Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs.
Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs.
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DOI：
10.1186/1755-8794-5-12
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发表时间：
2012-05-01
影响因子：
2.7
通讯作者：
Parra EJ
中科院分区：
文献类型：
--
作者：
Krithika S;Valladares-Salgado A;Peralta J;Escobedo-de La Peña J;Kumate-Rodríguez J;Cruz M;Parra EJ
我们在墨西哥城的混合样本中探索了IMPUTE程序的归算性能。评估了以下问题：(a)不同参考面板（HapMap vs. 1000基因组）对代入的影响；(b)单步法与两步法（分相法和归算法）在归算性能上的潜在差异；(c)不同INFO分数阈值对代入性能的影响；(d)常见与罕见标记的代入性能。来自墨西哥城的样本包括1310个个体，使用Affymetrix 5.0阵列进行基因分型。我们在12号染色体上随机屏蔽5%的直接基因型标记（n = 1,046），并将输入的基因型与微阵列基因型呼叫进行比较。用IMPUTE程序进行计算。输入和观察基因型之间的一致性率被用作输入准确性的衡量标准，而非缺失基因型的比例被用作输入有效性的衡量标准。单步插入方法产生的一致性率略高于两步策略（使用HapMap II期联合面板时为99.1%对98.4%），但代价是非缺失基因型比例较低（85.5%对90.1%）。1000个基因组参考样本产生的一致性率与HapMap第二阶段小组相似（使用两步策略，两个数据集的一致性为98.4%）。然而，1000个基因组参考样本的非缺失基因型比例显著增加（94.7%比90.1%）。罕见变异（<1%）的植入准确性和有效性低于普通标记。该程序对来自墨西哥城的混合样本中的共同等位基因具有出色的归算性能，该样本主要由美洲原住民（62%）和欧洲人（33%）贡献。尽管在HapMap和1000个基因组参考面板中没有印第安人样本，但使用所有插算策略的基因型一致性均高于98.4%。在1000个基因组面板上获得了最佳的插入精度和效率平衡。任何可用的小组都不能有效地捕获稀有变异，强调在解释输入的稀有变异的关联结果时需要谨慎。
We explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers. The sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n = 1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy. The single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers. The program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.
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