Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Snelling, Warren M., Hoff, Jesse L., Li, Jeremiah H., Kuehn, Larry A., Keel, Brittney N., Lindholm-Perry, Amanda K., Pickrell, Joseph K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7716200/
https://www.ncbi.nlm.nih.gov/pubmed/33167493
http://dx.doi.org/10.3390/genes11111312
_version_ 1783619112223637504
author Snelling, Warren M.
Hoff, Jesse L.
Li, Jeremiah H.
Kuehn, Larry A.
Keel, Brittney N.
Lindholm-Perry, Amanda K.
Pickrell, Joseph K.
author_facet Snelling, Warren M.
Hoff, Jesse L.
Li, Jeremiah H.
Kuehn, Larry A.
Keel, Brittney N.
Lindholm-Perry, Amanda K.
Pickrell, Joseph K.
author_sort Snelling, Warren M.
collection PubMed
description 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 ([Formula: see text]) and the genotypes called from the transcript sequence ([Formula: see text]). 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.
format Online
Article
Text
id pubmed-7716200
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-77162002020-12-05 Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers Snelling, Warren M. Hoff, Jesse L. Li, Jeremiah H. Kuehn, Larry A. Keel, Brittney N. Lindholm-Perry, Amanda K. Pickrell, Joseph K. Genes (Basel) Article 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 ([Formula: see text]) and the genotypes called from the transcript sequence ([Formula: see text]). 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. MDPI 2020-11-05 /pmc/articles/PMC7716200/ /pubmed/33167493 http://dx.doi.org/10.3390/genes11111312 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Snelling, Warren M.
Hoff, Jesse L.
Li, Jeremiah H.
Kuehn, Larry A.
Keel, Brittney N.
Lindholm-Perry, Amanda K.
Pickrell, Joseph K.
Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers
title Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers
title_full Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers
title_fullStr Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers
title_full_unstemmed Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers
title_short Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers
title_sort assessment of imputation from low-pass sequencing to predict merit of beef steers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7716200/
https://www.ncbi.nlm.nih.gov/pubmed/33167493
http://dx.doi.org/10.3390/genes11111312
work_keys_str_mv AT snellingwarrenm assessmentofimputationfromlowpasssequencingtopredictmeritofbeefsteers
AT hoffjessel assessmentofimputationfromlowpasssequencingtopredictmeritofbeefsteers
AT lijeremiahh assessmentofimputationfromlowpasssequencingtopredictmeritofbeefsteers
AT kuehnlarrya assessmentofimputationfromlowpasssequencingtopredictmeritofbeefsteers
AT keelbrittneyn assessmentofimputationfromlowpasssequencingtopredictmeritofbeefsteers
AT lindholmperryamandak assessmentofimputationfromlowpasssequencingtopredictmeritofbeefsteers
AT pickrelljosephk assessmentofimputationfromlowpasssequencingtopredictmeritofbeefsteers