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Grant Details

Grant Number: 5R01CA242929-02 Interpret this number
Primary Investigator: Gao, Guimin
Organization: University Of Chicago
Project Title: Transcriptome-Wide Association Studies and Genetic Risk Prediction for Breast Cancer Integrating Rna Splicing and Gene Expression From Multiple Tissues
Fiscal Year: 2020


Abstract

ABSTRACT Breast cancer is the most common cancer in women in the United States and worldwide. Although genome-wide association studies have identified multiple loci for breast cancer, most of heritability is still hidden. To date, transcriptome-wide association studies (TWAS) have been performed to quantify associations of genetically predicted gene expression with breast cancer risk. Our recent work showed that genetic variants that affect RNA splicing are very important contributors to complex traits but were previously missed when considering the genetic effects on gene expression only. Therefore, evaluating associations of genetically predicted splicing (as a linear combination of SNPs) with phenotypes has a great promise to discover novel putative candidate disease genes. Splicing events in local regions (such as intron excision clusters) can be highly correlated. However, existing statistical methods for TWAS do not account for correlation among splicing events, and thus may result in loss of power in detecting disease genes. Additionally, splicing levels (quantified as relative count ratios) in a gene and the overall gene expression level have not been considered together in previous gene mapping methods. For breast cancer prevention, stratification of women according to the risk of developing the cancer could improve risk reduction and screening strategies by targeting those most likely to benefit. SNP-based polygenic risk scores have been developed to predict breast cancer but their prediction accuracy remains low. To increase prediction accuracy, there is a need to incorporate useful information from genetically predicted expression and splicing. Recently, several transcriptome studies, such as GTEx, have collected DNA and RNA from multiple tissue samples; integrating information across multiple tissues into TWAS could significantly improve the identification of disease genes. In addition, African Americans (AAs) have different linkage disequilibrium (LD) pattern from Europeans, so genetic variants that affect RNA splicing and disease phenotypes could be ethnicity-specific. The objective of this study is to develop effective methods for gene mapping and genetic risk prediction of complex traits such as breast cancer by integrating multi–omics data from multiple tissues. Specifically, we will 1) develop methods for TWAS that leverage information of RNA splicing and expression from multiple tissues and apply the methods to identify novel breast cancer susceptibility genes; 2) develop joint polygenic risk prediction scores for breast cancer that model different LD patterns in distinct populations (including AAs) and incorporate information of genetically predicted splicing and gene expression from multiple tissues. We will account for correlation among splicing events in local regions and across multiple tissues. We expect that the proposed methods have higher power in gene mapping or higher accuracy in prediction of breast cancer than existing methods. The proposed methods can also be applied to other complex diseases.



Publications

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer.
Authors: Gao G. , McClellan J. , Barbeira A.N. , Fiorica P.N. , Li J.L. , Mu Z. , Olopade O.I. , Huo D. , Im H.K. .
Source: American Journal Of Human Genetics, 2024-05-08 00:00:00.0; , .
EPub date: 2024-05-08 00:00:00.0.
PMID: 38733992
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Using genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes.
Authors: Ping J. , Jia G. , Cai Q. , Guo X. , Tao R. , Ambrosone C. , Huo D. , Ambs S. , Barnard M.E. , Chen Y. , et al. .
Source: Nature Communications, 2024-05-02 00:00:00.0; 15(1), p. 3718.
EPub date: 2024-05-02 00:00:00.0.
PMID: 38697998
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Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction.
Authors: Jia G. , Ping J. , Guo X. , Yang Y. , Tao R. , Li B. , Ambs S. , Barnard M.E. , Chen Y. , Garcia-Closas M. , et al. .
Source: Nature Genetics, 2024 May; 56(5), p. 819-826.
EPub date: 2024-05-13 00:00:00.0.
PMID: 38741014
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Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status.
Authors: McClellan J.C. , Li J.L. , Gao G. , Huo D. .
Source: Breast Cancer Research : Bcr, 2024-03-21 00:00:00.0; 26(1), p. 51.
EPub date: 2024-03-21 00:00:00.0.
PMID: 38515142
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Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer.
Authors: Li J.L. , McClellan J.C. , Zhang H. , Gao G. , Huo D. .
Source: Journal Of The National Cancer Institute, 2024-02-23 00:00:00.0; , .
EPub date: 2024-02-23 00:00:00.0.
PMID: 38400758
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Novel breast cancer susceptibility loci under linkage peaks identified in African ancestry consortia.
Authors: Ochs-Balcom H.M. , Preus L. , Du Z. , Elston R.C. , Teerlink C.C. , Jia G. , Guo X. , Cai Q. , Long J. , Ping J. , et al. .
Source: Human Molecular Genetics, 2024-01-23 00:00:00.0; , .
EPub date: 2024-01-23 00:00:00.0.
PMID: 38263910
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On the problem of inflation in transcriptome-wide association studies.
Authors: Liang Y. , Nyasimi F. , Im H.K. .
Source: Biorxiv : The Preprint Server For Biology, 2023-10-20 00:00:00.0; , .
EPub date: 2023-10-20 00:00:00.0.
PMID: 37904952
Related Citations

