Opportunities and challenges for transcriptome-wide association studies

Wainberg, Michael; Sinnott-Armstrong, Nasa; Mancuso, Nicholas; Barbeira, Alvaro N.; Knowles, David A.; Golan, David E.; Ermel, Raili; Ruusalepp, Arno; Quertermous, Thomas; Hao, Ke; Björkegren, Johan; Im, Hae Kyung; Paşaniuc, Bogdan; Rivas, Manuel A.; Kundaje, Anshul

doi:10.1038/s41588-019-0385-z

Cited by 638 publications

(695 citation statements)

References 46 publications

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“…Finally, we comment on our view that TWAS is a weighted Sum test and its related issues, which are also discussed by Wainberg et al. () and in http://hakyimlab.org/post/vulnerabilities/. Although TWAS was originally proposed to identify GWAS associations through gene expression, any such discovery based on a single eQTL/GWAS dataset is at most only suggestive to mediating effects of gene expression.…”

Section: Discussionmentioning

confidence: 77%

Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia

Pan

2018

Genetic Epidemiology

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Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. The basic idea is to weight the SNPs of the genes in a pathway based on their estimated cis-effects on gene expression, then adaptively test for association of the pathway with a GWAS trait by effectively aggregating possibly weak association signals across the genes in the pathway. The P values can be calculated analytically and thus fast. We applied our proposed test with the KEGG and GO pathways to two schizophrenia (SCZ) GWAS summary association data sets, denoted by SCZ1 and SCZ2 with about 20,000 and 150,000 subjects, respectively. Most of the significant pathways identified by analyzing the SCZ1 data were reproduced by the SCZ2 data. Importantly, we identified 15 novel pathways associated with SCZ, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS. The newly identified pathways may help us gain insights into the biological mechanism underlying SCZ. Our results showcase the power of incorporating gene expression information and gene functional annotations into pathway-based association testing for GWAS.

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Section: Discussionmentioning

confidence: 77%

Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia

Pan

2018

Genetic Epidemiology

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“…33 In addition, TWAS cannot distinguish causal relationship and pleiotropy. TWAS genes are more appropriately interpreted as prioritized or ranked candidate causal genes at loci.…”

Section: Discussionmentioning

confidence: 99%

“…TWAS genes are more appropriately interpreted as prioritized or ranked candidate causal genes at loci. 33 In addition, TWAS cannot distinguish causal relationship and pleiotropy. For example, if the same SNPs affect the expression level of more than one gene, TWAS cannot delineate the causal one.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

Bossé

Xia

et al. 2019

Intl Journal of Cancer

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We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large‐scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome‐wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never‐ and ever‐smokers). We performed replication analysis using lung data from the Genotype‐Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever‐smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (pTWAS = 1.09E−99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (pTWAS = 3.72E−6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3‐adenocarcinoma on 9p13.3 was replicated using GTEx (pTWAS = 6.55E−5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk.

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“…Then we test the association between the imputed gene expression and the GWAS trait. If there is an association, then, under suitable modeling assumptions (Hu et al, 2019;Mancuso et al, 2019;Wainberg et al, 2019;Xu, Wu, Wei, & Pan, 2017), it is claimed that the gene is (putatively) causal to the trait: some causal SNPs affect the trait through the mediating effects of the gene's expression.…”

Section: Introductionmentioning

confidence: 99%

Some statistical consideration in transcriptome‐wide association studies

Xue

Pan

Initiative³

2019

Genetic Epidemiology

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The methodology of transcriptome‐wide association studies (TWAS) has become popular in integrating a reference expression quantitative trait (eQTL) data set with an independent main GWAS data set to identify (putatively) causal genes, shedding mechanistic insights to biological pathways from genetic variants to a GWAS trait mediated by gene expression. Statistically TWAS is a (two‐sample) 2‐stage least squares (2SLS) method in the framework of instrumental variables analysis for causal inference: in Stage 1 it uses the reference eQTL data to impute a genes expression for the main GWAS data, then in Stage 2 it tests for association between the imputed gene expression and the GWAS trait; if an association is detected in Stage 2, a (putatively) causal relationship between the gene and the GWAS trait is claimed. If a nonlinear model or a generalized linear model (GLM) is fitted in Stage 2 (e.g., for a binary GWAS trait), it is known that using only imputed gene expression, as in standard TWAS, in general does not lead to a consistent (i.e., asymptotically unbiased) estimate for the causal effect; accordingly, a variation of 2SLS, called two‐stage residual inclusion (2SRI), has been proposed to yield better estimates (e.g., being consistent under suitable conditions). Our main goal is to investigate whether it is necessary or even better to apply 2SRI, instead of the standard 2SLS. In addition, due to the use of imputed gene expression (i.e., with measurement errors), it is known that in general some correction to the standard error estimate of the causal effect estimate has to be applied, while in the standard TWAS no correction is applied. Is this an issue? We also compare one‐sample 2SLS with two‐sample 2SLS (i.e., the standard TWAS). We used the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and simulated data mimicking the ADNI data to address the above questions. At the end, we conclude that, in practice with the large sample sizes and small effect sizes of genetic variants, the standard TWAS performs well and is recommended.

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Opportunities and challenges for transcriptome-wide association studies

Cited by 638 publications

References 46 publications

Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia

Integrating eQTL data with GWAS summary statistics in pathway‐based analysis with application to schizophrenia

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

Some statistical consideration in transcriptome‐wide association studies

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