A framework for transcriptome-wide association studies in breast cancer in diverse study populations
A framework for transcriptome-wide association studies in breast cancer in diverse study populations
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DOI：
10.1186/s13059-020-1942-6
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发表时间：
2020-02-20
期刊：
影响因子：
12.3
通讯作者：
Love, Michael I.
中科院分区：
文献类型：
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作者：
Bhattacharya, Arjun;Garcia-Closas, Montserrat;Love, Michael I.
背景：胚系遗传变异与乳腺癌存活率之间的关系在很大程度上是未知的，特别是在研究不足的少数民族人群中，他们的存活率往往较低。全基因组关联研究已经询问了乳腺癌的存活率，但通常由于亚型的异质性和临床协变量而力量不足，并检测到难以解释的非编码区的基因座。转录组相关研究表明，通过利用来自相关组织外部参考小组的表达数量性状基因座(EQTL)，可以提高检测功能相关基因座的能力。然而，可能需要祖先或种族特定的参考小组来在祖先不同的队列中得出正确的推断。目前还缺乏这样的乳腺癌治疗面板。结果我们利用卡罗莱纳乳腺癌研究(CBCS)的数据，为不同人群中的乳腺癌提供了一个TWAS框架，这是一个以人群为基础的队列，过度抽样了黑人女性。我们对406个乳腺癌相关基因进行eQTL分析，以训练来自种系基因的肿瘤表达的种族分层预测模型。使用这些模型，我们将来自CBCS和TCGA的独立数据归因于表达，在评估绩效时考虑了抽样变异性。这些模型不适用于不同种族，它们的预测性能因肿瘤亚型而异。在CBCS(N=3,828)中，在错误发现调整重要性为0.10的情况下，并对种族分层，我们确定了AURKA、CAPN13、PIK3CA和SERPINB5附近的黑人女性通过TWAs的能力不足的关联。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预后的遗传学基础的有效方法。
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.