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HUMAN GENETICS

RNA splicing is a primary linkbetween genetic variation and diseaseYang I. Li,1 Bryce van de Geijn,2 Anil Raj,1 David A. Knowles,3,4 Allegra A. Petti,5

David Golan,1 Yoav Gilad,2* Jonathan K. Pritchard1,6,7*

Noncoding variants play a central role in the genetics of complex traits, but we stilllack a full understanding of the molecular pathways through which they act. We quantifiedthe contribution of cis-acting genetic effects at all major stages of gene regulationfrom chromatin to proteins, in Yoruba lymphoblastoid cell lines (LCLs). About ~65%of expression quantitative trait loci (eQTLs) have primary effects on chromatin, whereasthe remaining eQTLs are enriched in transcribed regions. Using a novel method, we alsodetected 2893 splicing QTLs, most of which have little or no effect on gene-levelexpression. These splicing QTLs are major contributors to complex traits, roughly on a parwith variants that affect gene expression levels. Our study provides a comprehensive viewof the mechanisms linking genetic variation to variation in human gene regulation.

Expression quantitative trait loci (eQTLs)are highly enriched among the risk loci forcomplex diseases (1, 2), suggesting that riskvariants often act by affecting aspects ofgene regulation. Previous attempts to elu-

cidate the mechanisms underlying eQTLs re-vealed that a large fraction of eQTLs are due tosingle-nucleotide polymorphisms (SNPs) thataffect transcription factor binding or other as-pects of chromatin function at enhancers orpromoters (3–9). Moreover, SNPs that lie withinactive chromatin regions in relevant cell typesare highly enriched among the signals obtainedthroughgenome-wideassociation studies (GWASs)(10, 11). In a few cases, specific links were iden-tified between genetic variation, variation in chro-matin, gene expression differences, and disease

risk (12). Genetic variation might also affectgene regulation and function through pre-mRNAsplicing by affecting either the expression levelsor amino acid sequences of the resulting proteins(13). There are reports of splicing variants con-tributing to complex traits (14); however, a recentcomprehensive study of splicing found no enrich-ment of predicted splicing variants among sig-nals identified in GWASs (15).We aimed to compile a detailed accounting of

the effects of genetic variants on gene regulationfrom chromatin to proteins. To do this, we an-alyzed molecular data for eight regulatory traitsmeasured in lymphoblastoid cell lines (LCLs) de-rived from Yoruba individuals (Fig. 1A), for whichgenome sequence data are available (16). Alto-gether, data are available for all eight molecular

phenotypes in 32 individuals and at least sixphenotypes in 68 individuals (table S8).Seven of the eight main molecular measure-

ments analyzed in this study were characterizedpreviously (4–6, 17–19). We additionally mea-sured transcription rates in 65 individuals, using4sU-labeled RNA sequencing (4sU-seq). The 4sUassay uses a pulse ofmodified uridine to label theaccumulation of new transcripts during a fixedtime interval, in order tomeasure the rate of RNAsynthesis (16). To confirm that the 4sU-seq datado indeedmeasure transcription rates, we exam-ined them in the context of mRNA decay mea-surements for the same LCLs (Fig. 1B). Steady-statemRNA levels should reflect a balance betweentranscription and decay. We found that the ratioof new transcript levels (based on 4sU data) tosteady-state RNA levels is negatively correlatedwith RNA decay estimates (P = 10−167, c2 good-ness of fit).We also computed correlations between read

counts across five molecular measurements (Fig.1C). As expected, 4sU and RNA-seq data are highlycorrelated [Spearman correlation (rho) > 0.90].Histone H3 lysine 27 acetylation (H3K27ac) readcounts at transcription start sites (TSSs) are morestrongly correlated with 4sU-seq than with RNA-seq data (P = 4 × 10−7, permutation). This isconsistent with the expectation that the effect of

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1Department of Genetics, Stanford University, Stanford, CA,USA. 2Department of Human Genetics, University of Chicago,Chicago, IL, USA. 3Department of Computer Science,Stanford University, Stanford, CA, USA. 4Department ofRadiology, Stanford University, Stanford, CA, USA. 5GenomeInstitute, Washington University in St. Louis, St. Louis, MO,USA. 6Department of Biology, Stanford University, Stanford,CA, USA. 7Howard Hughes Medical Institute, StanfordUniversity, Stanford, CA, USA.*Corresponding author. Email: [email protected] (Y.G.);[email protected] (J.K.P.)

