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

TitleA framework for transcriptome-wide association studies in breast cancer in diverse study populations.
Publication TypeJournal Article
Year of Publication2020
AuthorsBhattacharya, Arjun, Montserrat García-Closas, Andrew F. Olshan, Charles M. Perou, Melissa A. Troester, and Michael I. Love
JournalGenome Biol
Volume21
Issue1
Pagination42
Date Published2020 Feb 20
ISSN1474-760X
KeywordsAfrican Americans, Breast Neoplasms, Female, Genome-Wide Association Study, Humans, Polymorphism, Genetic, Quantitative Trait Loci, Reproducibility of Results, Transcriptome
Abstract

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

DOI10.1186/s13059-020-1942-6
Alternate JournalGenome Biol
Original PublicationA framework for transcriptome-wide association studies in breast cancer in diverse study populations.
PubMed ID32079541
PubMed Central IDPMC7033948
Grant ListU01 CA179715 / CA / NCI NIH HHS / United States
P30 ES010126 / ES / NIEHS NIH HHS / United States
P30 CA016086 / CA / NCI NIH HHS / United States
R01 MH118349 / MH / NIMH NIH HHS / United States
P50 CA058223 / CA / NCI NIH HHS / United States
P01 CA151135 / CA / NCI NIH HHS / United States
R01 HG009937 / HG / NHGRI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: