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Effect of marker-data editing on the accuracy of genomic prediction

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Standard

Effect of marker-data editing on the accuracy of genomic prediction. / Edriss, Vahid; Guldbrandtsen, Bernt; Lund, Mogens Sandø; Su, Guosheng.

I: Journal of Animal Breeding and Genetics, Bind 130, Nr. 2, 04.2013, s. 128-135.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Edriss, V, Guldbrandtsen, B, Lund, MS & Su, G 2013, 'Effect of marker-data editing on the accuracy of genomic prediction', Journal of Animal Breeding and Genetics, bind 130, nr. 2, s. 128-135. https://doi.org/10.1111/j.1439-0388.2012.01015.x

APA

Edriss, V., Guldbrandtsen, B., Lund, M. S., & Su, G. (2013). Effect of marker-data editing on the accuracy of genomic prediction. Journal of Animal Breeding and Genetics, 130(2), 128-135. https://doi.org/10.1111/j.1439-0388.2012.01015.x

CBE

Edriss V, Guldbrandtsen B, Lund MS, Su G. 2013. Effect of marker-data editing on the accuracy of genomic prediction. Journal of Animal Breeding and Genetics. 130(2):128-135. https://doi.org/10.1111/j.1439-0388.2012.01015.x

MLA

Edriss, Vahid o.a.. "Effect of marker-data editing on the accuracy of genomic prediction". Journal of Animal Breeding and Genetics. 2013, 130(2). 128-135. https://doi.org/10.1111/j.1439-0388.2012.01015.x

Vancouver

Edriss V, Guldbrandtsen B, Lund MS, Su G. Effect of marker-data editing on the accuracy of genomic prediction. Journal of Animal Breeding and Genetics. 2013 apr;130(2):128-135. https://doi.org/10.1111/j.1439-0388.2012.01015.x

Author

Edriss, Vahid ; Guldbrandtsen, Bernt ; Lund, Mogens Sandø ; Su, Guosheng. / Effect of marker-data editing on the accuracy of genomic prediction. I: Journal of Animal Breeding and Genetics. 2013 ; Bind 130, Nr. 2. s. 128-135.

Bibtex

@article{c6922d8a8c8849eaaf7759fcb3693d5f,
title = "Effect of marker-data editing on the accuracy of genomic prediction",
abstract = "Genomic selection is a method to predict breeding values using genome-wide single-nucleotide polymorphism (SNP) markers. High-quality marker data are necessary for genomic selection. The aim of this study was to investigate the effect of marker-editing criteria on the accuracy of genomic predictions in the Nordic Holstein and Jersey populations. Data included 4429 Holstein and 1071 Jersey bulls. In total, 48 222 SNP for Holstein and 44 305 SNP for Jersey were polymorphic. The SNP data were edited based on (i) minor allele frequencies (MAF) with thresholds of no limit, 0.001, 0.01, 0.02, 0.05 and 0.10, (ii) deviations from Hardy–Weinberg proportions (HWP) with thresholds of no limit, chi-squared p-values of 0.001, 0.02, 0.05 and 0.10, and (iii) GenCall (GC) scores with thresholds of 0.15, 0.55, 0.60, 0.65 and 0.70. The marker data sets edited with different criteria were used for genomic prediction of protein yield, fertility and mastitis using a Bayesian variable selection and a GBLUP model. De-regressed EBV were used as response variables. The result showed little difference between prediction accuracies based on marker data sets edited with MAF and deviation from HWP. However, accuracy decreased with more stringent thresholds of GC score. According to the results of this study, it would be appropriate to edit data with restriction of MAF being between 0.01 and 0.02, a p-value of deviation from HWP being 0.05, and keeping all individual SNP genotypes having a GC score over 0.15.",
keywords = "Hardy-Weinberg proportion, GenCall score, genomic prediction, minor allele frequency, single nucleotide polymorphism",
author = "Vahid Edriss and Bernt Guldbrandtsen and Lund, {Mogens Sand{\o}} and Guosheng Su",
year = "2013",
month = apr,
doi = "10.1111/j.1439-0388.2012.01015.x",
language = "English",
volume = "130",
pages = "128--135",
journal = "Journal of Animal Breeding and Genetics",
issn = "0931-2668",
publisher = "Wiley-Blackwell Verlag GmbH",
number = "2",

