SynthesisAmerican journal of human genetics2023
A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes.
Synthesis in American journal of human genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status.Breast cancer research : BCR · 2024Pooled it
- Multi-ancestry transcriptome-wide association studies uncover insights into breast cancer genetics and biology.Nature communications · 2026Article
- Identification and functional validation of EPS15L1 as a key driver of triple-negative breast cancer.NPJ breast cancer · 2026Article
- Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention.American journal of human genetics · 2026Article
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- Cell-type aware transcriptome-wide association study of mammographic density phenotypes.medRxiv : the preprint server for health sciences · 2025Article
- Integrating multi-ancestry genomic and proteomic data to identify blood risk biomarkers and target proteins for breast cancer genetic risk loci.International journal of cancer · 2025Article
- Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level.British journal of cancer · 2025Article
- TransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study.American journal of human genetics · 2025Article
- FarmGTEx TWAS-server: An Interactive Web Server for Customized TWAS Analysis.Genomics, proteomics & bioinformatics · 2025Article
- metacp: a versatile software package for combining dependent or independent p-values.BMC bioinformatics · 2025Article
- Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease.PLoS genetics · 2025Article
- Multivariate proteome-wide association study to identify causal proteins for Alzheimer disease.American journal of human genetics · 2025Article
- Genome-wide association studies are enriched for interacting genes.BioData mining · 2025Article
- A proteome-wide association study identifies putative causal proteins for breast cancer risk.British journal of cancer · 2024Article
- Understanding genetic architecture of breast cancer: how can proteome-wide association studies contribute?British journal of cancer · 2024Article
- Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk.Nature communications · 2024Article
- Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer.Journal of the National Cancer Institute · 2024Article
- A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer.American journal of human genetics · 2024Article
- Multi-Omic Analysis Reveals Genetic Determinants and Therapeutic Targets of Chronic Kidney Disease and Kidney Function.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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