A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes
Copyright © 2023 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved..
Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:110 |
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| Enthalten in: |
American journal of human genetics - 110(2023), 6 vom: 01. Juni, Seite 950-962 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gao, Guimin [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 05.06.2023 Date Revised 02.12.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ajhg.2023.04.005 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2023 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci | ||
| 650 | 4 | |a Meta-Analysis | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a breast cancer | |
| 650 | 4 | |a genetic epidemiology | |
| 650 | 4 | |a genome-wide association study | |
| 650 | 4 | |a meta-analysis | |
| 650 | 4 | |a multiple tissues | |
| 650 | 4 | |a susceptibility genes | |
| 650 | 4 | |a transcriptome-wide association study | |
| 700 | 1 | |a Fiorica, Peter N |e verfasserin |4 aut | |
| 700 | 1 | |a McClellan, Julian |e verfasserin |4 aut | |
| 700 | 1 | |a Barbeira, Alvaro N |e verfasserin |4 aut | |
| 700 | 1 | |a Li, James L |e verfasserin |4 aut | |
| 700 | 1 | |a Olopade, Olufunmilayo I |e verfasserin |4 aut | |
| 700 | 1 | |a Im, Hae Kyung |e verfasserin |4 aut | |
| 700 | 1 | |a Huo, Dezheng |e verfasserin |4 aut | |
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