}

RIS

TY - JOUR

T1 - Effect of marker-data editing on the accuracy of genomic prediction

AU - Edriss, Vahid

AU - Guldbrandtsen, Bernt

AU - Lund, Mogens Sandø

AU - Su, Guosheng

PY - 2013/4

Y1 - 2013/4

N2 - Genomic selection is a method to predict breeding values using genome-wide single-nucleotide polymorphism (SNP) markers. High-quality marker data are necessary for genomic selection. The aim of this study was to investigate the effect of marker-editing criteria on the accuracy of genomic predictions in the Nordic Holstein and Jersey populations. Data included 4429 Holstein and 1071 Jersey bulls. In total, 48 222 SNP for Holstein and 44 305 SNP for Jersey were polymorphic. The SNP data were edited based on (i) minor allele frequencies (MAF) with thresholds of no limit, 0.001, 0.01, 0.02, 0.05 and 0.10, (ii) deviations from Hardy–Weinberg proportions (HWP) with thresholds of no limit, chi-squared p-values of 0.001, 0.02, 0.05 and 0.10, and (iii) GenCall (GC) scores with thresholds of 0.15, 0.55, 0.60, 0.65 and 0.70. The marker data sets edited with different criteria were used for genomic prediction of protein yield, fertility and mastitis using a Bayesian variable selection and a GBLUP model. De-regressed EBV were used as response variables. The result showed little difference between prediction accuracies based on marker data sets edited with MAF and deviation from HWP. However, accuracy decreased with more stringent thresholds of GC score. According to the results of this study, it would be appropriate to edit data with restriction of MAF being between 0.01 and 0.02, a p-value of deviation from HWP being 0.05, and keeping all individual SNP genotypes having a GC score over 0.15.

AB - Genomic selection is a method to predict breeding values using genome-wide single-nucleotide polymorphism (SNP) markers. High-quality marker data are necessary for genomic selection. The aim of this study was to investigate the effect of marker-editing criteria on the accuracy of genomic predictions in the Nordic Holstein and Jersey populations. Data included 4429 Holstein and 1071 Jersey bulls. In total, 48 222 SNP for Holstein and 44 305 SNP for Jersey were polymorphic. The SNP data were edited based on (i) minor allele frequencies (MAF) with thresholds of no limit, 0.001, 0.01, 0.02, 0.05 and 0.10, (ii) deviations from Hardy–Weinberg proportions (HWP) with thresholds of no limit, chi-squared p-values of 0.001, 0.02, 0.05 and 0.10, and (iii) GenCall (GC) scores with thresholds of 0.15, 0.55, 0.60, 0.65 and 0.70. The marker data sets edited with different criteria were used for genomic prediction of protein yield, fertility and mastitis using a Bayesian variable selection and a GBLUP model. De-regressed EBV were used as response variables. The result showed little difference between prediction accuracies based on marker data sets edited with MAF and deviation from HWP. However, accuracy decreased with more stringent thresholds of GC score. According to the results of this study, it would be appropriate to edit data with restriction of MAF being between 0.01 and 0.02, a p-value of deviation from HWP being 0.05, and keeping all individual SNP genotypes having a GC score over 0.15.

KW - Hardy-Weinberg proportion

KW - GenCall score

KW - genomic prediction

KW - minor allele frequency

KW - single nucleotide polymorphism

U2 - 10.1111/j.1439-0388.2012.01015.x

DO - 10.1111/j.1439-0388.2012.01015.x

M3 - Journal article

C2 - 23496013

VL - 130

SP - 128

EP - 135

JO - Journal of Animal Breeding and Genetics

JF - Journal of Animal Breeding and Genetics

SN - 0931-2668

IS - 2

ER -