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Head ST, Dezem F, Todor A, Yang J, Plummer J, Gayther S, Kar S, Schildkraut J, Epstein MP. Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia. Am J Hum Genet 2024; 111:1084-1099. [PMID: 38723630 PMCID: PMC11179407 DOI: 10.1016/j.ajhg.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 04/11/2024] [Accepted: 04/16/2024] [Indexed: 05/21/2024] Open
Abstract
Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.
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Affiliation(s)
- S Taylor Head
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Felipe Dezem
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Andrei Todor
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jingjing Yang
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jasmine Plummer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Simon Gayther
- Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Siddhartha Kar
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Joellen Schildkraut
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
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2
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Wang L, Babushkin N, Liu Z, Liu X. Trans-eQTL mapping in gene sets identifies network effects of genetic variants. CELL GENOMICS 2024; 4:100538. [PMID: 38565144 PMCID: PMC11019359 DOI: 10.1016/j.xgen.2024.100538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/08/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.
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Affiliation(s)
- Lili Wang
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Nikita Babushkin
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Zhonghua Liu
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Xuanyao Liu
- The Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
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3
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Bunyavanich S, Becker PM, Altman MC, Lasky-Su J, Ober C, Zengler K, Berdyshev E, Bonneau R, Chatila T, Chatterjee N, Chung KF, Cutcliffe C, Davidson W, Dong G, Fang G, Fulkerson P, Himes BE, Liang L, Mathias RA, Ogino S, Petrosino J, Price ND, Schadt E, Schofield J, Seibold MA, Steen H, Wheatley L, Zhang H, Togias A, Hasegawa K. Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop. J Allergy Clin Immunol 2024; 153:954-968. [PMID: 38295882 PMCID: PMC10999353 DOI: 10.1016/j.jaci.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 02/29/2024]
Abstract
Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.
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Affiliation(s)
| | - Patrice M Becker
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | | | - Jessica Lasky-Su
- Brigham & Women's Hospital and Harvard Medical School, Boston, Mass
| | | | | | | | | | - Talal Chatila
- Boston Children's Hospital and Harvard Medical School, Boston, Mass
| | | | | | | | - Wendy Davidson
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Gang Dong
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Gang Fang
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - Patricia Fulkerson
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | | | - Liming Liang
- Harvard T. H. Chan School of Public Health, Boston, Mass
| | | | - Shuji Ogino
- Brigham & Women's Hospital and Harvard Medical School, Boston, Mass; Harvard T. H. Chan School of Public Health, Boston, Mass; Broad Institute of MIT and Harvard, Boston, Mass
| | | | | | - Eric Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Max A Seibold
- National Jewish Health, Denver, Colo; University of Colorado School of Medicine, Aurora, Colo
| | - Hanno Steen
- Boston Children's Hospital and Harvard Medical School, Boston, Mass
| | - Lisa Wheatley
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Hongmei Zhang
- School of Public Health, University of Memphis, Memphis, Tenn
| | - Alkis Togias
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Kohei Hasegawa
- Massachusetts General Hospital and Harvard Medical School, Boston, Mass
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Wittich H, Ardlie K, Taylor KD, Durda P, Liu Y, Mikhaylova A, Gignoux CR, Cho MH, Rich SS, Rotter JI, Manichaikul A, Im HK, Wheeler HE. Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits. Am J Hum Genet 2024; 111:445-455. [PMID: 38320554 PMCID: PMC10940016 DOI: 10.1016/j.ajhg.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 02/08/2024] Open
Abstract
Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.
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Affiliation(s)
- Henry Wittich
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Anna Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Chris R Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Heather E Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA; Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA.
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Akinbiyi T, McPeek MS, Abney M. ADELLE: A global testing method for Trans-eQTL mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.24.581871. [PMID: 38464248 PMCID: PMC10925110 DOI: 10.1101/2024.02.24.581871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Understanding the genetic regulatory mechanisms of gene expression is a challenging and ongoing problem. Genetic variants that are associated with expression levels are readily identified when they are proximal to the gene (i.e., cis-eQTLs), but SNPs distant from the gene whose expression levels they are associated with (i.e., trans-eQTLs) have been much more difficult to discover, even though they account for a majority of the heritability in gene expression levels. A major impediment to the identification of more trans-eQTLs is the lack of statistical methods that are powerful enough to overcome the obstacles of small effect sizes and large multiple testing burden of trans-eQTL mapping. Here, we propose ADELLE, a powerful statistical testing framework that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that ADELLE is more powerful than other methods at detecting SNPs that are associated with 0.2-2% of the traits. We apply ADELLE to a mouse advanced intercross line data set and show its ability to find trans-eQTLs that were not significant under a standard analysis. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.
