Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Opportunities and challenges for transcriptome-wide association studies



Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene–trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn’s disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.

GWAS have robustly associated thousands of genomic loci with complex traits. Despite this success, GWAS loci are often difficult to interpret: linkage disequilibrium (LD) often obscures the causal variants driving the association, and the causal genes mediating variant effects on the trait are rarely ascertainable from GWAS data alone1. This interpretation challenge has motivated the development of methods to prioritize causal genes at GWAS loci.

One such family of methods is TWAS, which leverage expression reference panels (eQTL cohorts with expression and genotype data) to discover gene–trait associations from GWAS datasets2,3,4. First, the expression panel is used to learn per-gene predictive models of expression variation by using allele counts of genetic variants in the gene’s vicinity (typically within 500 kilobases or 1 megabase). These models are used to predict gene expression for each individual in the GWAS cohort. Finally, statistical associations are estimated between predicted gene expression and the trait (Fig. 1a). Expression prediction and association may be performed sequentially with individual-level GWAS data (PrediXcan2) or simultaneously with summary-level GWAS data (Fusion3 and S-PrediXcan4). Closely related methods include SMR/HEIDI5,6,7, which performs Mendelian randomization (MR) from gene expression to trait, and GWAS–eQTL colocalization methods such as Sherlock8, coloc9,10, QTLMatch11, eCaviar12, enloc13 and RTC14, which discover genes whose expression is regulated by the same causal variants that underlie a GWAS hit.

Fig. 1: TWAS, like GWAS, frequently has multiple significant associations per locus.

a, An overview of TWAS. Briefly, TWAS involves: (i) training a predictive model of expression from genotype on a reference panel such as GTEx; (ii) using this model to predict expression for individuals in the GWAS cohort; and (iii) associating this predicted expression with the trait. b,c, Manhattan plots of GWAS (b) and Fusion TWAS (c) for LDL cholesterol, using GWAS summary statistics from the Global Lipids Genetics Consortium and liver expression from the STARNET cohort (Supplementary Note). GWAS has multiple hits per locus, owing to LD, and TWAS has multiple hits per locus, owing to co-regulation (which can also be driven in part by LD; described below), as explored in the main text. Clusters of multiple adjacent TWAS hit genes are highlighted in red. d, Three scenarios in which co-regulation can lead to multiple hits per locus, and the estimated percentage of non-causal hit genes subject to each scenario; each scenario is presented in a case study later in the text. To estimate the percentages, we grouped hits into 2.5-megabase clumps and made the approximation that genes that were not the top hit in multi-hit clumps were non-causal; we then calculated the percentage of these genes with total or predicted expression r2 ≥ 0.2 or ≥ 1 shared variant with the top hit in their block, aggregating genes across the LDL/liver and Crohn’s disease/whole-blood TWAS. The full distributions of the total and predicted expression correlations and number of shared variants are shown in Supplementary Fig. 1, separated by study.

TWAS have garnered substantial interest within genetics and have been conducted for many traits and tissues15,16. Although TWAS methods are statistical tests associating genetically predicted expression and disease risk, with no guarantees of causality, a key reason for their appeal is the promise of prioritizing candidate causal genes (genes mediating the phenotypic effects of causal genetic variants) and tissues underlying GWAS loci. Unfortunately, there is a prevalent misconception that TWAS are causal-gene tests and that TWAS associations represent bona fide causal genes; in the following sections, we provide guidelines for interpreting TWAS results, highlighting scenarios in which TWAS accurately prioritize candidate causal genes and others for which TWAS-prioritized genes are likely to be non-causal.

As a motivating example illustrating both the successes and interpretational challenges of TWAS, consider C4A, a causal gene for schizophrenia. Variants at the C4A locus contribute to schizophrenia risk by increasing the brain expression of C4A17. A TWAS has strongly associated C4A with schizophrenia on the basis of brain expression data from the Genotype-Tissue Expression (GTEx) project18. Notably, C4A is by far the most significantly associated gene within 100 kilobases in brain tissues. C4A is also the most significantly associated gene in any tissue (Supplementary Table 1), even compared with other closely related genes in the complement system (C4B, CFB and C2). However, 8 of the 12 other genes within 100 kilobases are at least marginally significant (P <0.05) in some brain tissue, and 11 of 12 are highly significant (P <5 × 10−5) in at least one tissue. C4A is also more significantly associated with schizophrenia in the pancreas than in any brain tissue.