Fig. 1. Systematicmapping of genetic variation that affects the gene-regulatory cascade. (A) QTLs mapped for eight cellular phenotypes in LCLs. For 4sU,30m and 60m refer to differentmeasurement time points. For RNA-seq,G andP refer to data from two studies (6, 17).TF, transcription factor. (B) Steady-state RNAlevels reflect abalancebetween transcriptionanddecay.NormalizedmRNAdecay rates (xaxis) areplottedagainst the ratioof newmRNAtosteady-statemRNA(yaxis).Each data point is a gene. (C) A correlationmatrix of seven data sets reflects the expected order of steps in gene regulation. Each entry in thematrix shows the correlationacross genes betweenmeasurements of a pair of samples and/or data types.The plot shows 10 different random samples for each data type.

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Fig. 2. Percolation of genetic effects through the gene regulatory cas-cade. (A) Correlation of effect sizes across different measurements fromeQTLs identified in the GEUVADIS YRI sample (6). Txn rate, transcription rate.(B) QTLsharingacross the regulatorycascade. Eachpanel shows the estimatedfraction of QTLs identified at one stage that are preserved at the next stage ofregulation.The four bars in each panel correspond to the P-value threshold forascertaining QTLs in each assay, using the linear regression t statistics. Barsrepresent 80%confidence intervals on π1, the fraction of true positives (16).Theenhancer→TSSpanel considers the effect of H3K27acQTLs on the nearest TSS.

(C) The fraction of expressionQTLs that also affect chromatin-level phenotypes,as estimated by twomodels, and formatched control SNPs. About 35% of geneeQTLs do not appear to affect chromatin traits. QTLs for H3K4me1 andH3K4me3 are from (8). (D) Functional context of eQTL SNPs that are notassociated with chromatin changes (“unexplained”) versus those eQTLsthat are also chromatin QTLs. 5′ untranslated regions were excluded fromthe “gene exons” annotation. Five annotations with bootstrap P > 0.05 arenot shown. (E) Summary of the effects of regulatory QTLs and of their sharingthrough the regulatory cascade.


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promoter function on transcription rate is moredirect than on steady-state mRNA levels. Over-all, the correlation structure reflects the fact thatpromoter activity, transcription rates, mRNA ex-pression levels, translation levels, and proteinexpression levels are regulated in a sequentialordered cascade.We next performed mapping of QTLs across

the eight molecular phenotypes, using a uniformQTL mapping pipeline [Fig. 2 and table S1 (16)].We found that effect sizes are correlated across

all phenotypes, with a minimum correlation of0.23 between H3K27ac marks and protein lev-els (Fig. 2A). Effect sizes for the RNA-relatedphenotypes are all highly correlated (minimumr2 = 0.87). Effect sizes at the protein level aresmaller on average than for translation (P= 0.002,Mann-Whitney U test), which is consistent witha previous report of potential protein expressionbuffering (fig. S4) (19).The high correlations of QTL effect sizes across

regulatory stages suggest high proportions of

shared QTLs. To quantify this, we considered theset of significant QTLs at each phenotype andestimated the sharing of QTLs at downstreamstages of regulation (16) (Fig. 2C and fig. S2). Start-ing with enhancers, we observed that ~25% ofH3K27acQTLs [histoneacetylationQTLs (haQTLs)]affect the expression levels of their nearest genesand ~50% affect the expression of any gene with-in 500 kb (fig. S2). In contrast, the majority ofpromoter haQTLs also affect expression (>65%)(Fig. 2B).


