Genomic control for association studies
Genomic control for association studies
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DOI：
10.1111/j.0006-341x.1999.00997.x
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发表时间：
1999-12-01
期刊：
影响因子：
1.9
通讯作者：
Roeder, K
中科院分区：
文献类型：
--
作者：
Devlin, B;Roeder, K
一组密集的单核苷酸多态性（SNP）覆盖的基因组和一个有效的方法来评估SNP基因型预计将在不久的将来。一个突出的问题是如何有效地利用这些技术来识别影响复杂疾病易感性的基因。为了实现这一目标，我们提出了一种统计方法，具有几个最佳性能：它可以用于病例对照数据，但像基于家庭的设计，控制人口异质性;它是不敏感的模型假设，如案件不严格独立的通常违反;并且，通过使用贝叶斯离群值方法，它避免了对多个测试进行Bonferroni校正的需要，在许多设置中导致更好的性能，同时仍然限制假阳性的风险。我们的基因组控制方法的性能是相当不错的责任基因，这预示着未来的复杂疾病的遗传分析的合理影响。
A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders. To achieve this goal, we propose a statistical method that has several optimal properties: It can be used with case-control data and yet, like family-based designs, controls for population heterogeneity; it is insensitive to the usual violations of model assumptions, such as cases failing to be strictly independent; and, by using Bayesian outlier methods, it circumvents the need for Bonferroni correction for multiple tests, leading to better performance in many settings while still constraining risk for false positives. The performance of our genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.