TWAS-significant loci contain multiple associated genes

GWAS are well known to rarely identify single variant–trait associations but instead to identify blocks of associated variants in LD (Fig. 1b). Analogously, TWAS frequently identify multiple hit genes per locus16 (Fig. 1c).

To explore this phenomenon, we performed TWAS in two traits and two tissues with Fusion and S-PrediXcan, by using GWAS summary statistics for low-density lipoprotein (LDL) cholesterol19 and Crohn’s disease20, and the 522 liver and 447 whole-blood expression samples from the Stockholm–Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET) cohort21 (Supplementary Fig. 2 and Supplementary Note). We grouped hit genes within 2.5 megabases and found some loci with a single hit gene but others with as many as 11 hit genes (Supplementary Fig. 3).

Correlated expression across individuals may cause false hits

We explored the extent to which co-regulation can lead to multi-hit loci. Co-regulation is conventionally measured by correlating the expression of a pair of genes across individuals. Do genes with correlated expression with a strong TWAS hit also tend to be TWAS hits? We analyzed the SORT1 locus in LDL/liver (TWAS P = 1 × 10−243; Fig. 2a), the strongest hit locus across all four Fusion TWAS.

Fig. 2: Co-regulation strongly predicts TWAS hit strength at the SORT1 locus.

a, Fusion Manhattan plot of the SORT1 locus. b, Expression correlation (corr.) with SORT1 versus TWAS P value, for each gene in the SORT1 locus. Chr, chromosome.

Although SORT1 has strong evidence of causality, its locus contains eight hit genes in addition to SORT1, and their TWAS P values are highly related to their expression correlation with SORT1 (Spearman correlation = 0.75; Fig. 2b). A similar pattern holds for S-PrediXcan (Supplementary Fig. 5). The two most correlated genes, PSRC1 and CELSR2, were previously noted22 to share an eQTL with SORT1 in the liver (rs646776). Given SORT1’s strong evidence of causality and the other genes’ lack of strong literature evidence, the most parsimonious (though certainly not the only) explanation is that most or all other genes are non-causal and are prioritized only because of correlation with SORT1.

Correlated predicted expression may also cause false hits

However, expression correlation is not the whole story: TWAS tests for association with genetically predicted expression, not total expression. Total expression includes genetic, environmental and technical components, and the genetic component includes contributions from common cis eQTLs (the only component reliably detectable in current TWAS methods), rare cis eQTLs and trans eQTLs. Predicted expression represents only a small component of total expression: a large-scale twin study23 has found that common cis eQTLs explain only approximately 10% of genetic variance in expression.

Predicted expression correlations between same-locus genes are generally slightly higher than total expression correlations, sometimes substantially so (Fig. 3a and Supplementary Figs. 4 and 5d). A gene pair can have correlated predicted expression if the same causal eQTL regulates both genes or if two causal eQTLs in LD each regulate one of the genes24. Although only the first case counts as mechanistic co-regulation, we consider both cases together, because they are not designed to be distinguishable by TWAS: the two genes’ TWAS models can rely on distinct variants even in the first case or rely on the same variant even in the second case. For instance, given a causal eQTL in near-perfect LD with another variant, an L1-penalized linear expression model (for example, LASSO or ElasticNet) may place the most weight on only one of the two variants, but which variant is chosen could change depending on statistical fluctuations in the training set.

Fig. 3: Correlated predicted expression can cause non-causal hits even in the absence of correlated total expression.

a, For nearby genes, Fusion-predicted expression correlations tend to be higher than total expression correlations, for example, at the SORT1 locus. b, Fusion Manhattan plot of the IRF2BP2 locus, where RP4-781K5.7 is a likely non-causal hit due to predicted expression correlation with IRF2BP2. c, Details of the two genes’ Fusion expression models: a line between a variant’s rs number and a gene indicates that the variant is included in the gene’s expression model with either a positive weight (blue) or a negative weight (orange); the thickness of the line increases with the magnitude of the weight; red arcs indicate LD. Pink rs numbers are GWAS hits (genome-wide significant or sub-significant), whereas gray rs numbers are not. For clarity, four variants with weights less than 0.05 in magnitude for IRF2BP2 (rs2175594, P = 0.02, weight +0.03; rs2439500, P = 0.2, weight = +0.01; rs11588636, P = 0.3, weight = –0.03; and rs780256, P = 0.9, weight = –0.03) and five variants for RP4-781K5.7 (rs478425, P = 0.01, weight = +0.02; rs633269, P = 0.02, weight = +0.01; rs881070, P = 0.06, weight = –0.02; rs673283, P = 0.1, weight = +0.004; and rs9659229, P = 0.1, weight = –0.04) are not shown. d, Estimated causal probability for each significant gene from Fusion at the SORT1 and IRF2BP2 loci, according to TWAS gene-based fine-mapping with the FOCUS method.

