SECONDARY ANALYSIS OF A LARGE SCALE GENETIC STUDY IN OBESITY AND RELATED OUTCOMES

描述(申请人提供)：肥胖是一种复杂的代谢紊乱，影响着相当大比例的工业化人口，是各种癌症、2型糖尿病、血脂异常、高血压和冠状动脉疾病的重要风险因素。在努力保持最佳身体质量指数(BMI)的过程中，美国人每年还在减肥产品上花费约400亿美元，如减肥食品和饮料、人造甜味剂、书籍和减肥计划。双胞胎、收养和家庭研究表明，体重指数的个体间差异中有40%-70%是可遗传的。最近从普通人群中抽样的全基因组关联研究(GWAS)发现了与肥胖风险相关的多种遗传变异，但累积起来只解释了BMI遗传变异的一小部分。传统GWAS分析中的一个反复出现的问题是，在复制研究中或在不同的种族群体中，缺乏检测变异SNP关联的小影响和重复性的能力。这里提出的具体研究的广泛、长期目标和目标是解决这些差距，并建立显著促进对肥胖和减肥/减肥背后的复杂遗传结构的理解的方法。这将通过对体重极端的仔细表型的个体进行全基因组关联研究(而不是从一般人群中抽样)，并通过应用创新的二次分析来补充和增强传统方法。具体地说，提出了三个目标--(I)识别与肥胖、减肥和体重维持相关的变异SNPs；(Ii)识别与肥胖和肥胖相关结果相关的基因和基因组(通路)；以及(Iii)识别减肥成功和预防体重增加的亚类的预测标记。每个特定目标将通过特定的分析方法来实现--(1)针对特定目标1的传统的、基于单一SNP的连续和离散性状的关联分析；(2)利用针对特定目标2的组合基因集浓缩技术，对与疾病结果相关的基因(SNP组合)和途径(基因组合)进行探索性的二次分析；(3)建立预测模型，以确定随时间推移的体重下降和体重回升的轨迹，并确定特定目标3的体重下降结果的预测生物标记物3.需要进行初步分析(特定目标1)，以便能够在特定目标2和3中进行二次分析。这些努力将有助于更好地了解与肥胖有关的具体结果，为未来研究的设计和内容提供信息，并确定长期保持体重或预防体重增加的预测性遗传生物标记物，这些生物标志物可以指导未来治疗与体重相关的疾病的临床实践。所有这些都与NIDDK的使命直接相关。 公共卫生相关性：这项名为《肥胖及相关结果的大规模基因研究的次级分析》的拟议研究在两个方面与公共卫生相关。首先，这项研究探索了一项关于肥胖、减肥成功和长期体重保持的全基因组关联研究的创新分析解决方案，以确定构成肥胖遗传结构和肥胖相关代谢特征的基因和生物途径。这些发现可能有助于为随后开发治疗肥胖症的药物揭示疾病靶点，也有助于识别在预测长期体重维持或防止体重增加方面具有临床实用价值的遗传生物标记物。其次，本提案中开发和评估的分析方法也适用于其他全基因组关联研究，因此，在增强大规模人类遗传学解决重大公共卫生问题的能力方面具有深远的影响。

DESCRIPTION (provided by applicant): Obesity is a complex metabolic isorder that affects a significant percentage of the industrialized population and is a significant risk factor for various types of cancer, type 2 diabetes, dyslipidemia, hypertension and coronary artery disease. In the struggle to maintain an optimal body mass index (BMI), Americans also spend approximately $40 billion annually on weight-loss products such as diet foods and drinks, artificial sweeteners, books and weight loss programs. Twin, adoption and family studies indicate that 40-70% of inter-individual variation in body mass index is heritable. Recent genome-wide association studies (GWAS) sampling from the general population have identified multiple genetic variants associated with obesity risk but cumulatively explain only a small fraction of the inherited variability in BMI. A recurring problem in traditional GWAS analysis is the lack of power to detect small effects and lack of reproducibility of variant SNP association in replication studies or in different ethnic populations. The broad, long-term objective and the goal of the specific research proposed here is to address these gaps and establish methods that significantly advance understanding of the complex genetic architecture underlying obesity and weight-loss/weight-maintenance. This will be achieved through a genome-wide association study of carefully phenotyped individuals at the extremes of body mass (instead of sampling from the general population) and by the application of innovative secondary analyses that complement and augment traditional approaches. Specifically, three aims are proposed - (i) to identify variant SNPs associated with obesity, weight-loss and weight-maintenance; (ii) to identify genes and gene-sets (pathways) that associate with obesity and obesity-related outcomes and (iii) to identify predictive markers for sub-categories of weight-loss success and prevention of weight gain. Each specific aim will be achieved through a specific analytic method - (i) traditional, single SNP based association analysis of continuous and discrete traits for specific aim 1; (ii) exploratory, secondary analysis for identifying genes (combination of SNPs) and pathways (combination of genes) associated with disease outcomes using combinatorial, gene-set enrichment techniques for specific aim 2; (iii) predictive modeling for determining trajectories of weight-loss and weight-regain over time and identifying predictive biomarkers of weight-loss outcomes for specific aim 3. The primary analysis (specific aim 1) is required to enable secondary analyses in specific aims 2 and 3. These efforts will lead to a better understanding of specific obesity-related outcomes, inform the design and content of future studies, and identify predictive genetic biomarkers of long term weight-maintenance or prevention of weight gain that can guide future clinical practice in the treatment of weight-related disorders. All of these are directly relevant to the mission of NIDDK. PUBLIC HEALTH RELEVANCE: The proposed research, 'Secondary nalysis of a Large Scale Genetic Study in Obesity and Related Outcomes' is relevant to public health in two ways. First, the research explores innovative analytic solutions on a genome-wide association study on obesity, weight-loss success and long-term weight maintenance, for the identification of genes and biological pathways that underlie the genetic architecture of obesity and obesity related metabolic traits. These findings could help uncover disease targets for the subsequent development of pharmacologic agents for the treatment of obesity and also help identify genetic biomarkers that have clinical utility in the prediction of long-term weight maintenance or prevention of weight gain. Secondly, the analytic approaches developed and evaluated in this proposal are also applicable to other genome-wide association studies and as such, have far-reaching consequences in enhancing the power of large-scale human genetics to address major public health concerns.