing QTLs (sQTLs), are underexplored. Here, we introduce an sQTL mapping method,  testing for heterogeneity between isoform-eQ TL effects (THISTLE), with improved power over competing methods. Applying THISTLE together with a complementary sQTL mapping strategy to brain transcriptomic ( n = 2,865) and genotype data, we identified 12,794 genes with cis-sQTLs at P < 5 × 10 −8, approximately 61% of which were distinct from eQTLs. Integrating the sQTL data into GWAS for 12 brain-related complex traits (including diseases), we identified 244 genes associated with the traits through cis-sQTLs, approximately 61% of which could not be discovered using the corresponding eQTL data. Our study demonstrates the distinct role of most sQTLs in the genetic regulation of transcription and complex trait variation.

          Abstract

          A powerful method for splicing quantitative trait loci (sQTL) mapping, THISTLE, is presented and applied to a collection of 2,865 brain samples. Integration with GWAS identifies 244 genes associated via cis-sQTLs, of which 61% were not identified using expression QTLs.

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          STAR: ultrafast universal RNA-seq aligner.

          Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)
          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

            Bo Li, Colin Dewey (2011)
            Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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              An Integrated Encyclopedia of DNA Elements in the Human Genome

              Iakes Ezkurdia (2016)
              Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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                Author and article information

                Contributors
                Jian Yang:
                ORCID: http://orcid.org/0000-0003-2001-2474
                jian.yang@westlake.edu.cn
                Journal
                Journal ID (nlm-ta): Nat Genet
                Journal ID (iso-abbrev): Nat Genet
                Title: Nature Genetics
                Publisher: Nature Publishing Group US (New York )
                ISSN (Print): 1061-4036
                ISSN (Electronic): 1546-1718
                Publication date (Electronic): 18 August 2022
                Publication date PMC-release: 18 August 2022
                Publication date (Print): 2022
                Volume: 54
                Issue: 9
                Pages: 1355-1363
                Affiliations
                [1 ]GRID grid.494629.4, ISNI 0000 0004 8008 9315, School of Life Sciences, , Westlake University, ; Hangzhou, China
                [2 ]GRID grid.494629.4, ISNI 0000 0004 8008 9315, Westlake Laboratory of Life Sciences and Biomedicine, ; Hangzhou, China
                [3 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Institute for Molecular Bioscience, , The University of Queensland, ; Brisbane, Queensland Australia
                [4 ]GRID grid.250407.4, ISNI 0000 0000 8900 8842, Neuroscience Research Australia, ; Sydney, New South Wales Australia
                [5 ]Clinical Genetics and Genomics, New South Wales Health Pathology Randwick, Sydney, New South Wales Australia
                Author information
                http://orcid.org/0000-0002-0128-7280
                http://orcid.org/0000-0001-8801-5220
                http://orcid.org/0000-0003-2001-2474
                Article
                Publisher ID: 1154
                DOI: 10.1038/s41588-022-01154-4
                PMC ID: 9470536
                PubMed ID: 35982161
                SO-VID: 5f57e2c5-1791-4f66-bb29-0038584fa269
                Copyright © © The Author(s) 2022
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 25 January 2021
                Date accepted : 8 July 2022
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                ScienceOpen disciplines: Genetics
                Keywords: genome-wide association studies,gene expression
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: genome-wide association studies, gene expression

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