Genotype imputation in the domestic dog.

Genotype imputation in the domestic dog.
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DOI:
10.1007/s00335-016-9636-9
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发表时间:
2016-10
期刊:
影响因子:
2.5
通讯作者:
Meurs, K. M.
Meurs, K. M.
中科院分区:
生物学4区
文献类型:
--
作者:
Friedenberg, S. G.;Meurs, K. M.

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应用插补方法准确预测狗中密集的 SNP 基因型阵列可以为当前基于阵列的基因分型数据分析提供重要的补充。在这里,我们利用全基因组测序在 15 个品种的 83 只狗中开发了一个包含 4,885,283 个 SNP 的参考面板。我们使用该面板预测了三个品种的 268 只狗的基因型,并以 84,193 个 SNP 阵列衍生的基因型作为输入。然后,我们(1)对实际数据和估算数据进行品种聚类; (2) 评估多个参考小组品种组合以确定最佳参考小组组成; (3) 比较两种常用软件算法(Beagle 和 IMPUTE2)的准确性。品种聚类在特征值的插补过程中得到了很好的保留,代表了插补数据中 75% 的变异。使用比格犬和来自单一品种的目标小组,与特定品种的参考小组 (87.0%) 或不包含与目标小组重叠的品种的参考小组 (74.9%) 相比,使用多品种参考小组 (92.4%) 的基因型一致性最高。使用来自其他两个品种的目标组证实了这一发现。此外,使用多品种参考组,与 Beagle 相比,IMPUTE2 的基因型一致性略高(94.1%);两个软件包的 Pearson 相关系数均略高(Beagle 为 0.946,IMPUTE2 为 0.961)。我们的研究结果表明,在目标组和参考组之间具有适当品种重叠的狗中,从 SNP 阵列衍生数据到全基因组水平基因型的基因型插补是可行且准确的。
Application of imputation methods to accurately predict a dense array of SNP genotypes in the dog could provide an important supplement to current analyses of array-based genotyping data. Here, we developed a reference panel of 4,885,283 SNPs in 83 dogs across 15 breeds using whole genome sequencing. We used this panel to predict the genotypes of 268 dogs across three breeds with 84,193 SNP array-derived genotypes as inputs. We then (1) performed breed clustering of the actual and imputed data; (2) evaluated several reference panel breed combinations to determine an optimal reference panel composition; and (3) compared the accuracy of two commonly used software algorithms (Beagle and IMPUTE2). Breed clustering was well preserved in the imputation process across eigenvalues representing 75 % of the variation in the imputed data. Using Beagle with a target panel from a single breed, genotype concordance was highest using a multi-breed reference panel (92.4 %) compared to a breed-specific reference panel (87.0 %) or a reference panel containing no breeds overlapping with the target panel (74.9 %). This finding was confirmed using target panels derived from two other breeds. Additionally, using the multi-breed reference panel, genotype concordance was slightly higher with IMPUTE2 (94.1 %) compared to Beagle; Pearson correlation coefficients were slightly higher for both software packages (0.946 for Beagle, 0.961 for IMPUTE2). Our findings demonstrate that genotype imputation from SNP array-derived data to whole genome-level genotypes is both feasible and accurate in the dog with appropriate breed overlap between the target and reference panels.
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