Fig. 3. Properties of sQTLs. Most sQTLs act independently from eQTLs:Positional distributions of (A) eQTLs and (B) sQTLs at 5% FDR are consistentwith our mechanistic understanding of gene transcription and splicing. (C) Thedistance between the best eQTL and best sQTL for genes with both types ofQTL is typically large, suggesting distinct causal variants. (D) A hierarchicalmodel reveals distinct genomic features that are most relevant for eQTLsand sQTLs, respectively. (E) QTLs for CTCF binding, and H3K27ac levels aremore likely to be sQTLs thanmatched SNPswithin CTCFandH3K27ac ChIP-seq peaks, respectively. (F) Example of an sQTL (rs6269) that is also a QTL

for CTCF, DNaseI sensitivity, and DNA methylation. The allele that is as-sociated with increased CTCF occupancy is also associated with increaseduse of an alternative upstream splice site for an exon of the catechol-O-methyltransferase gene,COMT, which is consistent with the model that PolIIpausing at CTCF binding sites can promote upstream exon inclusion (21).COMT, which regulates dopamine, has possible roles in neuropsychiatricconditions (25). In Europeans, the sQTL is in nearly complete linkage dis-equilibrium with a missense variant, rs4680, which has been the main focusof attention to date.


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When we used the same approach starting attranscription, we found that over 85% of QTLswere shared from one regulatory stage to thenext, from 4sU to protein (Fig. 2B and fig. S2E).Using a Bayesian model to quantify QTL sharingamong traits, we estimated that 73% of QTLsthat affect transcription rates also affect proteinexpression (16) (fig. S3). These observations areconsistent with a general percolation of geneticeffects from transcription through the regulatorycascade; however, it remains possible that someQTLs might affect multiple aspects of posttrans-criptional regulation independently.We next examined how often eQTLs can be

explained by inter-individual variation in chroma-tin properties (Fig. 2C), by estimating the fractionof eQTLs that are also QTLs for a chromatin-leveltrait, such as DNaseI-sensitive sites, DNA meth-ylation, H3K27ac, and H3K4me1 and H3K4me3QTLs as determined in (8). We confirmed thatthe majority of eQTLs are also nearby chromatinQTLs (65 versus 20% for matched control SNPs),which is consistent with previous reports (4).Thus ~35% of eQTLs are not associated with

known chromatin QTLs. To investigate whetherthese represent a distinct functional class, weaskedwhether the unexplained eQTLs are biasedtoward particular genomic regions or function-al annotations (Fig. 2D). Indeed, compared toeQTLs that also affect chromatin, the chromatin-independent eQTLs were enriched within genebodies (exons: P = 5.4 × 10−8; and introns: P =0.006; Mann-Whitney U test). Further, there isparticular enrichment in regions associated with

transcriptional elongationmarks (P = 3.0 × 10−21;Mann-Whitney U test). Hence, many of the un-explained eQTLs may affect transcriptional orposttranscriptional processes, alongside the morewidely studied effects on chromatin function.Together, these analyses help us provide an over-view of the genetic effects on diverse aspects ofgene regulation (Fig. 2E).We next turned to the effects of genetic var-

iation on pre-mRNA splicing (Fig. 3).Most studiesof splicing QTLs (sQTLs) have measured eitherexpression levels of individual exons or of tran-script isoforms (6, 14, 17,). Because both of theseare difficult to estimate accurately from short-read data, we developed a new method to detectsplicing variation, LeafCutter (20), which focuseson reads that span splice junctions (16). Usingthis approach, we identified 2893 sQTLs in 2313genes at 10% false discovery rate (FDR) (16).We verified that sQTLs and eQTLs tend to be