Predicted expression correlation may lead to non-causal genes being prioritized before causal genes, even if the total expression correlation is low. This type of confounding has also been observed in gene-set analysis25. For instance, SARS is the main outlier in Fig. 2b and is as significant as SORT1 despite having a total expression correlation of only ~0.2, because of its high predicted expression correlation of ~0.9 (Fig. 3a). SARS is also an outlier in PrediXcan for the same reason (Supplementary Fig. 5d).

Another example is the IRF2BP2 locus in LDL/liver (Fig. 3b). IRF2BP2 encodes an inflammation-suppressing regulatory factor with causal evidence from mouse models. RP4-781K5.7 is a largely uncharacterized long non-coding RNA that lacks evidence of function; most long non-coding RNAs are non-essential for cell fitness26, and current evidence is compatible with most non-coding RNAs being non-functional27. Despite a negligible total expression correlation between the two genes (–0.02), IRF2BP2’s Fusion expression model includes GWAS hit rs556107 with a negative weight, whereas RP4-781K5.7’s includes the same variant (plus two linked variants) with a positive weight (Fig. 3c), thus resulting in almost perfectly anti-correlated predicted expression (–0.94) and both genes being TWAS hits. IRF2BP2 and RP4-781K5.7 are also both hits with S-PrediXcan, and both S-PrediXcan and Fusion place the largest weight on rs556107 but with opposite signs (Supplementary Fig. 6).

We simulated expression and trait data (ntrait = 50,000 individuals; nexpression = 500) for 1,000 random genomic loci by using the FOCUS simulation framework24 and conducted TWAS by using L2-penalized linear regression (Supplementary Note). As expected, a larger predicted expression correlation increased the probability of having a larger TWAS z score than that of the causal gene (Supplementary Table 2). However, this probability remained modestly high even when the predicted expression correlation was low, thus implying that predicted expression, though better than true expression, still imperfectly captures co-regulation.

Shared GWAS variants may cause false hits

More generally, pairs of gene models may share variants (or at least LD partners) even if the predicted expression correlation is low, because other variants distinct between the models may ‘dilute’ the correlation. For instance, at the NOD2 locus for Crohn’s disease/whole blood, NOD2 is a known causal gene, but four other genes are also Fusion hits (Fig. 4a), none of which have strong causal evidence (though rare variants in ADCY7 have been associated with ulcerative colitis28). The model for the strongest hit gene, BRD7, places the most weight on rs1872691, the strongest GWAS hit in NOD2’s model (Fig. 4b). However, NOD2’s model places the most weight on two weaker GWAS hits, rs7202124 and rs1981760. Thus, even though co-regulation with NOD2 may explain why BRD7 is a TWAS hit, this co-regulation is not captured by the metrics that we discussed: both the predicted expression (–0.03) and total expression (0.05) correlations are near 0. The same five genes are also S-PrediXcan hits, and NOD2 and BRD7’s models share the same rs1872691 variant, as with Fusion (Supplementary Fig. 7).

Fig. 4: Sharing of GWAS variants between expression models can contribute to non-causal hits even without correlated predicted expression.

a, Fusion Manhattan plot of the NOD2 locus. b, Details of the expression models of NOD2 and BRD7; as in Fig. 2, a line between a variant’s rs number and a gene indicates that the variant is included in the gene’s expression model with either a positive weight (blue) or a negative weight (orange), with the thickness of the line increasing with the magnitude of the weight. Red arcs indicate LD.

