Comparison of multilevel modeling and the family-based association test for identifying genetic variants associated with systolic and diastolic blood pressure using Genetic Analysis Workshop 18 simulated data.
Comparison of multilevel modeling and the family-based association test for identifying genetic variants associated with systolic and diastolic blood pressure using Genetic Analysis Workshop 18 simulated data.
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DOI：
10.1186/1753-6561-8-s1-s30
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Shete S
中科院分区：
文献类型：
--
作者：
Wang J;Yu R;Shete S
识别与复杂疾病相关的遗传变异是遗传研究中的一项重要任务。尽管基于无关个体的关联研究（即病例对照全基因组关联研究）已成功鉴定出许多复杂疾病的常见单核苷酸多态性，但这些研究不太可能鉴定出罕见的遗传变异。相比之下，基于家庭的关联研究对于识别罕见变异关联特别有用。最近，人们对在基于家族的遗传关联研究中采用多层次模型产生了一些兴趣。然而，此类模型在这些研究中的性能，尤其是基于纵向家族的序列数据，尚未得到充分研究。因此，在本研究中，我们研究了多级模型在基于家族的遗传关联分析中的性能，并将其与传统的基于家族的关联测试进行比较，通过使用遗传分析研讨会18模拟数据中的3个数据集（全基因组关联单核苷酸多态性数据、序列数据和仅稀有变异数据）检查2种方法的功效和I型错误率。与基于单变量家族的关联检验相比，多水平模型利用全基因组关联单核苷酸多态性数据和序列数据识别大多数因果遗传变异的功效略高。然而，这两种方法识别大多数因果单核苷酸多态性的能力都很低，尤其是那些相对罕见的遗传变异。因此，我们建议采用一种统一的方法，将两种方法结合起来并结合折叠策略，这可能比单独使用基于家族的数据研究遗传关联的任何一种方法更强大。
Identifying genetic variants associated with complex diseases is an important task in genetic research. Although association studies based on unrelated individuals (ie, case-control genome-wide association studies) have successfully identified common single-nucleotide polymorphisms for many complex diseases, these studies are not so likely to identify rare genetic variants. In contrast, family-based association studies are particularly useful for identifying rare-variant associations. Recently, there has been some interest in employing multilevel models in family-based genetic association studies. However, the performance of such models in these studies, especially for longitudinal family-based sequence data, has not been fully investigated. Therefore, in this study, we investigated the performance of the multilevel model in the family-based genetic association analysis and compared it with the conventional family-based association test, by examining the powers and type I error rates of the 2 approaches using 3 data sets from the Genetic Analysis Workshop 18 simulated data: genome-wide association single-nucleotide polymorphism data, sequence data, and rare-variants-only data. Compared with the univariate family-based association test, the multilevel model had slightly higher power to identify most of the causal genetic variants using the genome-wide association single-nucleotide polymorphism data and sequence data. However, both approaches had low power to identify most of the causal single-nucleotide polymorphisms, especially those among the relatively rare genetic variants. Therefore, we suggest a unified method that combines both approaches and incorporates collapsing strategy, which may be more powerful than either approach alone for studying genetic associations using family-based data.