independent. Unlike eQTLs, which are enrichednear TSSs, the sQTLs are enriched within genebodies and in particular within the introns theyregulate (Fig. 3, A and B). Moreover, most of thesQTLs are not associated with gene expressionlevels (74% have P values >10−2, t test; fig. S9).We examined 275 genes associated with both

an sQTL and an eQTL at 10%FDR. The lead eQTLand sQTL SNP is the same for only 14 of thesegenes. In most cases, the lead SNPs are morethan 10 kb apart, suggesting that the majority ofthese effects are independent (Fig. 3C and fig.S12). Although most sQTLs do not affect overallexpression, themajority (89%) affect the predicted

coding sequences, thereby potentially affectingprotein function (fig. S10).We used a hierarchical model to identify which

genomic annotations aremost relevant to splicingversus gene expression (16). As expected, geneticvariants located in active promoters, strong en-hancers, andweak promoters weremost likely toaffect gene expression. In contrast, splicing wasmost strongly affected by variants near splicesites and by synonymous and missense variants(Fig. 3D).Although active chromatin does not seem to

be the primary driver of sQTLs, DNA-bindingproteins such as the CTCF transcription factormay affect transcription speed and thereby affectsplicing, which occurs cotranscriptionally (21).sQTL SNPs show a modest enrichment for as-sociation with chromatin QTLs (17.5%) com-pared to matched control SNPs (13.3%). Overall,we identified 171 sQTLs associated with two ormore chromatin-level phenotypes (table S7; ex-ample in Fig. 3F). Specifically considering QTLsfor CTCF (16) and for H3K27ac, we found thatchromatin QTLs aremore likely to affect splicingthanmatched control SNPs (P = 2 × 10−5 and P =1 × 10−34, respectively, likelihood-ratio test; Fig. 3E).Variations in CTCF binding and H3K27ac levelshave been shown to correlate with differences insplicing (21, 22). Our findings, however, providedirect evidence that genetic variation can affectsplicing by altering chromatin-level traits.Our results indicate that splicing is a primary

target of common genetic variation, which, inmost cases, has direct effects on protein sequences.

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Fig. 4. Contribution of regulatory variants to complextraits. (A) Annotations identified with significant enrichmentfor GWAS traits by fgwas (23). (B) Annotations identifiedwithsignificant enrichment for GWAS traits by polyTest (16).(C) Quantile-quantile (Q-Q) plot for multiple sclerosis GWASsuggests that splicing plays an important role in the etiologyof multiple sclerosis. (D) Model of the regulatory mechanismsthrough which common variants affect complex traits.


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Although studies have implicated variants in ac-tive chromatin regions and eQTLs as contrib-uting significantly to complex traits (2, 8, 10, 23),the importance of splicing remains unclear. Wetherefore wondered what role common sQTLsmight play in complex diseases.Owing to the extensive sharing of QTLs across

cell types (2, 24), we reasoned thatQTLs identifiedin LCLs should be informative about the relativecontribution of different regulatory mechanismsto complex traits, in particular to immune-relateddiseases (8). We thus compiled genome-widesummary statistics for rheumatoid arthritis,mul-tiple sclerosis, Alzheimer’s disease, schizophrenia,height, and bodymass index (16). Using two testswith different underlying statistical models, wesearched for functional annotations that are as-sociated with GWAS signals (16, 23).As expected, eQTLs and haQTLs are predicted

to contribute to rheumatoid arthritis, multiplesclerosis, and height according to one or bothmethods (Fig. 4). Consistent with the notion thatdisease SNPs in histone modification peaks aremediated through the SNPs’ effect on chromatin,haQTLs are more enriched in risk loci than arevariants that lie within H3K27ac peaks overall(Fig. 4C and fig. S14).sQTLs appear to have effects of similar or even