Most generally, models need not even share the same GWAS variants (or LD partners) to have spurious hits. For instance, rs4643314, the strongest GWAS hit in BRD7’s Fusion model, is neither shared nor in strong LD with any variants in NOD2’s model, although it is in weak LD with rs1872691 (Fig. 4b). Although the most parsimonious explanation is that BRD7 is also causal, and rs4643314 acts through BRD7, BRD7 lacks evidence of causality. An alternate explanation is that only NOD2 is causal, rs4643314 acts through NOD2 (but also happens to co-regulate BRD7), and NOD2’s model erroneously fails to include it (a false negative). One trivial reason for false negatives is variants outside the 500 kilobase/1 megabase window included in the model, which can be solved by increasing the window. More problematic causes include bias in the expression panel (‘Discussion’) and, for methods using GWAS summary statistics, LD mismatch between the expression panel and GWAS. This scenario might occur even without any false negatives, for example, if a variant in LD with rs4643314 deleteriously affects NOD2’s coding sequence as well as regulating BRD7, because TWAS is not designed to detect coding effects. Figure 5 illustrates the various types of co-regulation that may lead to non-causal TWAS hits.

Fig. 5: Co-regulation scenarios in TWAS that may lead to non-causal hits, from least to most general.

a, Correlated expression across individuals: the causal gene has correlated total expression with another gene, which may become a non-causal TWAS hit. Co-reg, co-regulation. b, Correlated predicted expression across individuals: even if total expression correlation is low, predicted expression correlation may be high if the same variants (or variants in LD) regulate both genes and are included in both models. c, Sharing of GWAS hits: even if the two genes’ models include largely distinct variants, and predicted expression correlation is low, only a single shared GWAS hit variant (or variant in LD) is necessary for both genes to be TWAS hits. d, Both models include distinct GWAS hits: in the most general case, the GWAS hits driving the signal at the two genes may not be in LD with each other, for instance if the non-causal gene’s GWAS hit happens to regulate the causal gene as well, but this connection is missed by the expression modeling (a false negative), or if the causal gene’s GWAS hit acts via a coding mechanism (not shown).

Bias with expression panels from non-trait-related tissues

Tissues with large expression panels (whole blood or lymphoblastoid) are commonly used to maximize power, even when they are mechanistically less related to the trait. To date, our case studies have used expression from mechanistically related tissues: liver for LDL and whole blood for Crohn’s disease. What if we swap these tissues and use tissues without a clear mechanistic relationship? The architecture of eQTLs differs substantially across tissues: even among strong eQTLs in GTEx (P ≈ 1 × 10−10), one-quarter show a switch in the most significantly associated gene across tissues18.

We curated candidate causal genes from the literature (Supplementary Table 3) at nine LDL/liver and four Crohn’s disease/whole-blood Fusion TWAS loci and examined how the hit strengths changed when we swapped tissues (Fig. 6). Notably, almost every candidate causal gene (9 of 11 for LDL and 5 of 6 for Crohn’s disease) was no longer a hit in the ‘opposite’ tissue, because of either insufficient expression (n = 4: PPARG, LPA, LPIN3 and SLC22A4) or insufficiently heritable cis expression according to Fusion’s likelihood-ratio test (n = 10: SORT1, IRF2BP2, TNKS, FADS3, ALDH2, KPNB1, SLC22A5, IRF1, CARD9, STAT3). This trend held globally, albeit less strongly: genome-wide, 3,085 of 5,858 LDL/liver genes (53%) dropped out after switching to whole blood, and 1,202 of 2,118 Crohn’s disease/whole-blood genes (57%) dropped out after switching to liver. Just because a gene does not drop out, and is present in both tissues as a result of shared cross-tissue regulatory architecture, causality is not necessarily implied.

Fig. 6: Most candidate causal genes drop out after switching to a tissue with a less clear mechanistic relationship to the trait, owing to a lack of sufficient expression or sufficiently heritable expression.

Fusion TWAS P values at nine LDL/liver and four Crohn’s disease/whole-blood multi-hit loci, using expression from tissues with a clear (top row) or less clear or absent (bottom row) mechanistic relationship to the trait. Candidate causal genes are labeled and colored red.

More problematically, 15 other genes at the same loci were still hits (eight in LDL/whole blood and seven in Crohn’s disease/liver), five with P <1 × 10−20. This result suggests that the strategy of conducting TWAS in a sub-optimal tissue with a large expression panel is especially problematic because even if there are hits at a locus, the causal gene may not be among them.