A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes.
Authors: Gao G. , Fiorica P.N. , McClellan J. , Barbeira A.N. , Li J.L. , Olopade O.I. , Im H.K. , Huo D. .
Source: American Journal Of Human Genetics, 2023-05-03 00:00:00.0; , .
EPub date: 2023-05-03 00:00:00.0.
PMID: 37164006
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An enhancer variant associated with breast cancer susceptibility in Black women regulates TNFSF10 expression and antitumor immunity in triple-negative breast cancer.
Authors: Han Y.J. , Zhang J. , Hardeman A. , Liu M. , Karginova O. , Romero R. , Khramtsova G.F. , Zheng Y. , Huo D. , Olopade O.I. .
Source: Human Molecular Genetics, 2023-01-01 00:00:00.0; 32(1), p. 139-150.
PMID: 35930348
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Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach.
Authors: Gao G. , Zhao F. , Ahearn T.U. , Lunetta K.L. , Troester M.A. , Du Z. , Ogundiran T.O. , Ojengbede O. , Blot W. , Nathanson K.L. , et al. .
Source: Human Molecular Genetics, 2022-05-12 00:00:00.0; , .
EPub date: 2022-05-12 00:00:00.0.
PMID: 35554533
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Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries.
Authors: Liang Y. , Pividori M. , Manichaikul A. , Palmer A.A. , Cox N.J. , Wheeler H.E. , Im H.K. .
Source: Genome Biology, 2022-01-13 00:00:00.0; 23(1), p. 23.
EPub date: 2022-01-13 00:00:00.0.
PMID: 35027082
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Cross-ancestry GWAS meta-analysis identifies six breast cancer loci in African and European ancestry women.
Authors: Adedokun B. , Du Z. , Gao G. , Ahearn T.U. , Lunetta K.L. , Zirpoli G. , Figueroa J. , John E.M. , Bernstein L. , Zheng W. , et al. .
Source: Nature Communications, 2021-07-07 00:00:00.0; 12(1), p. 4198.
EPub date: 2021-07-07 00:00:00.0.
PMID: 34234117
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The impact of cell type and context-dependent regulatory variants on human immune traits.
Authors: Mu Z. , Wei W. , Fair B. , Miao J. , Zhu P. , Li Y.I. .
Source: Genome Biology, 2021-04-29 00:00:00.0; 22(1), p. 122.
EPub date: 2021-04-29 00:00:00.0.
PMID: 33926512
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Circulating Insulin-Like Growth Factor-1 and Risk of Total and 19 Site-specific Cancers: Cohort Study Analyses from the UK Biobank.
Authors: Qian F. , Huo D. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2020-08-20 00:00:00.0; , .
EPub date: 2020-08-20 00:00:00.0.
PMID: 32856611
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