RNA splicing is a primary link between genetic variation and disease

By Yang I Li, Bryce van de Geijn, Anil Raj, David A. Knowles, Allegra Petti, David Golan, Yoav Gilad, J. K. Pritchard. 2016

634 citations recorded by the source corpus.

RNA splicing links genetics to disease Many genetic variants associated with disease have no apparent effect on any specific protein coding sequence. Li et al. systematically analyzed the effects of DNA variants on the main steps of gene regulation, from the chromatin state through protein function. One-third of expression quantitative train loci (QTLs) are mediated through transcriptional processes, not chromatin. Splice QTLs and expression QTLs are about comparable in their complex disease risk. Posttranscriptional mechanisms therefore play a large role in translating genotype to phenotype. Science, this issue p. 600 Phenotype is most affected by genetic variants that influence gene expression and transcript splicing. Noncoding variants play a central role in the genetics of complex traits, but we still lack a full understanding of the molecular pathways through which they act. We quantified the contribution of cis-acting genetic effects at all major stages of gene regulation from chromatin to proteins, in Yoruba lymphoblastoid cell lines (LCLs). About ~65% of expression quantitative trait loci (eQTLs) have primary effects on chromatin, whereas the remaining eQTLs are enriched in transcribed regions. Using a novel method, we also detected 2893 splicing QTLs, most of which have little or no effect on gene-level expression. These splicing QTLs are major contributors to complex traits, roughly on a par with variants that affect gene expression levels. Our study provides a comprehensive view of the mechanisms linking genetic variation to variation in human gene regulation.

Subfields: Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “A recent study found that quantitative trait loci that affect splicing in lymphoblastoid cell lines are enriched within risk loci for schizophrenia, multiple sclerosis, and other immune diseases, implicating mis-splicing as a more widespread feature of human pathologies than previously thought [150]”
Annotation-free quantification of RNA splicing using LeafCutter (2017, positive): “Using an early version of LeafCutter, we found that alternative splicing is an important mechanism through which genetic variants contribute to disease risk 17 .”
An expanded view of complex traits: from polygenic to omnigenic (2017, background): “A second surprise was that, in contrast to Mendelian diseases—which are largely caused by protein-coding changes (Botstein and Risch, 2003)—complex traits are mainly driven by noncoding variants that presumably affect gene regulation (Pickrell, 2014; Welter et al., 2014; Li et al., 2016).”
The GTEx Consortium atlas of genetic regulatory effects across human tissues (2019, methodology): “Similarly, we highlight cis-eQTL and cis-sQTL effects on the same gene, typically driven by distinct causal variants (4, 35).”
Sequential regulatory activity prediction across chromosomes with convolutional neural networks (2017, background): “23 Discussion Transcriptional regulation is the primary driver of gene expression specificity across cell types and states (Waszak et al. 2015; Grubert et al. 2015; Battle et al. 2015; Li et al. 2016).”
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells (2016, background): “Alternative splicing regulates lineage commitment of human blood progenitors into mature blood cells (Chen et al., 2014) and contributes to disease as shown in lymphoblastoid cell lines (Li et al., 2016).”
The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, background): “These findings support the idea that large GWAS are well powered to identify splice variants with large phenotypic effects (Li et al., 2016), and these splice variants represent a currently under-appreciated mechanism of trait regulation in GWAS loci.”
Improving genetic diagnosis in Mendelian disease with transcriptome sequencing (2016, positive): “Such approaches have already proven useful for elucidating mechanisms of cancer and common disease (16, 17) but are not currently systematically applied to rare disease diagnosis.”
Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism (2016, neutral): “4h), consistent with the observation that splicing alterations are related to common disease risk independently of gene expression changes 16 .”