Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

Mancuso, Nicholas; Gayther, Simon A.; Gusev, Alexander; Wang, Zheng; Penney, Kathryn L.; Kóte-Jarai, Zsofia; Eeles, Rosalind; Freedman, Matthew L.; Haiman, Christopher A.; Paşaniuc, Bogdan; Henderson, Brian E.; Benlloch, Sara; Schumacher, Fredrick R.; Olama, Ali Amin Al; Muir, Kenneth; Berndt, Sonja I.; Conti, David V.; Wiklund, Fredrik; Chanock, Stephen J.; Stevens, Victoria L.; Tangen, Catherine M.; Batra, Jyotsna; Clements, Judith A.; Grönberg, Henrik; Pashayan, Nora; Schleutker, Johanna; Albanês, Demetrius; Weinstein, Stephanie J.; Wolk, Alicja; West, Catharine; Mucci, Lorelei A.; Cancel‐Tassin, Géraldine; Koutros, Stella; Sørensen, Karina Dalsgaard; Mæhle, Lovise; Neal, David E.; Hamdy, Freddie C.; Donovan, Jenny; Travis, Ruth C.; Hamilton, Robert J.; Ingles, Sue A.; Rosenstein, Barry S.; Lu, Yong‐Jie; Giles, Graham G.; Kibel, Adam S.; Vega, Ana; Kogevinas, Manolis; Park, Jong Y.; Stanford, Janet L.; Cybulski, Cezary; Nordestgaard, Børge G.; Brenner, Hermann; Maier, Christiane; Kim, Jeri; John, Esther M.; Teixeira, Manuel R.; Neuhausen, Susan L.; Ruyck, Kim De; Razack, Azad Hassan Abdul; Newcomb, Lisa F.; Lessel, Davor; Kaneva, Radka; Usmani, Nawaid; Claessens, Frank; Townsend, Paul A.; Gago‐Dominguez, Manuela; Roobol, Monique J.; Ménégaux, Florence; Khaw, Kay‐Tee; Cannon-Albright, Lisa; Pandha, Hardev; Thibodeau, Stephen N.; Hunter, David J.; Kraft, Peter

doi:10.1038/s41467-018-06302-1

Cited by 139 publications

(121 citation statements)

References 59 publications

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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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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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“…To further illustrate the benefit of hJAM , we next considered the gene-prostate cancer risk association of two genes on chromosome 1q32.1, gene PM20D1 (Peptidase M20 Domain Containing 1) and gene NUCKS1 (Nuclear Casein Kinase and Cyclin Dependent Kinase Substrate 1). Both PM20D1 and NUCKS1 are protein coding genes and previous transcriptome studies have found a significant effect of both PM20D1 and NUCKS1 on the risk of prostate cancer among a European-ancestry population 34, 35 . Due to the close proximity of the two genes along the genome, there is a potential for a univariate approach to result in biased estimates.…”

Section: Data Examplesmentioning

confidence: 87%

A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis

Jiang

Mancuso

et al. 2020

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Background: Previous research has demonstrated the usefulness of hierarchical modeling for incorporating a flexible array of prior information in genetic association studies. When this prior information consists of effect estimates from association analyses of single nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a two-stage instrumental or transcriptome-wide association study (TWAS) analysis, respectively. Methods:We propose to extend our previous approach for the joint analysis of marginal summary statistics (JAM) to incorporate prior information via a hierarchical model (hJAM). In this framework, the use of appropriate effect estimates as prior information yields an analysis similar to Mendelian Randomization (MR) and TWAS approaches such as FUSION and S-PrediXcan. However, hJAM is applicable to multiple correlated SNPs and multiple correlated intermediates to yield conditional estimates of effect for the intermediate on the outcome, thus providing advantages over alternative approaches. Results:We investigate the performance of hJAM in comparison to existing MR approaches (inverse-variance weighted MR and multivariate MR) and existing TWAS approaches (S-PrediXcan) for effect estimation, type-I error and empirical power. We apply hJAM to two examples: estimating the conditional effects of body mass index and type 2 diabetes on myocardial infarction and estimating the effects of the expressions of gene NUCKS1 and PM20D1 on the risk of prostate cancer. Conclusions:Across numerous causal simulation scenarios, we demonstrate that hJAM is unbiased, maintains correct type-I error and has increased power. Key Messages:• Mendelian randomization and transcriptome-wide association studies (TWAS) can be viewed as similar approaches via a hierarchical model. • The hierarchal joint analysis of marginal summary statistics (hJAM) is a multivariateMendelian randomization approach which offers a simple way to address the pleiotropy bias that is introduced by genetic variants associated with multiple risk factors or expressions of genes.• hJAM incorporates the linkage disequilibrium structure of the single nucleotide polymorphism (SNPs) in a reference population to account for the correlation between SNPs.• In addition to Mendelian randomization and TWAS, hJAM offers flexibility to incorporate functional or genomic annotation or information from metabolomic studies.(3) assuming there is no direct effect from the genetic variants to the outcome (i.e.The estimate of ߨ from Eq. 3 is equivalent to the result from the two-stage least square (2SLS) regression, which is employed by PrediXcan 15 and others 16 . The prior information ‫ܣ‬ መ is the association estimates between the genetic variants and the intermediate and can be applied to impute the intermediate with the genetic variants:Similar to above, we then fit a hierarchical model by incorporating the second-stage model (Eq.2) into Eq.5 and construct the hJAM model as

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“…Often, an implicated GWAS locus contains many genes. Common GWAS mapping techniques would assign the SNP association to the closest gene, however, this has been shown to be suboptimal [12][13][14] . After conditioning on the TWAS signal for each transcriptome-wide significant hit, most of the signal was explained by the expression of the conditioned gene.…”

Section: Discussionmentioning

confidence: 99%

Multi-tissue probabilistic fine-mapping of transcriptome-wide association study identifies cis-regulated genes for miserableness

Liao

Vuokila

et al. 2019

Preprint

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Short summary: The first transcriptome-wide association study of miserableness identifies many genes including c7orf50 implicated in the trait.Word count: 1,522 excluding abstract and references Tables: 3 Abstract (141 words) Miserableness is a behavioural trait that is characterized by strong negative feelings in an individual. Although environmental factors tend to invoke miserableness, it is common to feel miserable 'for no reason', suggesting an innate, potential genetic component. Currently, little is known about the functional relevance of common variants associated with miserableness. To further characterize the trait, we conducted a transcriptome-wide association study (TWAS) on 373,733 individuals and identified 104 signals across brain tissue panels with 37 unique genes.Subsequent probabilistic fine-mapping prioritized 95 genes into 90%-credible sets. Amongst these prioritized hits, C7orf50 had the highest posterior inclusion probability of 0.869 in the brain cortex. Furthermore, we demonstrate that many GWAS hits for miserableness are driven by expression. To conclude, we successfully identified several genes implicated in miserableness and highlighted the power of TWAS to prioritize genes associated with a trait.

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Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

Cited by 139 publications

References 59 publications

Quantifying genetic effects on disease mediated by assayed gene expression levels

Quantifying genetic effects on disease mediated by assayed gene expression levels

A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis

Multi-tissue probabilistic fine-mapping of transcriptome-wide association study identifies cis-regulated genes for miserableness

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