A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants

Gusev, Alexander; Lawrenson, Kate; Lin, Xianzhi; Lyra, Paulo C.; Kar, Siddhartha; Vavra, Kevin C.; Segato, Felipe; Fonseca, Marcos A. S.; Lee, Janet M.; Pejovic, Tanya; Liu, Gang; Karlan, Beth Y.; Freedman, Matthew L.; Noushmehr, Houtan; Monteiro, Alvaro N.A.; Pharoah, Paul D.P.; Paşaniuc, Bogdan; Gayther, Simon A.

doi:10.1038/s41588-019-0395-x

Cited by 96 publications

(97 citation statements)

References 61 publications

(73 reference statements)

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“…For ovarian cancer, we identified 17 candidate susceptibility genes that were located within 1 Mb of a published lead variant associated at genome-wide significance with ovarian cancer risk (Supplementary Table 5; (23)). Six of these genes have also been reported in a previously published TWAS for ovarian cancer ( Supplementary Table 5; (11,12)). The 17 genes span 5 different genomic regions > 1 Mb apart.…”

Section: Candidate Breast Cancer and Ovarian Cancer Susceptibility Gementioning

confidence: 74%

“…In this study, we used the conditional and conjunctional FDR as a novel tool to systematically improve the power of breast cancer and ovarian cancer candidate susceptibility gene discovery in a PrediXcan-based TWAS. While gene expression prediction models based on multiple tissue types has been the more common approach to improving TWAS power (11,28), the conditional/conjunction FDR approach gains power through the incorporation of multiple related GWAS data sets into a TWAS analysis. We investigated the shared inherited genetic basis of these two cancer types by integrating normal and tumor tissue-specific transcriptomic data sets with largescale genome-wide association meta-analysis findings for susceptibility to breast cancer and ovarian cancer.…”

Section: Discussionmentioning

confidence: 99%

“…PrediXcan is a method developed recently for conducting TWAS (8). TWAS methods have been applied to single cancer types before, including breast cancer (9,10) and ovarian cancer (11,12). Here we present the first application of PrediXcan, and indeed broadly of TWAS, in the pleiotropic cross-cancer setting.…”

Section: Introductionmentioning

confidence: 99%

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Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

Kar¹,

Considine²,

Tyrer³

et al. 2020

Preprint

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Familial, genome-wide association (GWAS), and sequencing studies and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWAS) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through novel application of a pleiotropy-guided conditional/conjunction false discovery rate approach for the first time in the setting of a TWAS. This identified 14 new candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 new candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were > 1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. Overlaying candidate causal risk variants identified by GWAS fine mapping onto expression prediction models for genes at known loci suggested that the association for 55% of these genes was driven by the underlying GWAS signal. SignificanceThe 22 new genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

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Section: Candidate Breast Cancer and Ovarian Cancer Susceptibility Gementioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

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Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

Kar¹,

Considine²,

Tyrer³

et al. 2020

Preprint

Self Cite

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“…We evaluated the colocalization status of a gene by calculating the posterior probability that the genetic and functional associations derived from (i) distinct causal SNPs (PP3), and (ii) a shared causal SNP (PP4) (24). Of the 177 significant features, 101 (57 unique genes) were considered as colocalized based on their high PP4 (> 0.8), in line with previous literature (25,26) ( Supplementary Table S3).…”

Section: Colocalizationmentioning

confidence: 96%

Delineating the Genetic Component of Gene Expression in Major Depression

Tiemeier

Lewis

Pain

2020

Preprint

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Background: Major Depression (MD) is determined by a multitude of factors including genetic risk variants which regulate gene expression (GE). Here, we examined the genetic component of GE in MD by performing a Transcriptome-Wide Association Study (TWAS), inferring GE-trait relationships from genetic, transcriptomic and phenotypic information. Method: Genes differentially expressed in depression were identified with the TWAS FUSION method, based on summary statistics from the largest genome-wide association analysis of MD (N cases = 135,458) and GE levels from 20 tissue datasets. Follow-up analyses were performed to extensively characterize the identified associations: colocalization, conditional, and fine-mapping analyses together with functionally-enriched pathway investigations. Results: Transcriptome-wide significant GE differences between cases and controls were found at 91 genes, 50 of which were not found in previous MD TWASs. Of the 91 significant genes, eight represented strong, colocalized, and potentially causal associations with depression, which were independent from the effect of nearby genes. Such "high-confidence associations" include NEGR1, CTC-467M3.3, TMEM106B, CTD-2298J14.2, CCDC175, ESR2, PROX2, ZC3H7B. Lastly, TWAS-based enrichment analysis highlighted dysregulation of gene sets for long term potentiation, dendritic shaft, and memory processes in MD. Conclusion: This study has shed light on the genetic component of GE in depression by characterizing the identified associations, unravelling novel risk genes, and determining which associations are congruent with a causal model. These findings can be used as a resource for prioritizing and designing subsequent functional studies of MD.

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“…whether or not SNPs are eQTLs) [17][18][19][20] . Application of these methods to eQTL and GWAS data has shown that many genes have eQTLs that colocalize with GWAS loci [6][7][8][9][10] and/or exhibit significant cis-genetic correlations between their expression and trait [11][12][13][14][15][16][21][22][23][24][25][26][27][28] , while also showing that eQTLs as a whole are significantly enriched for disease heritability [17][18][19][20] .…”

Section: Introductionmentioning

confidence: 99%

Quantifying genetic effects on disease mediated by assayed gene expression levels

Yao

O’Connor

Price

et al. 2019

Preprint

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Disease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs). However, it remains unclear whether this overlap is driven by mediation of genetic effects on disease by expression levels, or whether it primarily reflects pleiotropic relationships instead. Here we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis-genetic component of assayed steady-state gene expression levels, using summary association statistics from GWAS and eQTL studies. We show that MESC produces robust estimates of expression-mediated heritability across a wide range of simulations. We applied MESC to GWAS summary statistics for 42 diseases and complex traits (average N = 323K) and cis-eQTL data across 48 tissues from the GTEx consortium. We determined that a statistically significant but low proportion of disease heritability (mean estimate 11% with S.E. 2%) is mediated by the cis-genetic component of assayed gene expression levels, with substantial variation across diseases (point estimates from 0% to 38%). We further partitioned expression-mediated heritability across various gene sets. We observed an inverse relationship between cis-heritability of expression and disease heritability mediated by expression, suggesting that genes with weaker eQTLs have larger causal effects on disease. Moreover, we observed broad patterns of expression-mediated heritability enrichment across functional gene sets that implicate specific gene sets in disease, including loss-of-function intolerant genes and FDA-approved drug targets. Our results demonstrate that eQTLs estimated from steady-state expression levels in bulk tissues are informative of regulatory disease mechanisms, but that such eQTLs are insufficient to explain the majority of disease heritability. Instead, additional assays are necessary to more fully capture the regulatory effects of GWAS variants.

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A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants

Cited by 96 publications

References 61 publications

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies new candidate susceptibility genes for breast and ovarian cancer

Delineating the Genetic Component of Gene Expression in Major Depression

Quantifying genetic effects on disease mediated by assayed gene expression levels

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