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Affiliation(s)
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, IL, US
- Department of Human Genetics, The University of Chicago, Chicago, IL, US
| | - Mark Abney
- Department of Human Genetics, The University of Chicago, Chicago, IL, US
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Head ST, Dezem F, Todor A, Yang J, Plummer J, Gayther S, Kar S, Schildkraut J, Epstein MP. Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.09.566218. [PMID: 38014246 PMCID: PMC10680675 DOI: 10.1101/2023.11.09.566218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Transcriptome-wide association studies (TWAS) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have only considered regulatory effects of risk associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWAS of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents a first look into the role of trans-eQTLs in the complex molecular mechanisms underlying these diseases.
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Affiliation(s)
- S. Taylor Head
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Felipe Dezem
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Andrei Todor
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jingjing Yang
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Jasmine Plummer
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Simon Gayther
- Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Siddhartha Kar
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Joellen Schildkraut
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Michael P. Epstein
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA
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7
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Iakovliev A, McGurnaghan SJ, Hayward C, Colombo M, Lipschutz D, Spiliopoulou A, Colhoun HM, McKeigue PM. Genome-wide aggregated trans-effects on risk of type 1 diabetes: A test of the "omnigenic" sparse effector hypothesis of complex trait genetics. Am J Hum Genet 2023; 110:913-926. [PMID: 37164005 PMCID: PMC10257008 DOI: 10.1016/j.ajhg.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/12/2023] [Indexed: 05/12/2023] Open
Abstract
The "omnigenic" hypothesis postulates that the polygenic effects of common SNPs on a typical complex trait are mediated through trans-effects on expression of a relatively sparse set of effector ("core") genes. We tested this hypothesis in a study of 4,964 cases of type 1 diabetes (T1D) and 7,497 controls by using summary statistics to calculate aggregated (excluding the HLA region) trans-scores for gene expression in blood. From associations of T1D with aggregated trans-scores, nine putative core genes were identified, of which three-STAT1, CTLA4 and FOXP3-are genes in which variants cause monogenic forms of autoimmune diabetes. Seven of these genes affect the activity of regulatory T cells, and two are involved in immune responses to microbial lipids. Four T1D-associated genomic regions could be identified as master regulators via trans-effects on gene expression. These results support the sparse effector hypothesis and reshape our understanding of the genetic architecture of T1D.
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Affiliation(s)
- Andrii Iakovliev
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland
| | - Stuart J McGurnaghan
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Caroline Hayward
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Marco Colombo
- University of Leipzig, Medical Faculty, University Hospital for Children and Adolescents, Center for Pediatric Research, Leipzig, Germany
| | - Debby Lipschutz
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Athina Spiliopoulou
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland
| | - Helen M Colhoun
- Institute of Genetics and Cancer, College of Medicine and Veterinary Medicine, University of Edinburgh, Western General Hospital Campus, Crewe Road, Edinburgh EH4 2XUC, Scotland
| | - Paul M McKeigue
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, Scotland.
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Dutta D, Sen A, Satagopan J. Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations. PLoS One 2022; 17:e0276886. [PMID: 36584096 PMCID: PMC9803132 DOI: 10.1371/journal.pone.0276886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 10/16/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Copy number aberrations (CNAs) in cancer affect disease outcomes by regulating molecular phenotypes, such as gene expressions, that drive important biological processes. To gain comprehensive insights into molecular biomarkers for cancer, it is critical to identify key groups of CNAs, the associated gene modules, regulatory modules, and their downstream effect on outcomes. METHODS In this paper, we demonstrate an innovative use of sparse canonical correlation analysis (sCCA) to effectively identify the ensemble of CNAs, and gene modules in the context of binary and censored disease endpoints. Our approach detects potentially orthogonal gene expression modules which are highly correlated with sets of CNA and then identifies the genes within these modules that are associated with the outcome. RESULTS Analyzing clinical and genomic data on 1,904 breast cancer patients from the METABRIC study, we found 14 gene modules to be regulated by groups of proximally located CNA sites. We validated this finding using an independent set of 1,077 breast invasive carcinoma samples from The Cancer Genome Atlas (TCGA). Our analysis of 7 clinical endpoints identified several novel and interpretable regulatory associations, highlighting the role of CNAs in key biological pathways and processes for breast cancer. Genes significantly associated with the outcomes were enriched for early estrogen response pathway, DNA repair pathways as well as targets of transcription factors such as E2F4, MYC, and ETS1 that have recognized roles in tumor characteristics and survival. Subsequent meta-analysis across the endpoints further identified several genes through the aggregation of weaker associations. CONCLUSIONS Our findings suggest that sCCA analysis can aggregate weaker associations to identify interpretable and important genes, modules, and clinically consequential pathways.
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Affiliation(s)
- Diptavo Dutta
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, United States of America
- Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
- * E-mail: ,
| | - Ananda Sen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America
- Department of Family Medicine, University of Michigan, Ann Arbor, MI, United States of America
| | - Jaya Satagopan
- Department of Biostatistics and Epidemiology, Rutgers University, New Brunswick, NJ, United States of America
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