larger magnitude than eQTLs. For instance, thereis an enrichment of sQTLs with low P values inthe multiple sclerosis GWASs, even when com-pared to eQTLs (Fig. 4C and fig. S15). These en-richments are robust to the eQTL and sQTLdetection cutoffs, suggesting that they are notsimply due to the power of detection (fig. S16).We also found similar patterns when we com-pared the effect of sQTLs on multiple sclerosisto the effects of eQTLs identified in three puri-fied immune cell types (fig. S17).In conclusion, three main pathways mediate

the impact of genetic variation on gene regulationwith phenotypic and pathogenic consequences.Of these, our work uncovers an unexpectedlyimportant role of RNA splicing in modulatingphenotypic traits (Fig. 4D). These findings in-dicate that RNA splicing should be a focal pointin future work on connecting genetic variation tocomplex disease.

REFERENCES AND NOTES

1. D. L. Nicolae et al., PLOS Genet. 6, e1000888 (2010).2. The GTEx Consortium, Science 348, 648–660 (2015).3. M. Kasowski et al., Science 328, 232–235 (2010).4. J. F. Degner et al., Nature 482, 390–394 (2012).5. G. McVicker et al., Science 342, 747–749 (2013).6. T. Lappalainen et al., Nature 501, 506–511 (2013).7. R. C. del Rosario et al., Nat. Methods 12, 458–464

(2015).8. F. Grubert et al., Cell 162, 1051–1065 (2015).9. S. M. Waszak et al., Cell 162, 1039–1050 (2015).10. H. K. Finucane et al., Nat. Genet. 47, 1228–1235 (2015).11. A. Kundaje et al., Nature 518, 317–330 (2015).12. M. Claussnitzer et al., N. Engl. J. Med. 373, 895–907

(2015).13. M. A. Garcia-Blanco, A. P. Baraniak, E. L. Lasda,

Nat. Biotechnol. 22, 535–546 (2004).14. H. B. Fraser, X. Xie, Genome Res. 19, 567–575

(2009).15. H. Y. Xiong et al., Science 347, 1254806 (2015).16. See the supplementary materials and methods on Science

Online.

17. J. K. Pickrell et al., Nature 464, 768–772 (2010).18. N. E. Banovich et al., PLOS Genet. 10, e1004663 (2014).19. A. Battle et al., Science 347, 664–667 (2015).20. Y. I. Li, D. A. Knowles, J. K. Pritchard, http://biorxiv.org/

content/early/2016/03/16/044107 (2016).21. S. Shukla et al., Nature 479, 74–79 (2011).22. M. Gutierrez-Arcelus et al., PLOS Genet. 11, e1004958

(2015).23. J. K. Pickrell, Am. J. Hum. Genet. 94, 559–573 (2014).24. T. Flutre, X. Wen, J. Pritchard, M. Stephens, PLOS Genet. 9,

e1003486 (2013).25. E. Ira, M. Zanoni, M. Ruggeri, P. Dazzan, S. Tosato,

J. Psychiatry Neurosci. 38, 366–380 (2013).

ACKNOWLEDGMENTS

We thank S. Prabhakar for early access to the H3K27ac data;J. Blischak for technical assistance with the 4sU experiments;the anonymous reviewers for helpful comments; and A. Battle,A. Pai, N. Banovich, A. Fu, X. Lan, A. Harpak, and other members

of the Pritchard/Gilad Labs for helpful discussions. This workwas supported by NIH grants R01MH084703, RO1MH101825,U01HG007036, and U54CA149145; by a Center for Computational,Evolutionary and Human Genomics Fellowship; and by the HowardHughes Medical Institute. The 4sU-seq data have been depositedin the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/)under accession no. GSE75220; other accession numbers can befound in table S8.

