Genetic analysis of lung cancer susceptibility

肺癌易感性基因分析

• 批准号: 10322757
• 负责人: Christopher I. Amos
• 金额: $ 8万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322757
• 关键词: Asia; Biological Markers; Biology; Cancer Etiology; Chemopreventive Agent; Collaborations; Collection; Colon Carcinoma; Communities; Data; Development; Dideoxy Chain Termination DNA Sequencing; Enrollment; Ethnic group; Etiology; Europe; Family history of; Genetic; Genetic Predisposition to Disease; Genetic study; Genotype; Geography; Heritability; Image Analysis; Individual; Integration Host Factors; International; Intervention; Joints; Knowledge; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Mendelian randomization; Modeling; North America; Parents; Participant; Patient Recruitments; Patients; Play; Population; Predisposition; Proxy; Public Health; Resources; Risk; Risk Assessment; Risk Factors; Role; Sample Size; Sampling; Site; Smoker; Smoking Behavior; South America; Statistical Data Interpretation; Subgroup; Susceptibility Gene; Technology; Variant; Vitamin B 12; Work; biobank; biomarker evaluation; cancer risk; case control; database of Genotypes and Phenotypes; design; epidemiology study; experience; genetic analysis; genetic architecture; genetic information; genetic predictors; genetic resource; genetic variant; genome wide association study; genome-wide; genome-wide analysis; high risk; improved; insight; malignant breast neoplasm; mortality; novel; polygenic risk score; population based; prediction algorithm; programs; risk prediction; screening; smoking cessation; tobacco exposure; tumor; validation studies

Abstract Although tobacco exposure is the major determinant of lung cancer, only about 15% of smokers develop lung cancer. In this study, we seek to identify genetic effects that influence lung cancer risk, either independently or jointly with smoking behavior. We hypothesize that novel genetic and host factors influencing the susceptibility for lung cancer can be identified by genotyping. We will pursue 3 aims. In Aim 1 we will (a) Genotype on bead arrays an 23,103 lung cancer cases and 23,103 unrelated controls on genome wide arrays in order to identify genetic variants and (b) Validate selected SNPs in additional populations. Validation studies will be conducted for selected variants to assure results are consistent between genotyping or Sanger sequencing analysis. In Aim 2, we will Evaluate strength of genetic effects using the additional genotypes generated in aim (1) and combine with our existing 29,683 lung cancer cases and 55,586 controls. In this aim, we take advantage of the larger population of samples to impute variants and perform association analyses for inferred variants for a much larger collection. Finally in Aim 4, we will Construct a risk prediction algorithm to improve the accuracy of the current lung cancer risk models. These models will be valuable for refining the selection of individuals to be enrolled in lung screening studies.

摘要 虽然烟草暴露是肺癌的主要决定因素，但只有约15%的吸烟者 患上肺癌在这项研究中，我们试图确定影响肺癌的遗传效应， 风险，无论是独立或共同吸烟行为。我们假设新的基因 和影响肺癌易感性的宿主因素 通过基因分型我们将追求三个目标。在目标1中，我们将（a）在微珠阵列上进行基因分型， 23，103例肺癌病例和23，103例无关对照在全基因组阵列上， 鉴定遗传变体和（B）在另外群体中筛选选择的SNP。 将对选定的变体进行验证研究，以确保结果一致 基因分型或桑格测序分析之间的差异。在目标2中，我们将评估实力 使用目标（1）和联合收割机中产生的额外基因型的遗传效应 我们现有的29,683例肺癌病例和55,586例对照。为此，我们采取 更大样本群体插补变异和进行 关联分析推断的变体为一个更大的集合。最后，在目标4中， 我们将构建一个风险预测算法，以提高当前肺部的准确性， 癌症风险模型这些模型将是有价值的细化选择的个人， 参加肺部筛查研究。