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Open AccessArticle

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

1
U.S. Department of Agriculture, Agricultural Research Service, U.S. Meat Animal Research Center, Clay Center, NE 68933, USA
2
Gencove, Inc., New York, NY 10016, USA
*
Author to whom correspondence should be addressed.
The USDA is an equal opportunity provider and employer. The mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USD.
Genes 2020, 11(11), 1312; https://doi.org/10.3390/genes11111312
Received: 29 September 2020 / Revised: 28 October 2020 / Accepted: 2 November 2020 / Published: 5 November 2020
(This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data)
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 Bos taurus and Bos indicus-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. View Full-Text
Keywords: sequence; imputation; genomic prediction; beef cattle sequence; imputation; genomic prediction; beef cattle
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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.4147100
    Link: https://zenodo.org/record/4147100
    Description: Table S1: Sup1.tsv, tab separated text list of animals in imputation reference, containing ID, SRR Accessions, Source, Breed and number of downsampled progeny examined in this study; Table S2: Sup2.vcf, VCF-formatted text containing snpEff annotation of variants imputed from low-pass sequence; Table S3: Sup3.tsv, tab separated text summarizing number of variants, low-confidence variants and variant spacing in 1 Mb intervals. Figure S1: steerworkflow.pdf, diagram depicting processes to obtain imputed genotypes, (G)EBV and MBV of the study steers; Table S4; Sup4.tsv, tab separated text containing correlations between array- and sequence-based MBV.
MDPI and ACS Style

Snelling, W.M.; Hoff, J.L.; Li, J.H.; Kuehn, L.A.; Keel, B.N.; Lindholm-Perry, A.K.; Pickrell, J.K. Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers. Genes 2020, 11, 1312. https://doi.org/10.3390/genes11111312

AMA Style

Snelling WM, Hoff JL, Li JH, Kuehn LA, Keel BN, Lindholm-Perry AK, Pickrell JK. Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers. Genes. 2020; 11(11):1312. https://doi.org/10.3390/genes11111312

Chicago/Turabian Style

Snelling, Warren M.; Hoff, Jesse L.; Li, Jeremiah H.; Kuehn, Larry A.; Keel, Brittney N.; Lindholm-Perry, Amanda K.; Pickrell, Joseph K. 2020. "Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers" Genes 11, no. 11: 1312. https://doi.org/10.3390/genes11111312

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