Annotation-free quantification of RNA splicing using LeafCutter

Li, Yang; Knowles, David A.; Humphrey, Jack; Barbeira, Alvaro N.; Dickinson, Scott; Im, Hae Kyung; Pritchard, Jonathan K.

doi:10.1038/s41588-017-0004-9

Cited by 588 publications

(749 citation statements)

References 37 publications

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“…S10). We mapped splicing QTLs in cis with intron excision ratios from LeafCutter (11,13), and discovered 12,828 (66.5%) protein coding and 1,600 (21.5%) lincRNA genes (14,424 total) with a cis-sQTL (5% FDR, per tissue) in at least one tissue (cis-sVariants) ( fig. 2a, table S2).…”

Section: Qtl Discoverymentioning

confidence: 99%

The GTEx Consortium atlas of genetic regulatory effects across human tissues

Aguet¹,

Barbeira²,

Bonazzola³

et al. 2019

Preprint

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304

548

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The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

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Section: Qtl Discoverymentioning

confidence: 99%

The GTEx Consortium atlas of genetic regulatory effects across human tissues

Aguet¹,

Barbeira²,

Bonazzola³

et al. 2019

Preprint

Self Cite

304

548

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“…We compared the number of mashr models to the number of Elastic Net models from GTEx version 7 (g. S13). We generated analogous prediction models for splicing ratios, as computed by Leafcutter (45), applying the same model-building methodology to the data from the sQTL analysis.…”

Section: Prediction Modelsmentioning

confidence: 99%

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Barbeira

Bonazzola

Gamazon

et al. 2019

Preprint

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The resources generated by the GTEx consortium oer unprecedented opportunities to advance our understanding of the biology of human traits and diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genetic loci discovered by genome-wide association studies (GWAS). Across a broad set of complex traits and diseases, we nd widespread dosedependent eects of RNA expression and splicing, with higher impact on molecular phenotypes translating into higher impact downstream. Using colocalization and association approaches that take into account the observed allelic heterogeneity, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotypephenotype link. Harmonized GWAS and QTL datasetsThe nal GTEx data release (v8) includes 54 primary human tissues, 49 of which included at least 65 samples and were used for cis-QTL mapping ( Fig. 1) (9). This phase increases the number of available tissues relative to previous GTEx publications (v6p; 44 tissues) (8) and doubles the sample size from 7,051 RNA-Seq samples from 449 individuals to 15,253 samples from 838 individuals, now all with whole genome sequencing data as opposed to genotype imputation in v6p. Furthermore, the v8 core data resources now include splicing QTLs (9), allowing parallel analysis of both expression and splicing variation underlying complex traits. Using these resources, we investigated the contribution of expression and splicing QTLs in cis (eQTL and sQTL, respectively) to complex trait variance and etiology.We retained 87 GWAS datasets representing 74 distinct complex traits for further analyses (table S1 and g. S1) after stringent quality control (g. S2; (21)) and data harmonization(g. S3, g. S4). 6We found a signicantly higher correlation in mediating eect between primary and secondary eQTLs for a given gene compared to a null distribution obtained by sampling GWAS eect sizes from a bivariate normal distribution to account for the small observed LD between primary and secondary eQTLs ( Fig. 2D-E) while keeping the observed eQTL eect sizes (p < 1 × 10 −30 ).Interestingly, the correlation between primary and secondary eQTLs for non-colocalized genes (rcp < 0.01), which were used as controls (9, 21), was signicantly higher than this more accurate null, indicating that even eQTLs with very low colocalization probability include many genes that are likely causal. Given this concordance between multiple independent eQTLs, it is clear that with widespread allelic heterogeneity detected with currently available sample sizes, methods that assume single causal variants are highly limited. The approaches described here enable insights into how multiple regulatory effects converge to mediate the same trait association. 7 * alphabetic order

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“…Even though the transcriptomic complexity is now widely accepted, consideration of proteomic complexity is usually restricted to one protein (and its splicing-derived isoforms) per gene. Protein complexity can arise from multiple sources, such as RNA splicing and editing, posttranslational modifications, alternative initiation (internal ribosome entry site), stop codon read-through, or non-AUG initiation (Dunn et al 2013;Venne et al 2014;Ingolia 2016;Nishikura 2016;Blencowe 2017;Li et al 2018). Notwithstanding, this review will focus on the proteomic complexity resulting from proteins encoded in alternative ORFs.…”

mentioning

confidence: 99%

Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship

et al. 2018

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Technological advances promise unprecedented opportunities for whole exome sequencing and proteomic analyses of populations. Currently, data from genome and exome sequencing or proteomic studies are searched against reference genome annotations. This provides the foundation for research and clinical screening for genetic causes of pathologies. However, current genome annotations substantially underestimate the proteomic information encoded within a gene. Numerous studies have now demonstrated the expression and function of alternative (mainly small, sometimes overlapping) ORFs within mature gene transcripts. This has important consequences for the correlation of phenotypes and genotypes. Most alternative ORFs are not yet annotated because of a lack of evidence, and this absence from databases precludes their detection by standard proteomic methods, such as mass spectrometry. Here, we demonstrate how current approaches tend to overlook alternative ORFs, hindering the discovery of new genetic drivers and fundamental research. We discuss available tools and techniques to improve identification of proteins from alternative ORFs and finally suggest a novel annotation system to permit a more complete representation of the transcriptomic and proteomic information contained within a gene. Given the crucial challenge of distinguishing functional ORFs from random ones, the suggested pipeline emphasizes both experimental data and conservation signatures. The addition of alternative ORFs in databases will render identification less serendipitous and advance the pace of research and genomic knowledge. This review highlights the urgent medical and research need to incorporate alternative ORFs in current genome annotations and thus permit their inclusion in hypotheses and models, which relate phenotypes and genotypes.

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Annotation-free quantification of RNA splicing using LeafCutter

Cited by 588 publications

References 37 publications

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The GTEx Consortium atlas of genetic regulatory effects across human tissues

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship

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