Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies

Feng, Helian; Mancuso, Nicholas; Gusev, Alexander; Majumdar, Årindam; Major, Megan; Paşaniuc, Bogdan; Kraft, Peter

doi:10.1371/journal.pgen.1008973

Cited by 39 publications

(38 citation statements)

References 28 publications

(81 reference statements)

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“…41 We then also employed an analysis using cross-tissue weights computed from GTEx v8 tissues, available as 3 canonical vectors (sCCA1-3) that capture most of the gene expression. 42 All associations reaching a Bonferroni corrected significance threshold corresponding to the number of gene tested (N=14,219, P < 3.52 × 10 −6 ) were deemed statistically significant. As several genes can be associated at the same locus, the TWAS results were subjected to a conditional analysis implemented in FUSION to select genes that remained conditionally independent.…”

Section: Methodsmentioning

confidence: 99%

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

Thibord

Klarin

Brody

et al. 2022

Preprint

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Venous thromboembolism (VTE) is a complex disease with environmental and genetic determinants. We present new cross-ancestry meta-analyzed genome-wide association study (GWAS) results from 30 studies, with replication of novel loci and their characterization through in silico genomic interrogations. In our initial genetic discovery effort that included 55,330 participants with VTE (47,822 European, 6,320 African, and 1,188 Hispanic ancestry), we identified 48 novel associations of which 34 replicated after correction for multiple testing. In our combined discovery-replication analysis (81,669 VTE participants) and ancestry-stratified meta-analyses (European, African and Hispanic), we identified another 44 novel associations, which are new candidate VTE-associated loci requiring replication. In total, across all GWAS meta-analyses, we identified 135 independent genomic loci significantly associated with VTE risk. We also identified 31 novel transcript associations in transcriptome-wide association studies and 8 novel candidate genes with protein QTL Mendelian randomization analyses. In silico interrogations of hemostasis and hematology traits and a large phenome-wide association analysis of the 135 novel GWAS loci provided insights to biological pathways contributing to VTE, indicating that some loci may contribute to VTE through well-characterized coagulation pathways while others provide new data on the role of hematology traits, particularly platelet function. Many of the replicated loci are outside of known or currently hypothesized pathways to thrombosis. In summary, these findings highlight new pathways to thrombosis and provide novel molecules that may be useful in the development of antithrombosis treatments with reduced risk of bleeds.

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Section: Methodsmentioning

confidence: 99%

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

Thibord

Klarin

Brody

et al. 2022

Preprint

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“…There are several limitations in our study. First, we focused on single-gene, single-trait analyses of splicing data, and there are exciting opportunities for methods development and gene discovery in multi-tissue, multi-trait, multi-gene, and cis and trans effects analyses [23,66,67]. Second, we used the GTEx transcriptome data from adult bulk tissues.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Feng at al. [23] proposed to use sparse canonical correlation analysis (sCCA) [24] to directly build multi-tissue gene expression features and then jointly test those sCCA features and single-tissue predicted expressions using the aggregate Cauchy association test (ACAT) [25]. They showed that this sCCA+ACAT approach could be more powerful than S-MultiXcan and UTMOST.…”

Section: Introductionmentioning

confidence: 99%

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

Wei

Chen

et al. 2021

Preprint

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A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which carries as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG’s applications to Alzheimer’s disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.Author summaryWhile genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits, it remains difficult to identify which genes they regulate and in which biological contexts. This interpretation challenge has motivated the development of computational methods to prioritize causal genes at GWAS loci. Most available methods have focused on linking risk variants with differential gene expression. However, genetic control of splicing and expression are comparable in their complex trait risk, and few studies have focused on identifying causal genes using splicing information. To study splicing mediated effects, one important statistical challenge is the large multiple testing burden generated from multidimensional splicing events. In this study, we develop a new approach, MSG, to test the mediating role of splicing variation on complex traits. We integrate multidimensional splicing data using sparse canonocial correlation analysis and then combine evidence for splicing-trait associations across features using a joint test. We show this approach has higher power to identify causal genes using splicing data than current state-of-art methods designed to model multidimensional expression data. We illustrate the benefits of our approach through extensive simulations and applications to real data sets of 14 complex traits.

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“…A wide variety of expression–prediction strategies have been proposed, typically some form of penalized linear regression (Gamazon et al, 2015; Gusev et al, 2019; L. Liu et al, 2021; Nagpal et al, 2019). Some of these strategies build predictors using single‐tissue expression reference panes, and others use multiple‐tissue panels (Barbeira et al, 2019, 2020; Feng et al, 2021; Hu et al, 2019). TWAS methods have also been extended to account for confounding due to colocalization or pleiotropy or differences in expression prediction across ethnicity (Barfield et al, 2018; Bhattacharya et al, 2020; Geoffroy et al, 2020; L. Liu et al, 2021; Mancuso et al, 2019; Mogil et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al, 2021;Nagpal et al, 2019). Some of these strategies build predictors using single-tissue expression reference panes, and others use multiple-tissue panels (Barbeira et al, 2019(Barbeira et al, , 2020Feng et al, 2021;Hu et al, 2019). TWAS methods have also been extended to account for confounding due to colocalization or pleiotropy or differences in expression prediction across ethnicity (Barfield et al, 2018;Bhattacharya et al, 2020;Geoffroy et al, 2020;L.…”

Section: Introductionmentioning

confidence: 99%

Multitrait transcriptome‐wide association study (TWAS) tests

Feng

Mancuso

Paşaniuc

et al. 2021

Genetic Epidemiology

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Multitrait tests can improve power to detect associations between individual single‐nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi‐SNP transcriptome‐wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single‐SNP setting. Thus, established methods for combining single‐SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene‐phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well‐calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single‐trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levels compared with 87 identified through Bonferroni‐corrected single‐trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single‐trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome‐wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.

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Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies

Cited by 39 publications

References 28 publications

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

Multitrait transcriptome‐wide association study (TWAS) tests

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