Discovery and CRISPR validation of genetic factors associated with antipsychotic-induced weight gain and cardiometabolic risk

项目总结/摘要 抗精神病药物引起的体重增加（AIWG）在精神病患者人群中具有重要的公共卫生意义， 可以通过个性化的精准医疗来解决抗精神病药物会增加体重， 从而增加心脏代谢风险（CMR）状况如2型糖尿病和心血管疾病， 与加速细胞老化有关的条件。这导致了10至15年的死亡率差距 精神病患者和普通人之间的关系抗精神病药物通常用于所有 年龄，但与脂肪增加的差异模式有关，即儿童增加更多，老年人增加 少许多全基因组关联研究（GWAS）已经确定了与糖尿病相关的关键遗传因素。 AIWG，但受到使用间接测量体脂的限制，如体重或体重指数（BMI）， 与代谢性疾病风险的相关性较低。此外，现有的研究并没有完全解决与年龄有关的问题。 AIWG的差异。针对NIH PA-17-088“现有队列、数据集和 存储生物标本，以解决临床衰老研究问题，”我们提出了一种新的方法， 基于人群的遗传学，现有的生物样本与相关的临床数据，包括精确测量的肥胖 和胰岛素敏感性，以及先进的分子工具，以确定和功能验证关键的遗传 AIWG和CMR的决定因素。这种方法利用了1）现有的人口水平数据 来自大型生物库计划和流行病学研究，包括约15，000名患有 遗传和相关表型数据，2）来自NIH资助的随机化研究的现有临床和生物样本数据 描述抗精神病药物在儿童、成人和老年人中的代谢效应的临床试验或RCT 直接和精确的身体脂肪测量，以及来自大约600名遗传性肥胖症患者的数据， 代谢风险的数据和其他生物标志物，以及3）基于CRISPR的体外药物暴露，然后是 细胞功能测定以表征抗精神病药物影响的分子机制。额外来源 现有的数据将在资金到位后提供，其中包括来自大型企业的约3000名个人的数据。 行业资助的RCT，来自精神病遗传学联盟（PGC，见 支持信），以及来自荷兰双相队列研究（Dutch Bipolar Cohort Study）的2，000多名个体的数据（见支持信）。 支持）也将用于独立验证和复制。本研究将联合收割机结合无偏基因组 方法，包括基于阵列的基因分型，GWAS和GWAS荟萃分析，基于CRISPR的基因 抑制/激活筛选（CRISPRi/a），以及对优先的CRISPRi/a变体的功能性分子和细胞研究。 兴趣，结合独特的临床数据，以确定遗传因素，并产生预测模型的重量 与加速老化相关的生理变化。这种分子技术的组合将 使我们能够建立在已知的遗传关联，同时发现新的基因和遗传变异， 与年轻和老年患者中治疗相关脂肪增加的最大风险相关。该项目将 有助于开发一种基于精确度的治疗算法，可以准确地预测和预防 青少年、青年、中年和老年人的AIWG和心脏代谢风险这项研究的结果将 还对公开可用的数据集做出重要贡献，并推动未来收集必要的类似数据 进一步验证我们的结果

PROJECT SUMMARY/ABSTRACT Antipsychotic-induced weight gain (AIWG) is of significant public health importance in mentally ill populations, potentially addressable with personalized, precision medicine. Antipsychotic medications increase body weight, thereby increasing cardiometabolic risk (CMR) conditions like type 2 diabetes and cardiovascular disease, conditions associated with accelerated cellular aging. This has contributed to a 10 to 15-year mortality gap between mentally ill individuals and the general population. Antipsychotic medications are commonly used at all ages, but are associated with differential patterns of fat gain, whereby children gain more and older adults gain less. Numerous genome-wide association studies (GWAS) have identified key genetic factors associated with AIWG, but are limited by the use of indirect measures of body fat, like weight or body mass index (BMI), that are less well correlated with metabolic disease risk. Additionally, existing research does not fully address age-related differences in AIWG. In response to NIH PA-17-088 “Secondary Analyses of Existing Cohorts, Data Sets and Stored Biospecimens to Address Clinical Aging Research Questions,” we propose a novel approach applying population-based genetics, existing biospecimen with linked clinical data including precisely-measured adiposity and insulin sensitivity, and advanced molecular tools to identify and functionally validate key genetic determinants of AIWG and CMR across the age-span. This approach leverages 1) existing population-level data from large biobanking initiatives and epidemiological studies inclusive of approximately 15,000 individuals with genetic and relevant phenotypic data, 2) existing clinical and biospecimen data from NIH funded randomized clinical trials or RCTs characterizing the metabolic effects of antipsychotics in children, adults and older adults with direct and precise measures of body fat, together with data from approximately 600 individuals with genetic data and additional biomarkers of metabolic risk, and 3) CRISPR based in vitro drug exposure, followed by cellular functional assays to characterize molecular mechanisms impacted by antipsychotic. Additional sources of existing data will be available upon funding, including data on approximately 3000 individuals from large industry funded RCTs, data on up to 250,000 individuals from the Psychiatric Genetics Consortium (PGC, see letter of support), and data from more than 2,000 individuals from the Dutch Bipolar Cohort Study (see letter of support) will also be used for independent validation and replication. This study will combine unbiased genomic methods, including array-based genotyping, GWAS and GWAS meta-analysis, CRISPR-based gene inhibition/activation screens (CRISPRi/a), and functional molecular and cellular studies on prioritized variants of interest, combined with unique clinical data to identify genetic factors and generate predictive models of weight related physiological changes associated with accelerated aging. This combined set of molecular techniques will allow us to build on known genetic associations, while discovering new genes and genetic variants that are associated with the greatest risk for treatment-related fat gain in younger and older patients. This project will contribute to the development of a precision-based treatment algorithm that can accurately predict and prevent AIWG and cardiometabolic risk in youth, young, middle-aged, and older adults. The results from this study will also importantly contribute to publicly available datasets, and motivate future collection of similar data necessary for further validation of our results.