An integrative omics approach to investigate gene-environment interaction in colorectal cancer risk

项目摘要/摘要 结直肠癌(CRC)仍然是世界各地癌症相关死亡的主要原因之一。 已经为这个复合体确定了许多环境风险因素和200多个遗传风险变体， 多因素疾病。然而，尽管有很强的生物学基础来证明细菌的重要性和丰度 基因-环境(GxE)相互作用，环境风险因素(这里广义定义为 生活方式、饮食、肥胖、药物使用和中间生物标记物)对遗传风险因素的调节作用知之甚少。 为了实现精准预防的承诺，我们迫切需要对GxE有更深入的了解 结直肠癌风险中的相互作用。了解哪些可改变的风险因素调节遗传风险，这是固定的， 为新的预防干预战略提供生物学见解和可操作的目标。为了加速 发现结直肠癌风险中的GxE相互作用并朝着转换迈出重要的下一步，我们建议 结合单细胞多组学数据、个体水平协调的全面创新方法 流行病学和临床数据，以及来自大型、特征良好的多样化研究的全基因组数据 使用新的计算和统计方法。单电池的显著改进 多模式组学技术，结合基于强大深度学习的新计算工具 建模方法现在允许我们预测基因变异对细胞类型基因调控的影响- 具体的整体方式。因为同时测量的单细胞基因表达(scRNA-seq)和 正常大肠粘膜组织染色质可及性(scatac-seq)数据缺乏人种和人种。 不同的样本，并详细评估环境风险因素，我们在目标1中建议产生这样的 50个人的数据。该资源与其他用于结直肠组织的单细胞多组学汇编一起 (如Htan)，将被用来开发整个基因组中遗传变异的功能预测分数。在……里面 目标2，我们将使用这些功能预测分数来提高发现新的GxE的统计能力 互动。我们将在超过230,000个种族和民族多样化的结直肠癌病例中进行全基因组GxE扫描 以及对主要环境风险因素的控制，包括肥胖、糖尿病、吸烟、酗酒、吸毒、 饮食因素和中间生物标志物与代谢失调和慢性炎症有关。至 扩大我们可以评估的关键风险因素的数量，我们将利用现有的遗传工具。在目标3中，我们 将全面描述和转换GxE互动。为此，我们将通过以下方式对GxE调查结果进行分层 临床因素，包括发病年龄、种族和民族、性别和肿瘤亚型。此外，我们还将 将GxE相互作用和基因预测的生物标记物纳入全面的跨祖先风险 预测模型改进预测，提供可操作的信息，减轻CRC的负担。我们的 社区顾问强调了将遗传和环境之间的相互作用包括在内的重要性 风险预测模型中的风险因素，以提高社区对风险预测模型的接受度。

PROJECT SUMMARY/ABSTRACT Colorectal cancer (CRC) remains one of the leading causes of cancer-related deaths around the world. Many environmental risk factors and over 200 genetic risk variants have been identified for this complex, multifactorial disease. However, despite the strong biological rationale for the importance and abundance of gene-environment (GxE) interactions, the extent to which environmental risk factors (broadly defined here as lifestyle, diet, obesity, drug use and intermediate biomarkers) modulate genetic risk factors is poorly understood. To achieve the promise of precision prevention, we urgently need to gain a deeper understanding of GxE interactions in CRC risk. Understanding which modifiable risk factors modulate genetic risk, which is fixed, provides biological insights and actionable targets for new prevention intervention strategies. To accelerate the discovery of GxE interactions in CRC risk and to take an important next step towards translation, we propose a comprehensive innovative approach that combines single-cell multi-omics data, individual-level harmonized epidemiological and clinical data, and genome-wide data from large, well-characterized, diverse study populations, with novel computational and statistical approaches. Dramatic improvements in single-cell multimodal omics technologies, combined with new computational tools based on powerful deep-learning modeling approaches now allow us to predict the impact of genetic variants on gene regulation in a cell-type- specific holistic manner. Because simultaneously measured single-cell gene expression (scRNA-seq) and chromatin accessibility (scATAC-seq) data for normal colorectal mucosa tissue is lacking for racially and ethnically diverse samples with detailed assessment of environmental risk factors, we propose in Aim 1 to generate such data for 50 individuals. This resource, together with other single cell multi-omics compendia for colorectal tissue (like HTAN), will be leveraged to develop functional prediction scores for genetic variants across the genome. In Aim 2, we will use these functional prediction scores to boost statistical power for discovery of novel GxE interactions. We will perform genome-wide GxE scans in over 230,000 racially and ethnically diverse CRC cases and controls across key environmental risk factors, including obesity, diabetes, smoking, alcohol, drug use, dietary factors and intermediate biomarkers linked to metabolic dysregulation and chronic inflammation. To expand the number of key risk factors we can evaluate, we will utilize existing genetic instruments. In Aim 3, we will comprehensively characterize and translate GxE interactions. To do so, we will stratify GxE findings by clinical factors, including age of onset, racial and ethnic group, sex, and tumor subtypes. Additionally, we will incorporate GxE interactions and genetically predicted biomarkers in a comprehensive trans-ancestral risk prediction model to improve prediction and provide actionable information to reduce the burden of CRC. Our community advisors have stressed the importance of including the interplay between genetic and environmental risk factors in risk prediction modeling to enhance the acceptance of risk prediction models in the community.