Pseudosibship methods in the case-parents design.
Pseudosibship methods in the case-parents design.
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DOI：
10.1002/sim.4397
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发表时间：
2011-11-30
影响因子：
2
通讯作者：
Deng, Li
中科院分区：
文献类型：
--
作者：
Yu, Zhaoxia;Deng, Li
最近的证据表明，复杂的性状很可能是由多个基因座决定的，每个基因座都有微弱到中等的个体效应。尽管存在大量关于无关对象的多基因座分析的文献，但使用家族数据联合分析多个基因座的策略相对较少。在这里，我们通过评估两种伪同胞关系方法来解决这个问题：1：1匹配，它将每个受影响的后代与未传递给受影响后代的等位基因形成的伪兄弟姐妹进行匹配；穷举匹配，将每个受影响的后代与由所有其他可能的亲本等位基因组合形成的伪兄弟姐妹进行匹配。我们证明了这两种匹配策略分别在加性遗传模型和乘性遗传模型下使用完全相同和大致相同的信息量。在各种模型和检验假设下的数值计算表明，与穷举匹配法相比，1：1匹配法在检测单基因座分析中的乘性/加性效应和多基因座分析中的主效应方面具有相当的渐近能力，并允许对多个连锁基因座进行关联检验。这些结果为为病例对照(或配对病例对照)设计开发的许多现有多位点分析方法在稍作修改后应用于病例-父母资料铺平了道路。例如，在1：1匹配的情况下，我们将L1正则化回归应用于克罗恩病数据集。使用我们的方法选择的多个基因座，与使用最显著的单个基因座相比，我们的p值下降了一个数量级，预测精度提高了18.9%。
Recent evidence suggests that complex traits are likely determined by multiple loci, with each of which contributes a weak to moderate individual effect. Although extensive literature exists on multi-locus analysis of unrelated subjects, there are relatively fewer strategies for jointly analyzing multiple loci using family data. Here we address this issue by evaluating two pseudo-sibship methods: the 1:1 matching, which matches each affected offspring to the pseudo sibling formed by the alleles not transmitted to the affected offspring; the exhaustive matching, which matches each affected offspring to the pseudo siblings formed by all the other possible combinations of parental alleles. We prove that the two matching strategies use exactly and approximately the same amount of information from data under additive and multiplicative genetic models, respectively. Using numerical calculations under a variety of models and testing assumptions, we show that compared to the exhaustive matching, the 1:1 matching has comparable asymptotic power in detecting multiplicative / additive effects in single-locus analysis and main effects in multi-locus analysis, and it allows association testing of multiple linked loci. These results pave the way for many existing multi-locus analysis methods developed for the case-control (or matched case-control) design to be applied to case-parents data with minor modifications. As an example, with the 1:1 matching, we applied an L1 regularized regression to a Crohn’s disease dataset. Using the multiple loci selected by our approach, we obtained an order-of-magnitude decrease in p-value and an 18.9% increase in prediction accuracy when comparing to using the most significant individual locus.
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