SUPPLEMENTARY MATERIALS

www.sciencemag.org/content/352/6285/600/suppl/DC1Materials and MethodsFigs. S1 to S18Tables S1 to S8References (26–53)Data Table S1

24 November 2015; accepted 25 March 201610.1126/science.aad9417

BIOPHYSICS

Broken detailed balance atmesoscopic scales in activebiological systemsChristopher Battle,1,2* Chase P. Broedersz,2,3,4* Nikta Fakhri,1,2,5* Veikko F. Geyer,6

Jonathon Howard,6 Christoph F. Schmidt,1,2† Fred C. MacKintosh2,7†

Systems in thermodynamic equilibrium are not only characterized by time-independentmacroscopic properties, but also satisfy the principle of detailed balance in thetransitions between microscopic configurations. Living systems function out ofequilibrium and are characterized by directed fluxes through chemical states, whichviolate detailed balance at the molecular scale. Here we introduce a method to probe forbroken detailed balance and demonstrate how such nonequilibrium dynamics aremanifest at the mesosopic scale. The periodic beating of an isolated flagellum fromChlamydomonas reinhardtii exhibits probability flux in the phase space of shapes.With a model, we show how the breaking of detailed balance can also be quantified instationary, nonequilibrium stochastic systems in the absence of periodic motion.We further demonstrate such broken detailed balance in the nonperiodic fluctuations ofprimary cilia of epithelial cells. Our analysis provides a general tool to identifynonequilibrium dynamics in cells and tissues.

When a system reaches thermodynamicequilibrium, its properties become sta-tionary in time, which requires a netbalance between rates of transitions intoand out of any particular microstate of

the system. Systems in thermodynamic equilib-rium, however, are known to be balanced in aneven stronger way. They obey detailed balance,in which transition rates between any two micro-states are pairwise balanced (Fig. 1A). This meansthere can be no net flux of transitions anywherein the phase space of system states. This principlewas identified and used by Ludwig Boltzmannin his pioneering development of statistical me-chanics, the microscopic basis for thermody-namics (1). In contrast, living systems operatefar from equilibrium, and molecular-scale viola-tions of detailed balance lie at the heart of theirdynamics. For instance, metabolic and enzymaticprocesses drive closed-loop fluxes through thesystem’s chemical states (Fig. 1B) (2).Nonequilibrium driving can boost intracel-

lular transport (3–5), the fidelity of transcrip-tion (6), chemotaxis (7, 8), and the accuracy ofsensory perception (9, 10). To understand cell


1Drittes Physikalisches Institut, Georg-August-Universität,37077 Göttingen, Germany. 2The Kavli Institute forTheoretical Physics, University of California, Santa Barbara,CA 93106, USA. 3Arnold-Sommerfeld-Center for TheoreticalPhysics and Center for NanoScience, Ludwig-Maximilians-Universität München, Theresienstrasse 37, D-80333München, Germany. 4Lewis–Sigler Institute for IntegrativeGenomics and Joseph Henry Laboratories of Physics,Princeton University, Princeton, NJ 08544, USA.5Department of Physics, Massachusetts Institute ofTechnology, Cambridge, MA 02139, USA. 6Department ofMolecular Biophysics and Biochemistry, Yale University, NewHaven, CT, USA. 7Department of Physics and Astronomy,Vrije Universiteit, Amsterdam, Netherlands.*These authors contributed equally to this work. †Correspondingauthor. Email: [email protected] (F.C.M.); [email protected] (C.F.S.)


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RNA splicing is a primary link between genetic variation and diseaseYang I. Li, Bryce van de Geijn, Anil Raj, David A. Knowles, Allegra A. Petti, David Golan, Yoav Gilad and Jonathan K. Pritchard

DOI: 10.1126/science.aad9417 (6285), 600-604.352Science

, this issue p. 600SciencePosttranscriptional mechanisms therefore play a large role in translating genotype to phenotype.processes, not chromatin. Splice QTLs and expression QTLs are about comparable in their complex disease risk.state through protein function. One-third of expression quantitative train loci (QTLs) are mediated through transcriptional

systematically analyzed the effects of DNA variants on the main steps of gene regulation, from the chromatinet al.Li Many genetic variants associated with disease have no apparent effect on any specific protein coding sequence.

RNA splicing links genetics to disease

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