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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations