ArticleGenome biology2020
A framework for transcriptome-wide association studies in breast cancer in diverse study populations.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.
- Multiancestry Transcriptome-Wide Association Study Identifies Candidate Genes Associated with Hepatoblastoma.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Multi-ancestry transcriptome-wide association studies uncover insights into breast cancer genetics and biology.Nature communications · 2026Article
- Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies.American journal of human genetics · 2026Article
- Transcriptome-wide association studies at cell-state level using single-cell eQTL data.Cell genomics · 2026Article
- ASTWAS: modeling alternative polyadenylation and SNP effects in kernel-driven TWAS reveal novel genetic associations for complex traits.Briefings in bioinformatics · 2026Article
- Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level.British journal of cancer · 2025Article
- Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data.Nature methods · 2025Article
- CADET: Enhanced transcriptome-wide association analyses in admixed samples using eQTL summary data.American journal of human genetics · 2025Article
- The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.Briefings in bioinformatics · 2025Review
- Protocol to estimate the heritability of drug response with GxEMM and identify gene-drug interactions with TxEWAS.STAR protocols · 2025Article
- An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.Cell genomics · 2025Article
- Genomic Analysis Reveals Racial and Age-Related Differences in the Somatic Landscape of Breast Cancer and the Association with Socioeconomic Factors.Cancer research · 2025Article
- Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits.Genome biology · 2025Article
- Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies.HGG advances · 2025Article
- Characterizing the genetic architecture of drug response using gene-context interaction methods.Cell genomics · 2024Article
- Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.Genetic epidemiology · 2024Article
- Novel insights into the pleiotropic health effects of growth differentiation factor 11 gained from genome-wide association studies in population biobanks.BMC genomics · 2024Article
- Using genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes.Nature communications · 2024Article
- Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets.Nature communications · 2024Article
- Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges.Current protocols · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThe relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking.
resultsWe provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS.
conclusionsWe show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.