Combining the whole-blood and liver reference panels by averaging each individual’s expression in the two tissues (equivalent, for L1- and/or L2-penalized regression, to concatenating the two panels) performed more poorly than using the mechanistically related tissue alone but better than using the less related tissue alone (Supplementary Fig. 8).

TWAS improves causal-gene prioritization

We investigated TWAS’s performance at ranking (prioritizing) causal genes at loci from the previous section. We compared Fusion to two simple gene-ranking baselines (Supplementary Table 4): transcription-start-site proximity to the most significant GWAS variant within 2.5 megabases of any gene at the locus (‘proximity’) and median expression across GTEx individuals in the liver (for LDL genes) or whole blood (for Crohn’s disease genes) (‘expression’). Genes with more significant TWAS P values, smaller distances to the lead GWAS variant or higher expression had higher rankings. The mean rank of the 17 candidate causal genes was 3.9 by random per-locus ranking, 2.0 by TWAS, 2.2 by proximity (P = 0.5, two-tailed Wilcoxon signed-rank test) and 2.9 by expression (P = 0.006). Hence, Fusion outperforms both baselines but does not significantly outperform proximity.

Suggested best practices and future opportunities

We highlighted two vulnerabilities—co-regulation and tissue bias—that affect TWAS’s performance in causal-gene prioritization. In this section, we discuss current best practices and future opportunities for their mitigation.

One emerging approach to address co-regulation repurposes GWAS fine-mapping to TWAS, on the basis of the analogy between LD in GWAS and co-regulation in TWAS. Fine-mapping of causal gene sets (FOCUS)24 directly models predicted expression correlations and uses them to assign genes posterior probabilities of causality. At the SORT1 locus, FOCUS includes SORT1, SARS and CELSR2 in the 90%-credible set; at the IRF2BP2 locus, FOCUS includes both IRF2BP2 and RP4-781K5.7 (Fig. 2d). We recommend using fine-mapping methods such as FOCUS or, at a minimum, considering relative association strengths (P values and effect sizes) at a locus when interpreting TWAS results. If individual-level data are available, inferring effects jointly through penalized regression (for example, LASSO or Ridge) offers a flexible alternative (Supplementary Tables 5 and 6). Nonetheless, TWAS fine-mapping is more challenging than GWAS fine-mapping: predicted expression only imperfectly captures cis expression, owing to both variance and bias in the expression modeling (Box 1).

To address tissue bias, we recommend using an expression panel from only the most mechanistically related tissue available, even when it is smaller than other tissues’. However, using a slightly less related tissue (for example, a different region of the brain) would be advisable if the sample size would be substantially increased; the trade-off between tissue bias and sample size should be evaluated on a case-by-case basis. When a trait’s most related tissue is not known a priori, a recent approach based on LD Score regression29 can be used to select among multiple reference panels. Methods to handle cross-tissue pleiotropy and cell-type heterogeneity, discussed above in the context of fine-mapping, can also mitigate tissue bias. If no sufficiently large reference panels from closely related tissues are available, we recommend aggregating information across all available tissues in a tissue-agnostic manner4,30.

When reference panels have highly dissimilar sizes across tissues, the tissue with the most significant TWAS P value cannot necessarily be assumed to be causal, because reference-panel size affects the P value. For this reason, we recommend considering TWAS effect size in addition to P value when investigating causal tissues for TWAS-associated genes. Even when all reference panels are similarly sized, the exact combination of tissue, cell type and context (for example, developmental stage and cellular stress) mediating the causal gene’s effect may not be captured by any panel, and this may be the case even if TWAS finds the correct causal gene (for example, C4A is correctly chosen on the basis of RNA-seq on adult samples even though its causal effect on schizophrenia probably occurs in adolescence). Furthermore, bias may alter the pattern of TWAS P values and effect sizes across tissues in unexpected ways. We caution against over-interpretation.

