QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection

Abstract Background National genetic evaluations for disease resistance do not exist, precluding the genetic improvement of cattle for these traits. We imputed BovineHD genotypes to whole genome sequence for 2703 Holsteins that were cases or controls for Bovine Respiratory Disease and sampled from e...

Full description

Bibliographic Details
Main Authors: Jesse L. Hoff, Jared E. Decker, Robert D. Schnabel, Christopher M. Seabury, Holly L. Neibergs, Jeremy F. Taylor
Format: Article
Language:English
Published: BMC 2019-07-01
Series:BMC Genomics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12864-019-5941-5
id doaj-2cf7aa554d554503ba74cc0514c26761
record_format Article
spelling doaj-2cf7aa554d554503ba74cc0514c267612020-11-25T03:18:58ZengBMCBMC Genomics1471-21642019-07-0120111510.1186/s12864-019-5941-5QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selectionJesse L. Hoff0Jared E. Decker1Robert D. Schnabel2Christopher M. Seabury3Holly L. Neibergs4Jeremy F. Taylor5Division of Animal Sciences, University of MissouriDivision of Animal Sciences, University of MissouriDivision of Animal Sciences, University of MissouriDepartment of Veterinary Pathobiology, Texas A&M UniversityDepartment of Animal Sciences, Washington State UniversityDivision of Animal Sciences, University of MissouriAbstract Background National genetic evaluations for disease resistance do not exist, precluding the genetic improvement of cattle for these traits. We imputed BovineHD genotypes to whole genome sequence for 2703 Holsteins that were cases or controls for Bovine Respiratory Disease and sampled from either California or New Mexico to construct and compare genomic prediction models. The sequence variation reference dataset comprised variants called for 1578 animals from Run 5 of the 1000 Bull Genomes Project, including 450 Holsteins and 29 animals sequenced from this study population. Genotypes for 9,282,726 variants with minor allele frequencies ≥5% were imputed and used to obtain genomic predictions in GEMMA using a Bayesian Sparse Linear Mixed Model. Results Variation explained by markers increased from 13.6% using BovineHD data to 14.4% using imputed whole genome sequence data and the resolution of genomic regions detected as harbouring QTL substantially increased. Explained variation in the analysis of the combined California and New Mexico data was less than when data for each state were separately analysed and the estimated genetic correlation between risk of Bovine Respiratory Disease in California and New Mexico Holsteins was − 0.36. Consequently, genomic predictions trained using the data from one state did not accurately predict disease risk in the other state. To determine if a prediction model could be developed with utility in both states, we selected variants within genomic regions harbouring: 1) genes involved in the normal immune response to infection by pathogens responsible for Bovine Respiratory Disease detected by RNA-Seq analysis, and/or 2) QTL identified in the association analysis of the imputed sequence variants. The model based on QTL selected variants is biased but when trained in one state generated BRD risk predictions with positive accuracies in the other state. Conclusions We demonstrate the utility of sequence-based and biology-driven model development for genomic selection. Disease phenotypes cannot be routinely recorded in most livestock species and the observed phenotypes may vary in their genomic architecture due to variation in the pathogen composition across environments. Elucidation of trait biology and genetic architecture may guide the development of prediction models with utility across breeds and environments.http://link.springer.com/article/10.1186/s12864-019-5941-5Genomic predictionBovine respiratory diseaseGenome sequence imputationRNA-SeqQuantitative trait locusFeature selection
collection DOAJ
language English
format Article
sources DOAJ
author Jesse L. Hoff
Jared E. Decker
Robert D. Schnabel
Christopher M. Seabury
Holly L. Neibergs
Jeremy F. Taylor
spellingShingle Jesse L. Hoff
Jared E. Decker
Robert D. Schnabel
Christopher M. Seabury
Holly L. Neibergs
Jeremy F. Taylor
QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection
BMC Genomics
Genomic prediction
Bovine respiratory disease
Genome sequence imputation
RNA-Seq
Quantitative trait locus
Feature selection
author_facet Jesse L. Hoff
Jared E. Decker
Robert D. Schnabel
Christopher M. Seabury
Holly L. Neibergs
Jeremy F. Taylor
author_sort Jesse L. Hoff
title QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection
title_short QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection
title_full QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection
title_fullStr QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection
title_full_unstemmed QTL-mapping and genomic prediction for bovine respiratory disease in U.S. Holsteins using sequence imputation and feature selection
title_sort qtl-mapping and genomic prediction for bovine respiratory disease in u.s. holsteins using sequence imputation and feature selection
publisher BMC
series BMC Genomics
issn 1471-2164
publishDate 2019-07-01
description Abstract Background National genetic evaluations for disease resistance do not exist, precluding the genetic improvement of cattle for these traits. We imputed BovineHD genotypes to whole genome sequence for 2703 Holsteins that were cases or controls for Bovine Respiratory Disease and sampled from either California or New Mexico to construct and compare genomic prediction models. The sequence variation reference dataset comprised variants called for 1578 animals from Run 5 of the 1000 Bull Genomes Project, including 450 Holsteins and 29 animals sequenced from this study population. Genotypes for 9,282,726 variants with minor allele frequencies ≥5% were imputed and used to obtain genomic predictions in GEMMA using a Bayesian Sparse Linear Mixed Model. Results Variation explained by markers increased from 13.6% using BovineHD data to 14.4% using imputed whole genome sequence data and the resolution of genomic regions detected as harbouring QTL substantially increased. Explained variation in the analysis of the combined California and New Mexico data was less than when data for each state were separately analysed and the estimated genetic correlation between risk of Bovine Respiratory Disease in California and New Mexico Holsteins was − 0.36. Consequently, genomic predictions trained using the data from one state did not accurately predict disease risk in the other state. To determine if a prediction model could be developed with utility in both states, we selected variants within genomic regions harbouring: 1) genes involved in the normal immune response to infection by pathogens responsible for Bovine Respiratory Disease detected by RNA-Seq analysis, and/or 2) QTL identified in the association analysis of the imputed sequence variants. The model based on QTL selected variants is biased but when trained in one state generated BRD risk predictions with positive accuracies in the other state. Conclusions We demonstrate the utility of sequence-based and biology-driven model development for genomic selection. Disease phenotypes cannot be routinely recorded in most livestock species and the observed phenotypes may vary in their genomic architecture due to variation in the pathogen composition across environments. Elucidation of trait biology and genetic architecture may guide the development of prediction models with utility across breeds and environments.
topic Genomic prediction
Bovine respiratory disease
Genome sequence imputation
RNA-Seq
Quantitative trait locus
Feature selection
url http://link.springer.com/article/10.1186/s12864-019-5941-5
work_keys_str_mv AT jesselhoff qtlmappingandgenomicpredictionforbovinerespiratorydiseaseinusholsteinsusingsequenceimputationandfeatureselection
AT jarededecker qtlmappingandgenomicpredictionforbovinerespiratorydiseaseinusholsteinsusingsequenceimputationandfeatureselection
AT robertdschnabel qtlmappingandgenomicpredictionforbovinerespiratorydiseaseinusholsteinsusingsequenceimputationandfeatureselection
AT christophermseabury qtlmappingandgenomicpredictionforbovinerespiratorydiseaseinusholsteinsusingsequenceimputationandfeatureselection
AT hollylneibergs qtlmappingandgenomicpredictionforbovinerespiratorydiseaseinusholsteinsusingsequenceimputationandfeatureselection
AT jeremyftaylor qtlmappingandgenomicpredictionforbovinerespiratorydiseaseinusholsteinsusingsequenceimputationandfeatureselection
_version_ 1724624560826875904