Maximum likelihood estimation of haplotype effects and haplotype-environment interactions in association studies
Maximum likelihood estimation of haplotype effects and haplotype-environment interactions in association studies
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DOI：
10.1002/gepi.20098
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发表时间：
2005-12-01
影响因子：
2.1
通讯作者：
Millikan, R
中科院分区：
文献类型：
--
作者：
Lin, DY;Zeng, D;Millikan, R
单倍型和疾病表型之间的关联为复杂疾病的遗传决定因素提供了有价值的线索。由于基因数据中的配子阶段未知，因此对这些关联做出统计推断是非常具有挑战性的。我们描述了一种基于一般可能性的方法，在对无关个体的研究中推断单倍型与疾病的相关性。我们考虑了所有可能的表型(包括疾病指标、数量性状和可能被审查的发病年龄)和所有常用的研究设计(包括横断面、病例对照、队列、嵌套病例对照和病例队列)。单倍型对表型的影响通过适当的回归模型来表征，该模型允许不同的遗传机制和基因-环境交互作用。在Hardy-Weinberg不平衡下，我们给出了所有研究设计和疾病表型的似然函数。相应的最大似然估计器是近似无偏的、正态分布的和统计上有效的。我们提供了简单而有效的数值算法来计算最大似然估计及其方差，并在可免费获得的计算机程序中实现了这些算法。大量的仿真研究表明，所提出的方法在实际情况下表现良好。一项对卡罗莱纳州乳腺癌研究的应用表明，在乳腺癌的发展过程中，单倍型效应和单倍型-吸烟相互作用显著。
The associations between haplotypes and disease phenotypes offer valuable clues about the genetic determinants of complex diseases. It is highly challenging to make statistical inferences about these associations because of the unknown gametic phase in genotype data. We describe a general likelihood-based approach to inferring haplotype-disease associations in studies of unrelated individuals. We consider all possible phenotypes (including disease indicator, quantitative trait, and potentially censored age at onset of disease) and all commonly used study designs (including cross-sectional, case-control, cohort, nested case-control, and case-cohort). The effects of haplotypes on phenotype are characterized by appropriate regression models, which allow various genetic mechanisms and gene-environment interactions. We present the likelihood functions for all study designs and disease phenotypes under Hardy-Weinberg disequilibrium. The corresponding maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We provide simple and efficient numerical algorithms to calculate the maximum likelihood estimators and their variances, and implement these algorithms in a freely available computer program. Extensive simulation studies demonstrate that the proposed methods perform well in realistic situations. An application to the Carolina Breast Cancer Study reveals significant haplotype effects and haplotype-smoking interactions in the development of breast cancer.