The gut microbiome, host genetics, and risk for inflammatory bowel disease

 描述（由申请方提供）：炎症性肠病（IBD）是一种严重的、不可治愈的胃肠道疾病，其特征为慢性炎症，严重影响患者的生活质量。症状包括腹痛或不适、发烧、腹泻和血便。多达25%的患者会出现其他症状，包括心脏、肺、眼睛、胰腺、骨骼或关节。在美国和加拿大，约有150万人患有IBD，这两个国家的发病率是世界上最高的，估计每年有17，000 - 93，000例新诊断。重要的是，发病的高峰年龄是年轻的成年人，7-20%的诊断是儿科的。由于个人需要血液检查来监测疾病，监测结肠镜检查，症状的药物管理，以及通常的手术，IBD对医疗保健系统有很大的影响。最近对遗传数据的荟萃分析显示，163个基因座与IBD显著相关，解释疾病风险的方差不到15%。有趣的是，相关的基因座富集了参与宿主对肠道微生物反应的基因。先前的研究表明，与对照个体相比，IBD患者的肠道细菌种群或微生物组改变了结构，包括多样性的总体减少以及特定细菌丰度的改变。目前，尽管有消息称， IBD的遗传和微生物组风险因素都存在，没有研究同时考虑到这两个因素。估算细菌基因组以观察肠道中微生物活性的有效性 也没有在IBD中进行研究。我们建议使用来自RISK队列的基因型和微生物组数据来调查这些问题，RISK队列是最大的早发性、未经治疗的克罗恩病病例和非IBD对照的集合。我们的第一个目标是测试这样一个假设，即结合使用遗传和微生物组数据比单独使用遗传或微生物组数据更精确地估计IBD风险。如Gevers等人所述，将根据末端回肠活检和粪便样本的16 S rRNA基因谱计算肠道微生物群落生态失调评分。将使用Jostins等人最近IBD荟萃分析的显著位点和效应量计算多基因风险评分。然后，我们将使用逻辑回归来检验病例/对照状态与多基因风险评分、生态失调指数以及多基因风险评分和生态失调指数的关联。我们的第二个目的是检验这样一个假设，即与分类学信息相比，从粪便样本估算的宏基因组更准确地估计IBD风险。我们将使用已建立的工具PICRUSt对目标1中讨论的同一队列的宏基因组进行插补。然后，我们可以在我们的模型中包括这些基因家族丰度，以测试与生态失调指数，多基因风险评分或两者组合相比，插补宏基因组的差异是否与病例/对照状态有更好的关联。这项拟议的研究将是第一个使用遗传和微生物组数据相结合来评估IBD风险的研究。我们的长期目标是促进对IBD发病机制的理解，并帮助改善诊断和治疗。更广泛地说，这项研究将为宿主基因组和微生物组如何相互作用提供重要的初步见解。

 DESCRIPTION (provided by applicant): Inflammatory bowel disease (IBD) is a serious, incurable gastrointestinal illness characterized by chronic inflammation which profoundly impacts quality of life for patients. Symptoms can include abdominal pain or discomfort, fever, diarrhea, and bloody stools. Up to 25% of patients experience other symptoms involving the heart, lungs, eyes, pancreas, bones, or joints. Around 1.5 million people have IBD in the U.S. and Canada, where rates are among the highest worldwide, and an estimated 17,000-93,000 new diagnoses are made each year. Importantly, peak age of onset is young adulthood, and 7-20% of diagnoses are pediatric. Because individuals require blood tests to monitor disease, surveillance colonoscopy, pharmaceutical management of symptoms, and often surgery, IBD has a large impact on the healthcare system. Recent meta-analysis of genetic data revealed 163 loci significantly associated with IBD that explain less than 15% of variance in disease risk. Intriguingly, associated loci are enriched for genes involved in host response to intestinal microbes. Previous studies have shown IBD patients' gut bacterial populations, or microbiomes, have altered configurations compared to control individuals, including overall reduction in diversity as well as altered abundance of specific bacteria. At this time, despite information that both genetic and microbiome risk factors exist for IBD, no study has taken both into account simultaneously. The validity of imputing bacterial genomes to look at microbial activity in the gut has also not been studied in IBD. We propose to investigate these issues using genotype and microbiome data from the RISK cohort, the largest collection of early-onset, treatment-naïve Crohn's cases and non-IBD controls. Our first aim is to test the hypothesis that using genetic and microbiome data in combination is a more precise estimator of IBD risk than either genetic or microbiome profile alone. Gut microbiome dysbiosis scores will be calculated from 16S rRNA gene profiling of terminal ileum biopsy and fecal samples as described by Gevers et al. Polygenic risk scores will be calculated using significant loci and effect sizes from the recent Jostins et al. meta-analysis of IBD. We will then use logistic regression to test for association o case/control status with polygenic risk score, dysbiosis index, and both polygenic risk score and dysbiosis index. Our second aim is to test the hypothesis that metagenomes imputed from fecal samples more precisely estimate IBD risk compared to taxonomic information. We will impute the metagenomes of the same cohort discussed in Aim 1 using established tool PICRUSt. We can then include these gene family abundances in our model to test whether differences in the imputed metagenome demonstrate better association with case/control status compared to dysbiosis index, polygenic risk score, or the two in combination. The proposed study will be the first to evaluate IBD risk using both genetic and microbiome data in combination. Our long-term goal is to advance understanding of the mechanisms of IBD pathogenesis and help improve diagnosis and treatment. More broadly, this study will contribute important preliminary insights into how the host genome and microbiome interact.