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低深度测序预测肉牛优秀程度的插补评估。

Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers.

机构信息

U.S. Department of Agriculture, Agricultural Research Service, U.S. Meat Animal Research Center, Clay Center, NE 68933, USA.

Gencove, Inc., New York, NY 10016, USA.

出版信息

Genes (Basel). 2020 Nov 5;11(11):1312. doi: 10.3390/genes11111312.

Abstract

Decreasing costs are making low coverage sequencing with imputation to a comprehensive reference panel an attractive alternative to obtain functional variant genotypes that can increase the accuracy of genomic prediction. To assess the potential of low-pass sequencing, genomic sequence of 77 steers sequenced to >10X coverage was downsampled to 1X and imputed to a reference of 946 cattle representing multiple and -influenced breeds. Genotypes for nearly 60 million variants detected in the reference were imputed from the downsampled sequence. The imputed genotypes strongly agreed with the SNP array genotypes (r¯=0.99) and the genotypes called from the transcript sequence (r¯=0.97). Effects of BovineSNP50 and GGP-F250 variants on birth weight, postweaning gain, and marbling were solved without the steers' phenotypes and genotypes, then applied to their genotypes, to predict the molecular breeding values (MBV). The steers' MBV were similar when using imputed and array genotypes. Replacing array variants with functional sequence variants might allow more robust MBV. Imputation from low coverage sequence offers a viable, low-cost approach to obtain functional variant genotypes that could improve genomic prediction.

摘要

降低成本使得使用低覆盖率测序并进行综合参考面板的插补成为获得可提高基因组预测准确性的功能变异基因型的有吸引力的替代方法。为了评估低通量测序的潜力,对 77 头公牛进行了 >10X 覆盖率的基因组测序,然后将其下采样到 1X 并进行插补,以获得代表多种 和 -影响品种的 946 头牛的参考。从下采样序列中对参考中检测到的近 6000 万个变体的基因型进行了插补。插补的基因型与 SNP 芯片基因型(r¯=0.99)和转录序列中调用的基因型(r¯=0.97)高度一致。在没有牛的表型和基因型的情况下,解决了 BovineSNP50 和 GGP-F250 变体对出生体重、断奶后增重和大理石纹的影响,然后将其应用于它们的基因型,以预测分子育种值(MBV)。使用插补和数组基因型时,牛的 MBV 相似。用功能序列变体替代数组变体可能会允许更稳健的 MBV。从低覆盖率序列进行插补提供了一种可行的、低成本的方法来获得可提高基因组预测准确性的功能变异基因型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8092/7716200/43a2adf61552/genes-11-01312-g001a.jpg

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