Genetic epidemiologists routinely assess disease susceptibility in relation to haplotypes, that is, combinations of alleles on a single chromosome. We study statistical methods for inferring haplotype-related disease risk using single nucleotide polymorphism (SNP) genotype data from matched case-control studies, where controls are individually matched to cases on some selected factors. Assuming a logistic regression model for haplotype-disease association, we propose two conditional likelihood approaches that address the issue that haplotypes cannot be inferred with certainty from SNP genotype data (phase ambiguity). One approach is based on the likelihood of disease status conditioned on the total number of cases, genotypes, and other covariates within each matching stratum, and the other is based on the joint likelihood of disease status and genotypes conditioned only on the total number of cases and other covariates. The joint-likelihood approach is generally more efficient, particularly for assessing haplotype-environment interactions. Simulation studies demonstrated that the first approach was more robust to model assumptions on the diplotype distribution conditioned on environmental risk variables and matching factors in the control population. We applied the two methods to analyze a matched case-control study of prostate cancer.

译文

遗传流行病学家通常评估与单倍型 (即单个染色体上等位基因的组合) 相关的疾病易感性。我们研究了使用来自匹配病例对照研究的单核苷酸多态性 (SNP) 基因型数据来推断单倍型相关疾病风险的统计方法,其中对照在某些选定因素上与病例单独匹配。假设单倍型-疾病关联的逻辑回归模型,我们提出了两种条件似然方法,以解决无法从SNP基因型数据 (阶段模糊性) 确定性推断单倍型的问题。一种方法是基于疾病状态的可能性,以每个匹配层中的病例总数,基因型和其他协变量为条件,另一种方法是基于疾病状态和基因型的联合可能性,仅以病例总数和其他协变量为条件。联合似然方法通常更有效,特别是对于评估单倍型-环境相互作用。模拟研究表明,第一种方法对以环境风险变量和对照人群中的匹配因子为条件的二倍体型分布的模型假设更稳健。我们应用这两种方法分析了一项匹配的前列腺癌病例对照研究。

+1
+2
100研值 100研值 ¥99课程
检索文献一次
下载文献一次

去下载>

成功解锁2个技能,为你点赞

《SCI写作十大必备语法》
解决你的SCI语法难题!

技能熟练度+1

视频课《玩转文献检索》
让你成为检索达人!

恭喜完成新手挑战

手机微信扫一扫,添加好友领取

免费领《Endnote文献管理工具+教程》

微信扫码, 免费领取

手机登录

获取验证码
登录