Several emerging topics in TWAS deserve further mention. Multi-tissue TWAS methods such as UTMOST30 increase power by jointly training expression models across multiple tissues. MulTiXcan31 fits a multivariate regression with phenotype as the outcome and a gene’s expression across multiple tissues or contexts as the inputs to increase power. The adaptive sum of a powered score test32 increases power by adaptively adjusting how much to exponentiate the weighted genotypes (genotypes times expression model weights) in the final expression-trait test, from γ = 1 (for example, Fusion or PrediXcan) or γ = 2 (for example, SKAT33) to γ = ∞ (in which all weight is placed on the most significant GWAS variant, a method more appropriate than standard TWAS when there are few associated variants). Mogil et al.34 have shown that between-population allele-frequency differences worsen cross-ancestry expression predictions, thus underscoring the importance of gathering diverse expression cohorts. Finally, the emerging ability to generate very large expression panels offers the promise of using trans eQTL signals to overcome the co-regulation problem35,36: although all genes at a locus may show GWAS signal at their cis eQTLs, owing to co-regulation, only the true causal genes are expected to show significant GWAS signal at their trans eQTLs as well.


In our case studies, we assumed that the single gene with substantial causal evidence was the sole causal gene at the locus, with some exceptions (FADS1/2/3 and SLC22A4/5IRF1). Nonetheless, other loci may contain multiple causal genes. Indeed, under an omnigenic model37, every gene may be causal to some degree, although TWAS identification of marginally causal genes as strong hits due to co-regulation (effect size inflation) remains problematic. Furthermore, the expression of a ‘non-causal’ gene may causally influence expression of the causal gene merely by being transcribed, even if the gene is non-coding or its protein product is non-causal38.

Co-regulation and tissue bias affect other methods integrating GWAS and expression data. Testing of gene–trait associations based on MR5,6,7 is vulnerable, because co-regulation, as a form of pleiotropy, violates one of the core assumptions of MR39. Although the HEIDI test5 corrects for the case in which two genes have distinct but linked causal variants, it does not correct for the case in which they share the same causal variant. GWAS–eQTL colocalization methods such as Sherlock8, coloc9,10, QTLMatch11, eCaviar12, enloc13 and RTC14 are also vulnerable. The more tightly a pair of genes is co-regulated in cis, the more difficult it becomes to distinguish causality on the basis of GWAS and expression data alone. Our results underscore the need for computational and experimental methods that move beyond expression variation across individuals to complement TWAS in identifying causal genes at GWAS loci.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.


  1. 1.

    Gallagher, M. D. & Chen-Plotkin, A. S. The post-GWAS era: from association to function. Am. J. Hum. Genet. 102, 717–730 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    CAS  Article  Google Scholar 

  3. 3.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  Article  Google Scholar 

  4. 4.

    Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

    Article  Google Scholar 

  5. 5.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  Article  Google Scholar 

  6. 6.

    Hauberg, M. E. et al. Large-scale identification of common trait and disease variants affecting gene expression. Am. J. Hum. Genet. 100, 885–894 (2017).

    CAS  Article  Google Scholar 

  7. 7.

    Pavlides, J. M. W. et al. Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Med. 8, 84 (2016).

    Article  Google Scholar 

  8. 8.

    He, X. et al. Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am. J. Hum. Genet. 92, 667–680 (2013).

    CAS  Article  Google Scholar 

  9. 9.

    Wallace, C. et al. Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes. Hum. Mol. Genet. 21, 2815–2824 (2012).

    CAS  Article  Google Scholar 

  10. 10.

    Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Plagnol, V., Smyth, D. J., Todd, J. A. & Clayton, D. G. Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13. Biostatistics 10, 327–334 (2009).

    Article  Google Scholar 

  12. 12.

    Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Wen, X., Pique-Regi, R. & Luca, F. Integrating molecular QTL data into genome-wide genetic association analysis: probabilistic assessment of enrichment and colocalization. PLoS Genet. 13, e1006646 (2017).

    Article  Google Scholar 

  14. 14.

    Nica, A. C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).

    Article  Google Scholar 

  15. 15.

    Mancuso, N. et al. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet. 100, 473–487 (2017).

    CAS  Article  Google Scholar 

  16. 16.

    Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018).

    CAS  Article  Google Scholar 

  17. 17.

    Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  19. 19.

    Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

    CAS  Article  Google Scholar 

  20. 20.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  Article  Google Scholar 

  21. 21.

    Franzén, O. et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 353, 827–830 (2016).

    Article  Google Scholar 

  22. 22.

    Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).

    CAS  Article  Google Scholar 

  23. 23.

    Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

    CAS  Article  Google Scholar 

  24. 24.

