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Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study

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Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle : A Simulation Study. / van den Berg, Irene; Boichard, Didier; Guldbrandtsen, Bernt et al.

In: G3: Genes, Genomes, Genetics, Vol. 6, No. 8, 2016, p. 2553-2561.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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van den Berg, Irene ; Boichard, Didier ; Guldbrandtsen, Bernt et al. / Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle : A Simulation Study. In: G3: Genes, Genomes, Genetics. 2016 ; Vol. 6, No. 8. pp. 2553-2561.

Bibtex

@article{aad90fbeef2d42cc8aef2c1d5942dd8f,
title = "Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study",
abstract = "Sequence data is expected to increase the reliability of genomic prediction by containing causative mutations directly, especially in cases where low linkage disequilibrium between markers and causative mutations limits prediction reliability, such as across breed prediction in dairy cattle. In practice, the causative mutations are unknown, and prediction with only variants in perfect linkage disequilibrium with the causative mutations is not realistic, leading to a reduced reliability compared to knowing the causative variants. Our objective was to use sequence data to investigate the potential benefits of sequence data for the prediction of genomic relationships and consequently reliability of genomic breeding values. We used sequence data from five dairy cattle breeds, and a larger number of imputed sequences for two of the five breeds. We focused on the influence of linkage disequilibrium between markers and causative mutations, and assumed that a fraction of the causative mutations was shared across breeds and had the same effect across breeds. By comparing the loss in reliability of different scenarios, varying the distance between markers and causative mutations, using either all genome wide markers from commercial SNP chips, or only the markers closest to the causative mutations, we demonstrate the importance of using only variants very close to the causative mutations, especially for across breed prediction. Rare variants improved prediction only if they were very close to rare causative mutations and all causative mutations were rare. Our results show that sequence data can potentially improve genomic prediction, but careful selection of markers is essential.",
author = "{van den Berg}, Irene and Didier Boichard and Bernt Guldbrandtsen and Lund, {Mogens S}",
note = "Copyright {\textcopyright} 2016 Author et al.",
year = "2016",
doi = "10.1534/g3.116.027730",
language = "English",
volume = "6",
pages = "2553--2561",
journal = "G3: Genes, Genomes, Genetics (Bethesda)",
issn = "2160-1836",
publisher = "Genetics Society of America",
number = "8",

}

RIS

TY - JOUR

T1 - Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle

T2 - A Simulation Study

AU - van den Berg, Irene

AU - Boichard, Didier

AU - Guldbrandtsen, Bernt

AU - Lund, Mogens S

N1 - Copyright © 2016 Author et al.

PY - 2016

Y1 - 2016

N2 - Sequence data is expected to increase the reliability of genomic prediction by containing causative mutations directly, especially in cases where low linkage disequilibrium between markers and causative mutations limits prediction reliability, such as across breed prediction in dairy cattle. In practice, the causative mutations are unknown, and prediction with only variants in perfect linkage disequilibrium with the causative mutations is not realistic, leading to a reduced reliability compared to knowing the causative variants. Our objective was to use sequence data to investigate the potential benefits of sequence data for the prediction of genomic relationships and consequently reliability of genomic breeding values. We used sequence data from five dairy cattle breeds, and a larger number of imputed sequences for two of the five breeds. We focused on the influence of linkage disequilibrium between markers and causative mutations, and assumed that a fraction of the causative mutations was shared across breeds and had the same effect across breeds. By comparing the loss in reliability of different scenarios, varying the distance between markers and causative mutations, using either all genome wide markers from commercial SNP chips, or only the markers closest to the causative mutations, we demonstrate the importance of using only variants very close to the causative mutations, especially for across breed prediction. Rare variants improved prediction only if they were very close to rare causative mutations and all causative mutations were rare. Our results show that sequence data can potentially improve genomic prediction, but careful selection of markers is essential.

AB - Sequence data is expected to increase the reliability of genomic prediction by containing causative mutations directly, especially in cases where low linkage disequilibrium between markers and causative mutations limits prediction reliability, such as across breed prediction in dairy cattle. In practice, the causative mutations are unknown, and prediction with only variants in perfect linkage disequilibrium with the causative mutations is not realistic, leading to a reduced reliability compared to knowing the causative variants. Our objective was to use sequence data to investigate the potential benefits of sequence data for the prediction of genomic relationships and consequently reliability of genomic breeding values. We used sequence data from five dairy cattle breeds, and a larger number of imputed sequences for two of the five breeds. We focused on the influence of linkage disequilibrium between markers and causative mutations, and assumed that a fraction of the causative mutations was shared across breeds and had the same effect across breeds. By comparing the loss in reliability of different scenarios, varying the distance between markers and causative mutations, using either all genome wide markers from commercial SNP chips, or only the markers closest to the causative mutations, we demonstrate the importance of using only variants very close to the causative mutations, especially for across breed prediction. Rare variants improved prediction only if they were very close to rare causative mutations and all causative mutations were rare. Our results show that sequence data can potentially improve genomic prediction, but careful selection of markers is essential.

U2 - 10.1534/g3.116.027730

DO - 10.1534/g3.116.027730

M3 - Journal article

C2 - 27317779

VL - 6

SP - 2553

EP - 2561

JO - G3: Genes, Genomes, Genetics (Bethesda)

JF - G3: Genes, Genomes, Genetics (Bethesda)

SN - 2160-1836

IS - 8

ER -