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.

中文翻译：

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不同研究人群乳腺癌全转录组关联研究的框架

背景 种系遗传变异与乳腺癌生存之间的关系在很大程度上尚不清楚，特别是在未充分研究的少数群体中，他们的生存率往往较差。全基因组关联研究 (GWAS) 探讨了乳腺癌的生存率，但由于亚型异质性和临床协变量，往往效果不足，并且检测到难以解释的非编码区域中的基因座。全转录组关联研究 (TWAS) 显示，通过利用相关组织中外部参考组的表达数量性状位点 (eQTL)，检测功能相关位点的能力有所增强。然而，可能需要特定于祖先或种族的参考小组才能在祖先不同的群体中得出正确的推论。缺乏此类乳腺癌检测组。结果 我们利用卡罗莱纳州乳腺癌研究 (CBCS) 的数据，为不同人群的乳腺癌提供了一个 TWAS 框架，该研究是一个基于人群的队列，对黑人女性进行了过度抽样。我们对 406 个乳腺癌相关基因进行 eQTL 分析，以训练种系基因型肿瘤表达的种族分层预测模型。使用这些模型，我们估算了来自 CBCS 和 TCGA 的独立数据中的表达，并考虑了评估绩效时的抽样变异性。这些模型并不适用于不同种族，而且它们的预测性能因肿瘤亚型而异。在 CBCS（N = 3,828）中，以 0.10 的错误发现调整显着性并按种族分层，我们通过 TWAS 确定了 AURKA 、 CAPN13 、 PIK3CA 和 SERPINB5 附近黑人女性的关联，而这些关联在 GWAS 中动力不足。结论 我们表明，仔细实施和彻底验证的 TWAS 是了解不同人群乳腺癌结果的遗传学基础的有效方法。