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

 描述（由申请人提供）：肺癌是全球最常见的癌症之一。虽然肥胖是某些类型癌症的一个强有力的危险因素，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，在调整其他既定的风险因素后，体重指数（BMI）与肺癌风险之间呈负相关。观察到的负相关性是由于BMI的生物学“真正”效应还是系统性偏倚引起的，这在很大程度上是有争议的。孟德尔随机化（MR）是一种分析方法，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的因果关系。然而，由于缺乏有效的统计工具，MR往往需要一个非常大的样本量，以达到足够的统计功效。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）数据构建MR分析中肥胖性状的IV。这种方法的主要优点是，它可以解开已知遗传变异无法解释的肥胖性状的缺失遗传力，从而大大提高了统计能力。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。这项研究将提供一个独特的机会来回答长期存在的问题，即肥胖特征是否对肺癌风险有因果关系，可能为进一步研究肺癌病因学开辟新的途径，并为促进调查风险因素与疾病之间的因果关系提供重要的统计工具。

 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.