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

• Published in: Genome Biology Vol. 21; no. 1; pp. 42 - 18
• Main Authors: Bhattacharya, Arjun, García-Closas, Montserrat, Olshan, Andrew F, Perou, Charles M, Troester, Melissa A, Love, Michael I
• Format: Journal Article
• Language: English
• Published: England BioMed Central 20-02-2020; BMC
• Subjects: African Americans; Binding sites; Black or African American - genetics; Breast cancer; Breast Neoplasms - ethnology; Breast Neoplasms - genetics; Expression quantitative trait loci (eQTL); Female; Gene expression; Gene loci; Genome-wide association studies; Genome-Wide Association Study - methods; Genome-Wide Association Study - standards; Genomes; Humans; Minority & ethnic groups; Polygenic traits; Polymorphism, Genetic; Population studies; Quantitative Trait Loci; Reproducibility of Results; Studies; Survival; Transcription factors; Transcriptome; Transcriptome-wide analysis (TWAS); Womens health; Breast cancer; Polygenic traits; Expression quantitative trait loci (eQTL); Survival; Transcriptome-wide analysis (TWAS)
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-020-1942-6

The 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.
We 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.
We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.