A framework for transcriptome-wide association studies in breast cancer in diverse study populations

Bhattacharya, Arjun; García‐Closas, Montserrat; Olshan, Andrew F.; Perou, Charles M.; Troester, Melissa A.; Love, Michael I.

doi:10.1186/s13059-020-1942-6

Cited by 67 publications

(70 citation statements)

References 100 publications

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“…A framework for transcriptome-wide association studies in breast cancer in diverse study populations [45] The median CV R 2 for the 153 genes is 0.011 in both African American and white women.…”

Section: Title Of the Publication Description Of Prediction Accuracymentioning

confidence: 99%

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

Cao

Ding

et al. 2021

PLoS Genet

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The transcriptome-wide association study (TWAS) has emerged as one of several promising techniques for integrating multi-scale ‘omics’ data into traditional genome-wide association studies (GWAS). Unlike GWAS, which associates phenotypic variance directly with genetic variants, TWAS uses a reference dataset to train a predictive model for gene expressions, which allows it to associate phenotype with variants through the mediating effect of expressions. Although effective, this core innovation of TWAS is poorly understood, since the predictive accuracy of the genotype-expression model is generally low and further bounded by expression heritability. This raises the question: to what degree does the accuracy of the expression model affect the power of TWAS? Furthermore, would replacing predictions with actual, experimentally determined expressions improve power? To answer these questions, we compared the power of GWAS, TWAS, and a hypothetical protocol utilizing real expression data. We derived non-centrality parameters (NCPs) for linear mixed models (LMMs) to enable closed-form calculations of statistical power that do not rely on specific protocol implementations. We examined two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression), and also tested the effects of various properties including expression heritability. Our analysis reveals two main outcomes: (1) Under pleiotropy, the use of predicted expressions in TWAS is superior to actual expressions. This explains why TWAS can function with weak expression models, and shows that TWAS remains relevant even when real expressions are available. (2) GWAS outperforms TWAS when expression heritability is below a threshold of 0.04 under causality, or 0.06 under pleiotropy. Analysis of existing publications suggests that TWAS has been misapplied in place of GWAS, in situations where expression heritability is low.

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Section: Title Of the Publication Description Of Prediction Accuracymentioning

confidence: 99%

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

Cao

Ding

et al. 2021

PLoS Genet

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“…Subsequently, we explored whether we could identify ancestry-specific enriched oncogenic pathways with prognostic relevance in BasalMyo tumors. In support of this concept, a recent transcriptome-wide association study of the Caroline Breast Cancer Study transcriptomic dataset, comprising of selfidentified African-American and European-American women, demonstrated that ancestry-stratified predictive risk models did not perform across ancestries and/or subtype 65 . Through integrative analysis of differential enrichment and prognostic connotation, we identified seven differentially enriched signaling pathways with prognostic connotation in patients of EA and/or AA.…”

Section: Discussionmentioning

confidence: 99%

Ancestry-associated transcriptomic profiles of breast cancer in patients of African, Arab, and European ancestry

et al. 2021

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Breast cancer largely dominates the global cancer burden statistics; however, there are striking disparities in mortality rates across countries. While socioeconomic factors contribute to population-based differences in mortality, they do not fully explain disparity among women of African ancestry (AA) and Arab ancestry (ArA) compared to women of European ancestry (EA). In this study, we sought to identify molecular differences that could provide insight into the biology of ancestry-associated disparities in clinical outcomes. We applied a unique approach that combines the use of curated survival data from The Cancer Genome Atlas (TCGA) Pan-Cancer clinical data resource, improved single-nucleotide polymorphism-based inferred ancestry assignment, and a novel breast cancer subtype classification to interrogate the TCGA and a local Arab breast cancer dataset. We observed an enrichment of BasalMyo tumors in AA patients (38 vs 16.5% in EA, p = 1.30E − 10), associated with a significant worse overall (hazard ratio (HR) = 2.39, p = 0.02) and disease-specific survival (HR = 2.57, p = 0.03). Gene set enrichment analysis of BasalMyo AA and EA samples revealed differences in the abundance of T-regulatory and T-helper type 2 cells, and enrichment of cancer-related pathways with prognostic implications (AA: PI3K-Akt-mTOR and ErbB signaling; EA: EGF, estrogen-dependent and DNA repair signaling). Strikingly, AMPK signaling was associated with opposing prognostic connotation (AA: 10-year HR = 2.79, EA: 10-year HR = 0.34). Analysis of ArA patients suggests enrichment of BasalMyo tumors with a trend for differential enrichment of T-regulatory cells and AMPK signaling. Together, our findings suggest that the disparity in the clinical outcome of AA breast cancer patients is likely related to differences in cancer-related and microenvironmental features.

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“…Expression weights from the model with the largest R 2 were used to compute TWAS association statistics. More details can be found in the original manuscript 18 and other publication 52 .…”

Section: Methodsmentioning

confidence: 99%

Functional annotation of genetic associations by transcriptome-wide association analysis provides insights into neutrophil development regulation

et al. 2020

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Genome-wide association studies (GWAS) have identified multiple genomic loci linked to blood cell traits, however understanding the biological relevance of these genetic loci has proven to be challenging. Here, we performed a transcriptome-wide association study (TWAS) integrating gene expression and splice junction usage in neutrophils (N = 196) with a neutrophil count GWAS (N = 173,480 individuals). We identified a total of 174 TWAS-significant genes enriched in target genes of master transcription factors governing neutrophil specification. Knockout of a TWAS candidate at chromosome 5q13.2, TAF9, in CD34+ hematopoietic and progenitor cells (HSPCs) using CRISPR/Cas9 technology showed a significant effect on neutrophil production in vitro. In addition, we identified 89 unique genes significant only for splice junction usage, thus emphasizing the importance of alternative splicing beyond gene expression underlying granulopoiesis. Our results highlight the advantages of TWAS, followed by gene editing, to determine the functions of GWAS loci implicated in hematopoiesis.

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A framework for transcriptome-wide association studies in breast cancer in diverse study populations

Cited by 67 publications

References 100 publications

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

Ancestry-associated transcriptomic profiles of breast cancer in patients of African, Arab, and European ancestry

Functional annotation of genetic associations by transcriptome-wide association analysis provides insights into neutrophil development regulation

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