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

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

doi:10.1186/s13059-020-1942-6

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.

Highlights

The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival
Race-specific germline Expression quantitative trait loci (eQTL) analysis To assess the association between germline genomic variation and tumor expression of 406 autosomal genes, targeted by the Carolina Breast Cancer Study (CBCS) because of their association with breast cancer progression, we first conducted a full cistrans expression quantitative trait loci analysis, stratifying on race and controlling for key biological covariates and population stratification
We evaluated associations between the tumor expression levels of 406 autosomal genes and 5,989,134 germline SNPs in samples derived from 621 self-identified African American women (AA) and 578 self-identified white women (WW)

Summary

Introduction

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. Ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. Few genome-wide association studies (GWAS) have studied the relationship between germline variation and survival outcomes in breast cancer, with most focusing instead on genetic predictors of risk [5, 6]. GWAS have shown evidence of association between candidate common germline variants and breast cancer survival, but these studies are often underpowered [7, 8].

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