Accuracy of genotype imputation in sheep breeds
Accuracy of genotype imputation in sheep breeds
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DOI：
10.1111/j.1365-2052.2011.02208.x
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发表时间：
2012-02-01
期刊：
影响因子：
2.4
通讯作者：
van der Werf, J. H. J.
中科院分区：
文献类型：
--
作者：
Hayes, B. J.;Bowman, P. J.;van der Werf, J. H. J.
虽然基因组选择提供了前景，提高遗传增益率的肉羊，羊毛和奶羊育种计划，关键的制约因素可能是基因分型的成本。潜在地，该约束可以通过对具有稀疏基因型覆盖的低密度（低成本）SNP组的基因分型选择候选物来克服，使用密集基因分型的参考群体来估算高得多的SNP基因型密度。这些插补的基因型然后将与预测方程一起使用以产生基因组估计育种值。在未来，也可能需要从中等密度SNP组中估算非常密集的标记基因型或甚至全基因组重测序数据。例如，这样的策略可以导致跨品种的基因组估计育种值的准确预测。我们使用来自4个绵羊品种的48640（50K）SNP基因分型的基因型，以研究从低密度SNP面板中估算50K SNP的准确性，以及从50K SNP中估算非常密集或全基因组重测序数据的前景（通过随机忽略少量的50K SNP）。如果稀疏组具有少于5000（5K）个标记，则插补的准确性低。在品种之间，很明显，如果品种内的遗传多样性较低，则从稀疏标记面板到50K的插补准确性较高，使得该品种中动物之间的关系较高。从稀疏基因型到50K基因型的插补的准确性更高时，插补是在品种内，而不是当汇集所有的数据，尽管事实上，汇集的参考集是更大。对于Border Leicesters、Poll Dorsets和白色Suffolks，5 K稀疏基因型足以以80%的准确度估算50 K。对于美利奴羊，从5K估算50K的准确性较低，为71%，尽管大量的动物具有完整的基因型（2215）被用作参考。对于所有品种，个体与参考的关系解释了高达64%的插补准确性变异，表明如果要插补的个体的父系和其他祖先包括在参考群体中，插补的准确性可以提高。如果系谱信息可用，并用于追踪家族内大染色体片段的遗传，插补的准确性也可以提高。在我们的研究中，我们只考虑了基于全人群连锁不平衡的插补方法（主要是因为某些人群的系谱不完整）。最后，在设计用于模拟来自50K面板的高密度或全基因组重测序数据的插补的场景中，插补的准确性要高得多（8696%）。这是有希望的，表明如果为每个品种测序一个合适的关键祖先库，那么在绵羊中进行计算机基因组重新测序是可能的。
Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re-sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re-sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population-wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re-sequence data from the 50K panel, the accuracy of imputation was much higher (8696%). This is promising, suggesting that in silico genome re-sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed.