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Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection

Genetics selection evolution (Paris), 2017-05, Vol.49 (1), p.44-44, Article 44 [Peer Reviewed Journal]

Copyright BioMed Central 2017 ;2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. ;Distributed under a Creative Commons Attribution 4.0 International License ;The Author(s) 2017 ;ISSN: 1297-9686 ;ISSN: 0999-193X ;EISSN: 1297-9686 ;DOI: 10.1186/s12711-017-0319-0 ;PMID: 28499345

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Title:
Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection
Author: Fang, Lingzhao ; Sahana, Goutam ; Ma, Peipei ; Su, Guosheng ; Yu, Ying ; Zhang, Shengli ; Lund, Mogens Sandø ; Sørensen, Peter
Subjects: Accuracy ; Allografts ; Animals ; Annotations ; Architecture ; Biopsy ; Cattle ; Correlation ; Dairy industry ; Differential equations ; E coli ; Enrichment ; Female ; Functional anatomy ; Gene expression ; Gene mapping ; Generalized linear models ; Genome-Wide Association Study - methods ; Genome-Wide Association Study - standards ; Genome-Wide Association Study - veterinary ; Genomes ; Genomic Feature ; Genomics ; Infections ; Insects ; Lactation - genetics ; Life Sciences ; Liver ; Liver - metabolism ; Mapping ; Markers ; Mastitis ; Mastitis, Bovine - genetics ; Mathematical models ; Metabolism ; Milk ; Milk - chemistry ; Milk production ; Milk Yield ; Nucleotides ; Ontology ; Pathogens ; Polymorphism, Single Nucleotide ; Prediction Accuracy ; Prediction models ; Protein Yield ; Quantitative Trait, Heritable ; Single-nucleotide polymorphism ; Studies ; Transcriptome ; Weighting
Is Part Of: Genetics selection evolution (Paris), 2017-05, Vol.49 (1), p.44-44, Article 44
Description: A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P < 0.05). GFBLUP provides a framework for integrating multiple layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set test might be used as a first-step to improve GFBLUP models. Approaches like GFBLUP and SNP set test will become increasingly useful, as the functional annotations of genomes keep accumulating for a range of species and traits.
Publisher: France: BioMed Central
Language: English;German
Identifier: ISSN: 1297-9686
ISSN: 0999-193X
EISSN: 1297-9686
DOI: 10.1186/s12711-017-0319-0
PMID: 28499345
Source: Hyper Article en Ligne (HAL) (Open Access)
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Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection

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