Implementing within‐cross genomic prediction to reduce oat breeding costs

Standard

Implementing within‐cross genomic prediction to reduce oat breeding costs. / Mellers, Greg; Mackay, Ian; Cowan, Sandy; Griffiths, Irene; Martinez-Martin, Maria; Poland, Jesse A.; Bekele, Wubishet Abebe; Tinker, Nicholas A.; Bentley, Alison; Howarth, Catherine.

In: The Plant Genome, Vol. 13, No. 1, e20004, 30.03.2020.

Research output: Contribution to journalArticlepeer-review

Harvard

Mellers, G, Mackay, I, Cowan, S, Griffiths, I, Martinez-Martin, M, Poland, JA, Bekele, WA, Tinker, NA, Bentley, A & Howarth, C 2020, 'Implementing within‐cross genomic prediction to reduce oat breeding costs', The Plant Genome, vol. 13, no. 1, e20004. https://doi.org/10.1002/tpg2.20004

APA

Mellers, G., Mackay, I., Cowan, S., Griffiths, I., Martinez-Martin, M., Poland, J. A., Bekele, W. A., Tinker, N. A., Bentley, A., & Howarth, C. (2020). Implementing within‐cross genomic prediction to reduce oat breeding costs. The Plant Genome, 13(1), [e20004]. https://doi.org/10.1002/tpg2.20004

Author

Mellers, Greg ; Mackay, Ian ; Cowan, Sandy ; Griffiths, Irene ; Martinez-Martin, Maria ; Poland, Jesse A. ; Bekele, Wubishet Abebe ; Tinker, Nicholas A. ; Bentley, Alison ; Howarth, Catherine. / Implementing within‐cross genomic prediction to reduce oat breeding costs. In: The Plant Genome. 2020 ; Vol. 13, No. 1.

Bibtex - Download

@article{efc7ca0420694c7499556aebc3e767d8,
title = "Implementing within‐cross genomic prediction to reduce oat breeding costs",
abstract = "A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrow‐base biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow‐base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.",
keywords = "oats; genomic prediction; genotyping; breeding",
author = "Greg Mellers and Ian Mackay and Sandy Cowan and Irene Griffiths and Maria Martinez-Martin and Poland, {Jesse A.} and Bekele, {Wubishet Abebe} and Tinker, {Nicholas A.} and Alison Bentley and Catherine Howarth",
note = "ACKNOWLEDGMENTS This paper is dedicated to the memory of our friend and colleague Dr. Greg Mellers who passed away on Friday 6th September 2019, aged 29. This work was supported by Biotechnology and Biological Sciences Research Council (BBSRC) grant BB/M000869/1 to IBERS and NIAB through the InnovOat project (www.innovoat.uk). Alison Bentley is supported by the BBSRC Cross‐Institute Strategic Program “Designing Future Wheat” BB/P016855/1.",
year = "2020",
month = mar,
day = "30",
doi = "10.1002/tpg2.20004",
language = "English",
volume = "13",
journal = "The Plant Genome",
issn = "1940-3372",
publisher = "Crop Science Society of America",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Implementing within‐cross genomic prediction to reduce oat breeding costs

AU - Mellers, Greg

AU - Mackay, Ian

AU - Cowan, Sandy

AU - Griffiths, Irene

AU - Martinez-Martin, Maria

AU - Poland, Jesse A.

AU - Bekele, Wubishet Abebe

AU - Tinker, Nicholas A.

AU - Bentley, Alison

AU - Howarth, Catherine

N1 - ACKNOWLEDGMENTS This paper is dedicated to the memory of our friend and colleague Dr. Greg Mellers who passed away on Friday 6th September 2019, aged 29. This work was supported by Biotechnology and Biological Sciences Research Council (BBSRC) grant BB/M000869/1 to IBERS and NIAB through the InnovOat project (www.innovoat.uk). Alison Bentley is supported by the BBSRC Cross‐Institute Strategic Program “Designing Future Wheat” BB/P016855/1.

PY - 2020/3/30

Y1 - 2020/3/30

N2 - A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrow‐base biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow‐base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.

AB - A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrow‐base biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow‐base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.

KW - oats; genomic prediction; genotyping; breeding

U2 - 10.1002/tpg2.20004

DO - 10.1002/tpg2.20004

M3 - Article

C2 - 33016630

VL - 13

JO - The Plant Genome

JF - The Plant Genome

SN - 1940-3372

IS - 1

M1 - e20004

ER -

Show download statistics
View graph of relations
Citation formats