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Grant Details

Grant Number: 5R01CA258808-02 Interpret this number
Primary Investigator: Mancuso, Nicholas
Organization: University Of Southern California
Project Title: An Integrative Multi-Omics Approach to Characterize Prostate Cancer Risk in Diverse Populations
Fiscal Year: 2022


PROJECT SUMMARY In the US, prostate cancer (PCa) is the second leading cause of cancer death in men, with men of African ancestry having the highest incidence and mortality rates. Indeed, men of African ancestry who develop PCa have more aggressive and lethal prostate tumors on average, compared to their non-African ancestry counterparts. While the reasons for this health disparity are unknown, evidence suggests that genetics is likely a contributing factor. Indeed, large-scale genome-wide association studies (GWAS) of PCa have identified 300 genomic risk variants; however, the vast majority are in non-coding regions, which makes identifying the proximal target gene challenging and hinders translational efforts. A large body of works have demonstrated that PCa risk is highly enriched in functional regions of the genome, which indicates that risk is mediated through perturbed regulatory action on relevant susceptibility genes. Multiple lines of evidence have shown that integrating omics with large-scale genetic data increases statistical power to identify novel genomic risk regions and uncovers target molecular mechanisms of risk. These analyses rely on first identifying associations between genetics and various omics data (i.e., molecular quantitative trait loci, or molQTLs) and then using these associations to impute or predict omics into large-scale PCa GWAS data. However, to date, analyses have been limited for three primary reasons. First, previous integrative analyses with PCa risk relied on diverse omics data measured across tissues other than prostate, where translation to prostate-specific results may be inaccurate. Previous omics datasets measured in prostate together with genotype have been limited to small sample sizes, resulting in less accurate prediction when compared with larger sample size datasets. Second, prior omics datasets have been measured primarily in men of European ancestry. Multiple recent works find that genetic-based omics prediction translates poorly across populations, which limits the utility of existing omics data to non-European men. Third, previous studies have shown the importance of integrating omics data beyond gene expression with PCa risk, thus demonstrating that multi-omics investigations facilitate a more unbiased approach to provide biological insights into disease mechanisms. To date, the majority of imputation-based approaches have been applied to large- scale GWAS, however recent works have made crucial discoveries in cancer biology by imputing cancer risk from GWAS into molecular cohorts. Here, to understand the genetic regulatory mechanisms in prostate tissues across the molecular cascade, we propose to assay methylation, transcriptomic, proteomic, and metabolomic data in prostate tissue to perform large-scale molQTL mapping for African- and European-ancestry men. To elucidate the underlying mechanisms responsible for PCa risk and identify novel genetic risk factors, we will integrate identified molQTLs with the largest-available PCa GWAS. Overall, our proposal aims to characterize the genetic regulatory landscape of prostate tissue, its effect on PCa risk, and health disparities of this disease.


Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.
Authors: Lu Z. , Wang X. , Carr M. , Kim A. , Gazal S. , Mohammadi P. , Wu L. , Gusev A. , Pirruccello J. , Kachuri L. , et al. .
Source: medRxiv : the preprint server for health sciences, 2024-04-16; , .
EPub date: 2024-04-16.
PMID: 38699369
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A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics.
Authors: Zhang Z. , Jung J. , Kim A. , Suboc N. , Gazal S. , Mancuso N. .
Source: American journal of human genetics, 2023-11-02; 110(11), p. 1863-1874.
EPub date: 2023-10-24.
PMID: 37879338
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twas_sim, a Python-based tool for simulation and power analysis of transcriptome-wide association analysis.
Authors: Wang X. , Lu Z. , Bhattacharya A. , Pasaniuc B. , Mancuso N. .
Source: Bioinformatics (Oxford, England), 2023-05-04; 39(5), .
PMID: 37099718
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A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics.
Authors: Zhang Z. , Jung J. , Kim A. , Suboc N. , Gazal S. , Mancuso N. .
Source: medRxiv : the preprint server for health sciences, 2023-03-29; , .
EPub date: 2023-03-29.
PMID: 37034739
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H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility.
Authors: Giambartolomei C. , Seo J.H. , Schwarz T. , Freund M.K. , Johnson R.D. , Spisak S. , Baca S.C. , Gusev A. , Mancuso N. , Pasaniuc B. , et al. .
Source: American journal of human genetics, 2021-12-02; 108(12), p. 2284-2300.
EPub date: 2021-11-24.
PMID: 34822763
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