Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expression quantitative trait locus (eQTL) data used to train the genetic predictors have small sample sizes, or when data from causally relevant tissues are not available. Here, we propose to address these issues by integrating multiple tissues in the TWAS using sparse canonical correlation analysis (sCCA). We show that sCCA-TWAS combined with single-tissue TWAS using an aggregate Cauchy association test (ACAT) outperforms traditional single-tissue TWAS. In empirically motivated simulations, the sCCA+ACAT approach yielded the highest power to detect a gene associated with phenotype, even when expression in the causal tissue was not directly measured, while controlling the Type I error when there is no association between gene expression and phenotype. For example, when gene expression explains 2% of the variability in outcome, and the GWAS sample size is 20,000, the average power difference between the ACAT combined test of sCCA features and single-tissue, versus single-tissue combined with Generalized Berk-Jones (GBJ) method, single-tissue combined with S-MultiXcan, UTMOST, or summarizing cross-tissue expression patterns using Principal Component Analysis (PCA) approaches was 5%, 8%, 5% and 38%, respectively. The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. When applied to publicly available summary statistics from 10 complex traits, the sCCA+ACAT test was able to increase the number of testable genes and identify on average an additional 400 additional gene-trait associations that single-trait TWAS missed. Our results suggest that aggregating eQTL data across multiple tissues using sCCA can improve the sensitivity of TWAS while controlling for the false positive rate.

Dupuytren's disease is a common inherited tissue‐specific fibrotic disorder, characterized by progressive and irreversible fibroblastic proliferation affecting the palmar fascia of the hand. Although genome‐wide association study (GWAS) have identified 24 genomic regions associated with Dupuytrens risk, the biological mechanisms driving signal at these regions remain elusive. We identify potential biological mechanisms for Dupuytren's disease by integrating the most recent, largest GWAS (3,871 cases and 4,686 controls) with eQTLs (47 tissue panels from five consortia, total n = 3,975) to perform a transcriptome‐wide association study. We identify 43 tissue‐specific gene associations with Dupuytren's risk, including one in a novel risk region. We also estimate the genome‐wide genetic correlation between Dupuytren's disease and 45 complex traits and find significant genetic correlations between Dupuytren's disease and body mass index (BMI), type II diabetes, triglycerides, and high‐density lipoprotein (HDL), suggesting a shared genetic etiology between these traits. We further examine local genetic correlation to identify 8 and 3 novel regions significantly correlated with BMI and HDL respectively. Our results are consistent with previous epidemiological findings showing that lower BMI increases risk for Dupuytren's disease. These 12 novel risk regions provide new insight into the biological mechanisms of Dupuytren's disease and serve as a starting point for functional validation.

AbstractTranscriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expression quantitative trait locus (eQTL) data used to train the genetic predictors have small sample sizes, or when data from causally relevant tissues are not available. Here, we propose to address these issues by integrating multiple tissues in the TWAS using sparse canonical correlation analysis (sCCA). We show that sCCA-TWAS combined with single-tissue TWAS using an aggregate Cauchy association test (ACAT) outperforms traditional single-tissue TWAS. In empirically motivated simulations, the sCCA+ACAT approach yielded the highest power to detect a gene associated with phenotype, even when expression in the causal tissue was not directly measured, while controlling the Type I error when there is no association between gene expression and phenotype. For example, when gene expression explains 2% of the variability in outcome, and the GWAS sample size is 20,000, the average power difference between the ACAT combined test of sCCA features and single-tissue, versus single-tissue combined with Generalized Berk-Jones (GBJ) method, single-tissue combined with S-MultiXcan or summarizing cross-tissue expression patterns using Principal Component Analysis (PCA) approaches was 5%, 8%, and 38%, respectively. The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. When applied to publicly available summary statistics from 10 complex traits, the sCCA+ACAT test was able to increase the number of testable genes and identify on average an additional 400 additional gene-trait associations that single-trait TWAS missed. Our results suggest that aggregating eQTL data across multiple tissues using sCCA can improve the sensitivity of TWAS while controlling for the false positive rate.Author summaryTranscriptome-wide association studies (TWAS) can improve the statistical power of genetic association studies by leveraging the relationship between genetically predicted transcript expression levels and an outcome. We propose a new TWAS pipeline that integrates data on the genetic regulation of expression levels across multiple tissues. We generate cross-tissue expression features using sparse canonical correlation analysis and then combine evidence for expression-outcome association across cross- and single-tissue features using the aggregate Cauchy association test. We show that this approach has substantially higher power than traditional single-tissue TWAS methods. Application of these methods to publicly available summary statistics for ten complex traits also identifies associations missed by single-tissue methods.

1Dupuytren's disease is the common inherited tissue-specific fibrotic disorder. It's characterized 2 by progressive and irreversible fibroblastic proliferation affecting the palmar fascia of the hand, 3 with an onset typically in the sixth decade of life. Although genome-wide association studies 4 (GWAS) have identified 24 genomic regions associated with Dupuytren's risk, the biological 5 mechanisms driving signal at these regions remain elusive. We identify potential biological 6 mechanisms for Dupuytren's disease by integrating the most recent, largest GWAS (݊ ௦ ௦ ൌ 7 ି ସ ), which suggests a shared genetic etiology. We further 15 refine the genome-wide genetic correlation signal to identify 8 regions significantly negatively 16 correlated with BMI and 3 regions significantly correlated (1 positively and 2 negatively 17 correlated) with HDL; none of these regions contained the novel gene association identified by 18 TWAS. Our results are consistent with previous epidemiological findings which show that lower 19 BMI increases risk for Dupuytren's disease. These 12 novel risk regions provide new insight into 20 the biological mechanisms of Dupuytren's disease and serve as a starting point for functional 21 validation. 22