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Increasing the Power and Efficiency of Disease-Marker Case-Control Association Studies through Use of Allele-Sharing Information

机译:通过使用等位基因共享信息提高疾病标志物病例对照协会研究的能力和效率

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摘要

Case-control disease-marker association studies are often used in the search for variants that predispose to complex diseases. One approach to increasing the power of these studies is to enrich the case sample for individuals likely to be affected because of genetic factors. In this article, we compare three case-selection strategies that use allele-sharing information with the standard strategy that selects a single individual from each family at random. In affected sibship samples, we show that, by carefully selecting sibships and/or individuals on the basis of allele sharing, we can increase the frequency of disease-associated alleles in the case sample. When these cases are compared with unrelated controls, the difference in the frequency of the disease-associated allele is therefore also increased. We find that, by choosing the affected sib who shows the most evidence for pairwise allele sharing with the other affected sibs in families, the test statistic is increased by >20%, on average, for additive models with modest genotype relative risks. In addition, we find that the per-genotype information associated with the allele sharing–based strategies is increased compared with that associated with random selection of a sib for genotyping. Even though we select sibs on the basis of a nonparametric statistic, the additional gain for selection based on the unknown underlying mode of inheritance is minimal. We show that these properties hold even when the power to detect linkage to a region in the entire sample is negligible. This approach can be extended to more-general pedigree structures and quantitative traits.
机译:病例对照疾病标志物关联研究通常用于寻找易患复杂疾病的变体。提高这些研究能力的一种方法是为可能由于遗传因素而受到影响的个体增加病例样本。在本文中,我们将使用等位基因共享信息的三种病例选择策略与从每个家庭中随机选择一个个体的标准策略进行比较。在受影响的同胞关系样本中,我们表明,通过在等位基因共享的基础上仔细选择同胞关系和/或个体,我们可以增加病例样本中与疾病相关的等位基因的频率。当将这些病例与不相关的对照进行比较时,与疾病相关的等位基因频率的差异也因此增加。我们发现,对于基因型相对风险适度的加性模型,通过选择受影响的同胞与其他受影响的同胞共享最多的成对等位基因共享证据,测试统计量平均增加了20%以上。此外,我们发现,与基于等位基因共享的策略相关的基于基因型的信息比与随机选择同胞进行基因分型相关的信息有所增加。即使我们基于非参数统计选择同胞,基于未知的基础继承模式进行选择所获得的额外收益也很小。我们表明,即使检测到整个样本中某个区域的连锁能力微不足道,这些属性仍然成立。这种方法可以扩展到更一般的血统结构和数量特征。

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