    Mancuso, N. et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat. Genet. (2019).

    CAS  Article  Google Scholar 

  25. 25.

    de Leeuw, C. A., Neale, B. M., Heskes, T. & Posthuma, D. The statistical properties of gene-set analysis. Nat. Rev. Genet. 17, 353–364 (2016).

    Article  Google Scholar 

  26. 26.

    Liu, S. J. et al. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 355, aah7111 (2017).

    Article  Google Scholar 

  27. 27.

    Palazzo, A. F. & Lee, E. S. Non-coding RNA: what is functional and what is junk? Front. Genet. 6, 2 (2015).

    Article  Google Scholar 

  28. 28.

    Luo, Y. et al. Exploring the genetic architecture of inflammatory bowel disease by whole-genome sequencing identifies association at ADCY7. Nat. Genet. 49, 186–192 (2017).

    CAS  Article  Google Scholar 

  29. 29.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    CAS  Article  Google Scholar 

  30. 30.

    Barbeira, A. N. et al. Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet. 15, e1007889 (2019).

    Article  Google Scholar 

  31. 31.

    Hu, Y. et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat. Genet. 51, 568–576 (2019).

    CAS  Article  Google Scholar 

  32. 32.

    Xu, Z., Wu, C., Wei, P. & Pan, W. A powerful framework for integrating eQTL and GWAS summary data. Genetics 207, 893–902 (2017).

    CAS  Article  Google Scholar 

  33. 33.

    Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    CAS  Article  Google Scholar 

  34. 34.

    Mogil, L. S. et al. Genetic architecture of gene expression traits across diverse populations. PLoS Genet. 14, e1007586 (2018).

    Article  Google Scholar 

  35. 35.

    Võsa, U. et al. Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. Preprint at (2018).

  36. 36.

    Wheeler, H. E. et al. Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits. Preprint at (2018).

  37. 37.

    Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    CAS  Article  Google Scholar 

  38. 38.

    Engreitz, J. M. et al. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539, 452–455 (2016).

    CAS  Article  Google Scholar 

  39. 39.

    Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. & Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013).

    CAS  Article  Google Scholar 

  40. 40.

    Bhutani, K., Sarkar, A., Park, Y., Kellis, M. & Schork, N. J. Modeling prediction error improves power of transcriptome-wide association studies. Preprint at (2017).

  41. 41.

    Ongen, H. et al. Estimating the causal tissues for complex traits and diseases. Nat. Genet. 49, 1676–1683 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    CAS  Article  Google Scholar 

  43. 43.

    Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    Article  Google Scholar 

  44. 44.

    Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).

    CAS  Article  Google Scholar 

Download references


We gratefully acknowledge J. Pritchard, H. Tang and members of the laboratory of N. Zaitlen for helpful discussions. This work was funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) (grant PGSD3-476082-2015 to M.W.); a Stanford Bio-X Bowes fellowship (to M.W.); a Stanford Graduate Fellowship (to N.S.-A.); a National Defense Science & Engineering Grant (to N.S.-A.); NIH grants 1DP2OD022870 and U01HG009431 (to A.K.), 1U24HG008956 and 5U01HG009080 (to M.A.R.), R01HG009120 and R01MH115676 (to B.P.), R01MH107666, R01MH101820 and P30DK20595 (to H.K.I.), and R01HL125863 and R21TR001739 (to J.L.M.B.); NHGRI grant R01HG010140 (to M.A.R.); Leducq Foundation grant 12CVD02 (to J.L.M.B.); and American Heart Association grant A14SFRN20840000 (to J.L.M.B.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information




M.W., M.A.R. and A.K. conceived the study. M.W., N.M. and A.N.B. performed analyses. N.S.-A., D.A.K. and D.G. provided intellectual input. R.E., A.R., T.Q., K.H. and J.L.M.B. provided assistance with analysis of the STARNET dataset. H.K.I., B.P., M.A.R. and A.K. supervised the study. M.W., H.K.I., B.P., M.A.R. and A.K. wrote the manuscript. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Johan L. M. Björkegren, Hae Kyung Im, Bogdan Pasaniuc, Manuel A. Rivas or Anshul Kundaje.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Figures 1–8 and Supplementary Tables 1–6

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wainberg, M., Sinnott-Armstrong, N., Mancuso, N. et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet 51, 592–599 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing