A Statistical Testing Strategy Accounting for Random and Nonrandom (Skewed) X-Chromosome Inactivation Identifies Lung Cancer Susceptibility Loci among Smokers.
Rodolphe Jantzen, Sophie Camilleri-Broët, Nicole Ezer, Philippe Broët
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引用次数: 0
Abstract
Introduction: Lung cancer is the most common cancer worldwide in mortality and the second in incidence. Epidemiological studies found a higher lung cancer risk for smoking women in comparison to men, but these sex differences, irrespective of smoking habits, remain controversial. One of the hypotheses concerns the genetic contribution of the sex chromosomes. However, while genome-wide association studies identified many lung cancer susceptibility loci, these analyses have excluded X-linked loci.
Methods: To account for nongenetic factors, we first presented an association test based on an additive-multiplicative hazard model accounting for random/nonrandom X-inactivation process. A simulation study was performed to investigate the properties of the proposed test as compared with the Wald test from a Cox model with random X-inactivation process and the partial likelihood ratio test proposed by Xu et al. accounting for nonrandom X-inactivation process. Then, we performed an X chromosome-wide association study on 9,261 individuals from the population-based cohort CARTaGENE to identify susceptibility loci for lung cancer among current and past smokers. We adjusted for the PLCOm2012 lung cancer risk score used in screening programs.
Results: Simulation results show the good behavior of the proposed test in terms of power and type I error probability as compared to the Xu et al. and the Wald test. Using the proposed test statistic and adjusting for the PLCOm2012 score, the X chromosome-wide statistical analysis identified two SNPs in low-linkage disequilibrium located in the IL1RAPL1 (IL-1 R accessory protein-like) gene: rs12558491 (p = 2.75×10-9) and rs12835699 (p = 1.26×10-6). For both SNPs, the minor allele was associated with lower lung cancer risk. Adjusting for multiple testing, no signal was detected using the Wald or the Xu et al. likelihood ratio tests.
Conclusion: By taking into account smoking behavior and the X-inactivation process, the investigation of the X chromosome has shed a new light on the association between X-linked loci and lung cancer. We identified two loci associated with lung cancer located in the IL1RAPL1 gene. This finding would have been overlooked by examining only results from other test statistics.
导言:肺癌是全球死亡率最高、发病率第二高的癌症。流行病学研究发现,与男性相比,吸烟女性患肺癌的风险更高,但无论吸烟习惯如何,这些性别差异仍存在争议。其中一个假说涉及性染色体的遗传贡献。然而,尽管全基因组关联研究发现了许多肺癌易感基因位点,但这些分析却排除了 X 连锁基因位点:为了考虑非遗传因素,我们首先提出了一种基于加乘危险模型的关联检验,该模型考虑了随机/非随机 X 失活过程。我们进行了一项模拟研究,以考察所提出的检验与随机 X 失活过程的 Cox 模型的 Wald 检验以及 Xu 等人提出的考虑非随机 X 失活过程的部分似然比检验相比的特性。然后,我们对基于人群的队列 CARTaGENE 中的 9,261 人进行了 X 染色体范围的关联研究,以确定当前和过去吸烟者的肺癌易感位点。我们对筛查计划中使用的 PLCOm2012 肺癌风险评分进行了调整:模拟结果表明,与 Xu 等人和 Wald 检验相比,建议的检验在功率和 I 类错误概率方面表现良好。利用所提出的检验统计量并根据 PLCOm2012 评分进行调整,X 染色体范围的统计分析确定了位于 IL1RAPL1(IL-1 R 辅助蛋白样)基因中的两个低连锁不平衡 SNP:rs12558491(p=2.75*10-9)和 rs12835699(p=1.26*10-6)。这两个 SNP 的小等位基因与较低的肺癌风险相关。在对多重检验进行调整后,使用 Wald 或 Xu 等人的似然比检验均未发现任何信号:结论:通过考虑吸烟行为和 X 失活过程,对 X 染色体的研究为 X 连锁基因位点与肺癌的相关性提供了新的线索。我们在 IL1RAPL1 基因中发现了两个与肺癌相关的基因位点。如果只研究其他测试统计的结果,这一发现可能会被忽视。
期刊介绍:
Gathering original research reports and short communications from all over the world, ''Human Heredity'' is devoted to methodological and applied research on the genetics of human populations, association and linkage analysis, genetic mechanisms of disease, and new methods for statistical genetics, for example, analysis of rare variants and results from next generation sequencing. The value of this information to many branches of medicine is shown by the number of citations the journal receives in fields ranging from immunology and hematology to epidemiology and public health planning, and the fact that at least 50% of all ''Human Heredity'' papers are still cited more than 8 years after publication (according to ISI Journal Citation Reports). Special issues on methodological topics (such as ‘Consanguinity and Genomics’ in 2014; ‘Analyzing Rare Variants in Complex Diseases’ in 2012) or reviews of advances in particular fields (‘Genetic Diversity in European Populations: Evolutionary Evidence and Medical Implications’ in 2014; ‘Genes and the Environment in Obesity’ in 2013) are published every year. Renowned experts in the field are invited to contribute to these special issues.