Develop and Apply a Novel Genome-wide Mendelian Randomization Method to Examine Relationship between Obesity and Lung Cancer

开发并应用新型全基因组孟德尔随机化方法来检查肥胖与肺癌之间的关系

• 批准号: 9025306
• 负责人: GLORIA YUEN FUN HO
• 金额: $ 20万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE, INC
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-11 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9025306
• 关键词: Accounting; Address; Affect; Biological; Body Weight decreased; Body mass index; Colon Carcinoma; Complex; Data; Development; Disease; Endometrial Carcinoma; Epidemiologic Studies; Etiology; Future; Genes; Genetic; Heritability; Individual; Inherited; Investigation; Knowledge; Lead; Link; Malignant Neoplasms; Malignant neoplasm of lung; Methods; Modeling; Obesity; Plague; Public Health; Randomized; Research; Residual state; Resources; Risk; Risk Factors; Role; Sample Size; Smoking; Smoking Status; Statistical Methods; Subgroup; Systematic Bias; Time; Waist-Hip Ratio; Weight; Weight Gain; base; cancer risk; cancer type; cost efficient; epidemiologic data; genetic variant; genome wide association study; genome-wide; improved; instrument; interest; malignant breast neoplasm; novel; prospective; protective effect; public health relevance; simulation; tool; trait; waist circumference

 DESCRIPTION (provided by applicant): Lung cancer is one of the most common cancers worldwide. While obesity is a strong risk factor for certain types of cancer, such as colon, breast and endometrial cancers, many epidemiological studies have consistently indicated an inverse association between body mass index (BMI) and risk of lung cancer after adjusting for other established risk factors. It is largely controversial whether the observed inverse association is due to the biological "genuine" effects of BMI or systematic biases. Mendelian randomization (MR) is an analytical approach that uses genetic variants as the instrumental variable (IV) to infer the causal relationship between an exposure variable and disease. However, because of the lack of efficient statistical tools, MR often requires an extremely large sample size to achiev adequate statistical power. In this proposed study, we will develop a novel statistical method that utilizes genome wide association study (GWAS) data to construct the IV for obesity traits in MR analysis. The major advantage of this approach is that it can unravel the missing heritability of obesity traits that is not accounted for by the known genetic variants, and thereby provide substantially improved statistical power. Because this study will utilize the existing individual GWAS data as well as detailed epidemiologic data from about 20,000 lung cancer cases and 20,000 controls in TRICL (Transdisciplinary Research in Cancer of the Lung), it will be conducted in an extremely cost-efficient manner. This study will provide a unique opportunity to answer the longstanding question of whether there are causal effects of obesity traits on lung cancer risk, potentially to open new avenues for further studies in understanding the etiology of lung cancer, and to provide important statistical tools for facilitating investigation of the causa relationship between risk factors and diseases in general.

 描述（由申请人提供）：肺癌是全世界最常见的癌症之一。虽然肥胖是某些类型癌症的强烈危险因素，例如结肠癌、乳腺癌 和子宫内膜癌，许多流行病学研究一致表明，在调整其他已确定的危险因素后，体重指数 (BMI) 与肺癌风险之间呈负相关。所观察到的负相关是由于 BMI 的生物学“真实”效应还是系统偏差，这一点存在很大争议。孟德尔随机化 (MR) 是一种使用遗传变异作为工具变量 (IV) 来推断暴露变量与疾病之间因果关系的分析方法。然而，由于缺乏有效的统计工具，MR往往需要极大的样本量才能获得足够的统计功效。在这项拟议的研究中，我们将开发一种新的统计方法，利用全基因组关联研究 (GWAS) 数据来构建 MR 分析中肥胖特征的 IV。这种方法的主要优点是，它可以揭示已知遗传变异无法解释的肥胖性状缺失的遗传性，从而提供显着改善的统计功效。由于这项研究将利用现有的个体 GWAS 数据以及 TRICL（肺癌跨学科研究）中约 20,000 例肺癌病例和 20,000 例对照的详细流行病学数据，因此将以极具成本效益的方式进行。这项研究将为回答肥胖特征是否与肺癌风险之间存在因果关系这一长期存在的问题提供一个独特的机会，有可能为了解肺癌病因学的进一步研究开辟新途径，并为促进研究危险因素与一般疾病之间的因果关系提供重要的统计工具。