Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data.
Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data.
复制标题
基于核心家庭和无关受试者的基于可能性的基因型数据的关联分析。
DOI：
10.1159/000119108
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发表时间：
2008
期刊：
影响因子：
1.8
通讯作者：
Dudbridge, Frank
中科院分区：
文献类型：
--
作者：
Dudbridge, Frank
关键词：
在遗传关联研究中，数据缺失的原因有很多，包括缺失家庭成员和不确定的单倍型阶段。最大似然是一种常用的方法，以适应缺失的数据，但它可能很难适用于家庭为基础的关联研究，因为可能会失去鲁棒性的人口分层的混杂。在这里，一个新的可能性的核心家庭，其中不同的关联参数集被用来模拟父母的基因型和后代的基因型。当数据完整时，该方法对总体结构具有鲁棒性，并且当存在缺失数据时，鲁棒性仅略有损失。它还允许一个新的条件反射步骤，在连锁存在的情况下对多个后代进行有效的分析。将不相关的受试者视为双亲失踪的子女。模拟和理论表明类似的操作特性，传输，但没有偏见的缺失数据的存在联系。与FBAT和PCPH相比，该模型对种群结构的鲁棒性稍差，但对强效应的检测能力更强。与APL和MITDT相比，该模型对分层更鲁棒，并且可以容纳任何大小的同胞。该方法实现了二进制和连续性状的软件，UNPHASED，可从作者。
Missing data occur in genetic association studies for several reasons including missing family members and uncertain haplotype phase. Maximum likelihood is a commonly used approach to accommodate missing data, but it can be difficult to apply to family-based association studies, because of possible loss of robustness to confounding by population stratification. Here a novel likelihood for nuclear families is proposed, in which distinct sets of association parameters are used to model the parental genotypes and the offspring genotypes. This approach is robust to population structure when the data are complete, and has only minor loss of robustness when there are missing data. It also allows a novel conditioning step that gives valid analysis for multiple offspring in the presence of linkage. Unrelated subjects are included by regarding them as the children of two missing parents. Simulations and theory indicate similar operating characteristics to TRANSMIT, but with no bias with missing data in the presence of linkage. In comparison with FBAT and PCPH, the proposed model is slightly less robust to population structure but has greater power to detect strong effects. In comparison to APL and MITDT, the model is more robust to stratification and can accommodate sibships of any size. The methods are implemented for binary and continuous traits in software, UNPHASED, available from the author.
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