Transcriptome-wide association studies: opportunities and challenges

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:10.1101/206961

Cited by 23 publications

(25 citation statements)

References 76 publications

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“…Our observation that candidate GWAS genes have high EDS has implications for using eQTL data to prioritize causal genes. Consistent with our results, other recent studies have also noted the limitations of using eQTL information in GWAS analysis 61,62 . While we expect the causal 480 genes at GWAS loci will be eQTL targets with sufficient study sample sizes, our results support a conjecture raised by Hormozdiari et al where the causal eQTL signals at GWAS loci are "secondary signals in comparison to the stronger associations found in current eQTL studies" 63 .…”

Section: Furthermore By Showing a Significant Relationship Between Esupporting

confidence: 93%

Enhancer redundancy predicts gene pathogenicity and informs complex disease gene discovery

Wang

Goldstein

2018

Preprint

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Non-coding transcriptional regulatory elements are critical for controlling the spatiotemporal expression of genes. Here, we demonstrate that the number of bases in enhancers linked to a gene reflects its disease pathogenicity. Moreover, genes with redundant enhancer domains are depleted of cis-acting genetic variants that disrupt gene expression, and are buffered against the effects of disruptive non-coding mutations. Our results demonstrate that dosage-sensitive genes have evolved robustness to the disruptive effects of genetic variation by expanding their regulatory domains. This resolves a puzzle in the genetic literature about why disease genes are depleted of cis-eQTLs, suggesting that eQTL information may implicate the wrong genes at genome-wide association study loci, and establishes a framework for identifying non-coding regulatory variation with phenotypic consequences.

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Section: Furthermore By Showing a Significant Relationship Between Esupporting

confidence: 93%

Enhancer redundancy predicts gene pathogenicity and informs complex disease gene discovery

Wang

Goldstein

2018

Preprint

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“…The transcriptome-wide association study (TWAS) approach 103 for prioritizing genes has also been inconclusive: it assigns highly significant scores to both IRF1 and SLC22A5, as well as five other genes at the locus whose predicted expression is positively associated to CD. 104,105 Our result provides genome-wide evidence for a genuine causal link between IRF1 and CD that, unlike single-locus approaches, is not fundamentally limited by LD and pleiotropy near the IRF1 gene (see Discussion). The top results in our MSigDB gene-set enrichment analysis strengthen our finding: the regions driving this association are most significantly enriched for genes involved in production of type I interferon and for genes involved in regulation of nuclear division (see Figure 6b and Table S10), matching well-known regulatory roles of IRF1 106,107 and suggesting that IRF1 may affect CD via production of type-I interferon and concomitant cellcycle regulation.…”

Section: Analysis Of 46 Diseases and Complex Traitsmentioning

confidence: 67%

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Reshef

Finucane

Kelley

et al. 2017

Preprint

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Biological interpretation of GWAS data frequently involves analyzing unsigned genomic annotations comprising SNPs involved in a biological process and assessing enrichment for disease signal. However, it is often possible to generate signed annotations quantifying whether each SNP allele promotes or hinders a biological process, e.g., binding of a transcription factor (TF). Directional effects of such annotations on disease risk enable stronger statements about causal mechanisms of disease than enrichments of corresponding unsigned annotations. Here we introduce a new method, signed LD profile regression, for detecting such directional effects using GWAS summary statistics, and we apply the method using 382 signed annotations reflecting predicted TF binding. We show via theory and simulations that our method is well-powered and is well-calibrated even when TF binding sites co-localize with other enriched regulatory elements, which can confound unsigned enrichment methods. We apply our method to 12 molecular traits and recover many known relationships including positive associations between gene expression and genome-wide binding of RNA polymerase II, NF-κB, and several ETS family members, as well as between known chromatin modifiers and their respective chromatin marks. Finally, we apply our method to 46 diseases and complex traits (average N = 289, 617) and identify 77 significant associations at per-trait FDR < 5%, representing 12 independent signals. Our results include a positive association between educational attainment and genome-wide binding of BCL11A, consistent with recent work linking BCL11A hemizygosity to intellectual disability; a negative association between lupus risk and genome-wide binding of CTCF, which has been shown to suppress myeloid differentiation; and a positive association between Crohn's disease (CD) risk and genome-wide binding of IRF1, an immune regulator that lies inside a CD GWAS locus and has eQTLs that increase CD risk. Our method provides a new way to leverage functional data to draw inferences about causal mechanisms of disease.

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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 (2017) and in http://hakyimlab. org/post/vulnerabilities/.…”

Section: Discussionmentioning

confidence: 93%

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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Transcriptome-wide association studies: opportunities and challenges

Cited by 23 publications

References 76 publications

Enhancer redundancy predicts gene pathogenicity and informs complex disease gene discovery

Enhancer redundancy predicts gene pathogenicity and informs complex disease gene discovery

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

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

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