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首页> 外文期刊>Open Journal of Genetics >An Adaptive Weighted Sum Test for Family-Based Multi-Marker Association Studies
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An Adaptive Weighted Sum Test for Family-Based Multi-Marker Association Studies

机译:基于家庭的多标记关联研究的自适应加权和检验

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Backgrounds: Although many disease-associated common variants have been discovered through genome-wide association studies, much of the genetic effects of complex diseases have not been explained. Population-based association studies are vulnerable to population stratification. A possible solution is to use family-based tests. However, if tests only estimate the genetic effect from the within-family variation to avoid population stratification, they may ignore the useful genetic information from between-family variation and lose power. Methods: We have developed an adaptive weighted sum test for family-based association studies. The new test uses data driven weights to combine two test statistics, and the weights measure the strength of population stratification. When population stratification is strong, the proposed test will automatically put more weight on one statistic derived from within-family variation to maintain robustness against spurious positives. On the other hand, when the effect of population stratification is relatively weak, the proposed test will automatically put more weight on the other statistic derived from both within-family and between-family variation to make use of both sources of genetic variation; and at the same time, the degrees of freedom of the test will be reduced and power of the test will be increased. Results: In our study, the proposed method achieves a higher power in most scenarios of linkage disequilibrium structure as well as Hap Map data from different genes under different population structures while still keeping its robustness against population stratification.
机译:背景:尽管通过全基因组关联研究发现了许多与疾病相关的常见变异,但尚未解释复杂疾病的许多遗传效应。基于人口的协会研究容易受到人口分层的影响。一种可能的解决方案是使用基于家庭的测试。但是,如果测试仅估计家庭内部变异的遗传效应以避免种群分层,则它们可能会忽略家庭之间变异的有用遗传信息,从而丧失能力。方法:我们为基于家庭的关联研究开发了自适应加权和检验。新测试使用数据驱动的权重来合并两个测试统计信息,权重用于衡量总体分层的强度。当人口分层很强时,建议的测试将自动对来自家庭内部变异的一项统计数据施加更大的权重,以保持对虚假阳性的鲁棒性。另一方面,当人口分层的影响相对较弱时,建议的检验将自动增加来自家庭内部和家庭之间变异的其他统计数据的权重,以利用两种遗传变异的来源;同时,将降低测试的自由度,并提高测试的功效。结果:在我们的研究中,该方法在大多数连锁不平衡结构以及来自不同种群结构下不同基因的Hap Map数据的大多数情况下均具有更高的功效,同时仍然保持了针对种群分层的鲁棒性。

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