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基因座，大多数遗传性仍然是 藏起来了。到目前为止，已经进行了转录组范围的关联研究(TWAS)来量化 基因预测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剪接的遗传变异是导致复杂性状的非常重要的因素，但 以前只考虑对基因表达的遗传影响时遗漏了。因此，评估 基因预测的剪接(作为SNPs的线性组合)与表型的关联具有 发现新的可能的疾病候选基因的希望很大。拼接本地区域中的事件(如 如内含子切除簇)可以高度相关。然而，现有的第三产业统计方法做了 不考虑剪接事件之间的相关性，因此可能导致检测的功率损失 疾病基因。此外，基因中的剪接水平(量化为相对计数比)和总体 在以往的基因定位方法中，基因表达水平没有被综合考虑。乳房 癌症预防、分层妇女根据癌症发生的风险可改善 通过瞄准最有可能受益的人来降低风险和筛查战略。基于SNP的多基因 风险评分已被开发用于预测乳腺癌，但其预测准确率仍然很低。至 为了提高预测的准确性，需要结合来自基因预测的有用信息 表达和拼接。最近，几项转录组研究，如GTEx，已经收集了DNA 和来自多个组织样本的RNA；将跨多个组织的信息整合到TWAs中可以 显著提高了对疾病基因的识别。此外，非裔美国人(AA)有 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 疾病表型可能是种族特有的。这项研究的目的是开发有效的 乳腺癌等复杂性状的基因定位和遗传风险预测方法 整合来自多个组织的多组学数据。具体地说，我们将1)开发针对第三世界的方法 利用来自多个组织的RNA剪接和表达的信息并将该方法应用于 发现新的乳腺癌易感基因；2)开发联合多基因风险预测评分 乳腺癌在不同的人群(包括AA)中模拟不同的LD模式，并合并 基因预测的剪接和多组织基因表达的信息。我们会说明的 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， 所提出的方法具有更高的基因定位能力或更高的乳腺癌预测精度 而不是现有的方法。所提出的方法也可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.