Transcriptome-wide association studies and genetic risk prediction for breast cancer integrating RNA splicing and gene expression from multiple tissues

摘要 乳腺癌是美国和全世界女性最常见的癌症。虽然 全基因组关联研究已经确定了乳腺癌的多个位点，但大部分遗传性仍然是 隐藏到目前为止，已经进行了全转录组关联研究（TWAS）， 基因预测的基因表达与乳腺癌风险的关系。我们最近的研究表明 影响RNA剪接的遗传变异是复杂性状的重要贡献者， 以前只考虑基因表达的遗传效应时忽略了这一点。因此，评估 基因预测的剪接（作为SNP的线性组合）与表型的关联， 发现新的假定候选疾病基因的巨大希望。局部区域中的剪接事件（例如 如内含子切除簇）可以高度相关。然而，TWAS的现有统计方法确实 没有考虑拼接事件之间的相关性，因此可能导致检测中的功率损失 疾病基因此外，基因中的剪接水平（量化为相对计数比率）和总体基因中的剪接水平（量化为相对计数比率）也可以被测量。 在以前的基因定位方法中，基因表达水平没有被一起考虑。乳腺 癌症预防，根据患癌症的风险对妇女进行分层， 通过针对最有可能受益的人群实施风险降低和筛查战略。SNP多基因 已经开发了风险评分来预测乳腺癌，但其预测准确性仍然很低。到 为了提高预测准确性，需要结合来自遗传预测有用信息 表达和剪接。最近，几个转录组研究，如GTEx，收集了DNA 以及来自多个组织样本的RNA;将多个组织的信息整合到TWAS中可以 大大提高了对疾病基因的识别。此外，非裔美国人（AAs） 与欧洲人不同的连锁不平衡（LD）模式，因此影响RNA剪接的遗传变异 并且疾病表型可能是种族特异性的。本研究的目的是开发有效的 用于基因定位和复杂性状如乳腺癌的遗传风险预测的方法， 整合来自多个组织的多组学数据。具体来说，我们将1）开发TWAS的方法 利用来自多个组织的RNA剪接和表达的信息，并将这些方法应用于 识别新的乳腺癌易感基因; 2）开发联合多基因风险预测评分， 在不同人群（包括AA）中模拟不同LD模式的乳腺癌， 来自多个组织的遗传预测剪接和基因表达的信息。我们会考虑 用于局部区域和跨多个组织的剪接事件之间的相关性。我们预计 提出的方法在基因定位方面具有更高的能力或在预测乳腺癌方面具有更高的准确性 比现有的方法。所提出的方法也可以应用于其他复杂疾病。

ABSTRACT Breast cancer is the most common cancer in women in the United States and worldwide. Although genome-wide association studies have identified multiple loci for breast cancer, most of heritability is still hidden. To date, transcriptome-wide association studies (TWAS) have been performed to quantify associations of genetically predicted gene expression with breast cancer risk. Our recent work showed that genetic variants that affect RNA splicing are very important contributors to complex traits but were previously missed when considering the genetic effects on gene expression only. Therefore, evaluating associations of genetically predicted splicing (as a linear combination of SNPs) with phenotypes has a great promise to discover novel putative candidate disease genes. Splicing events in local regions (such as intron excision clusters) can be highly correlated. However, existing statistical methods for TWAS do not account for correlation among splicing events, and thus may result in loss of power in detecting disease genes. Additionally, splicing levels (quantified as relative count ratios) in a gene and the overall gene expression level have not been considered together in previous gene mapping methods. For breast cancer prevention, stratification of women according to the risk of developing the cancer could improve risk reduction and screening strategies by targeting those most likely to benefit. SNP-based polygenic risk scores have been developed to predict breast cancer but their prediction accuracy remains low. To increase prediction accuracy, there is a need to incorporate useful information from genetically predicted expression and splicing. Recently, several transcriptome studies, such as GTEx, have collected DNA and RNA from multiple tissue samples; integrating information across multiple tissues into TWAS could significantly improve the identification of disease genes. In addition, African Americans (AAs) have different linkage disequilibrium (LD) pattern from Europeans, so genetic variants that affect RNA splicing and disease phenotypes could be ethnicity-specific. The objective of this study is to develop effective methods for gene mapping and genetic risk prediction of complex traits such as breast cancer by integrating multi–omics data from multiple tissues. Specifically, we will 1) develop methods for TWAS that leverage information of RNA splicing and expression from multiple tissues and apply the methods to identify novel breast cancer susceptibility genes; 2) develop joint polygenic risk prediction scores for breast cancer that model different LD patterns in distinct populations (including AAs) and incorporate information of genetically predicted splicing and gene expression from multiple tissues. We will account for correlation among splicing events in local regions and across multiple tissues. We expect that the proposed methods have higher power in gene mapping or higher accuracy in prediction of breast cancer than existing methods. The proposed methods can also be applied to other complex diseases.