Imputation of low-frequency variants using the HapMap3 benefits from large, diverse reference sets
Imputation of low-frequency variants using the HapMap3 benefits from large, diverse reference sets
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DOI：
10.1038/ejhg.2011.10
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发表时间：
2011-06-01
影响因子：
5.2
通讯作者：
Barrett, Jeffrey C.
中科院分区：
文献类型：
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作者：
Jostins, Luke;Morley, Katherine I.;Barrett, Jeffrey C.
插补允许推断低密度数据集中未观察到的基因型，并且通常用于测试标准基因分型芯片很难捕获的变异（例如低频变异）的疾病关联。尽管人们在开发最佳插补算法方面投入了大量精力，但人们对参考集选择对插补精度的影响知之甚少。我们评估了参考大小和多样性增加所带来的改进，特别是比较了迄今为止用于插补的 HapMap2 数据集和新的 HapMap3 数据集，其中包含来自更多样化人群的更多样本。我们发现，对于西欧样本的插补，HapMap3 参考比 HapMap2 提供了更准确的插补和更好的校准质量分数，并且增加参考集中包含的 HapMap3 群体数量可以进一步改进。低频变体的改进最为明显（频率
Imputation allows the inference of unobserved genotypes in low-density data sets, and is often used to test for disease association at variants that are poorly captured by standard genotyping chips (such as low-frequency variants). Although much effort has gone into developing the best imputation algorithms, less is known about the effects of reference set choice on imputation accuracy. We assess the improvements afforded by increases in reference size and diversity, specifically comparing the HapMap2 data set, which has been used to date for imputation, and the new HapMap3 data set, which contains more samples from a more diverse range of populations. We find that, for imputation into Western European samples, the HapMap3 reference provides more accurate imputation with better-calibrated quality scores than HapMap2, and that increasing the number of HapMap3 populations included in the reference set grant further improvements. Improvements are most pronounced for low-frequency variants (frequency