Lessons Learned in Pooling Data for Reference Populations

This study set out to demonstrate the feasibility of merging data from 4 different experimental resource dairy populations (1 herd in each of Scotland and Ireland, and 2 in the Netherlands) to create a pooled reference population for joint genetic and genomic analyses. Data included a total of 60,058 weekly records from 1,630 Holstein-Friesian cows across the 4 herds and included 7 traits: milk, fat and protein yield, milk somatic cell count, live weight, dry matter intake, and energy intake and balance. Missing records were predicted using random regression models, so that at the end there were 44 weekly records, corresponding to the typical 305-day lactation, for each cow. Data were subsequently merged and analysed with mixed linear models. Genetic variance and heritability estimates were greater (P0.05) from zero. When estimable, the genetic correlation between herds for the same trait ranged from 0.85 to 0.99. Results suggested that merging experimental herd data into a single dataset is both feasible and sensible, despite potential differences in management and recording of the animals in the four herds. Merging experimental data will increase the precision of parameter estimates in a genetic analysis and augment the potential reference population in genome-wide association studies especially of difficult-to-record traits.

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Main Authors: Wall, E., Coffey, M., Veerkamp, R.F., Mc Parland, S., Banos, G.
Format: Article in monograph or in proceedings biblioteca
Language:English
Subjects:Life Science,
Online Access:https://research.wur.nl/en/publications/lessons-learned-in-pooling-data-for-reference-populations
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spelling dig-wur-nl-wurpubs-4258542024-08-16 Wall, E. Coffey, M. Veerkamp, R.F. Mc Parland, S. Banos, G. Article in monograph or in proceedings Proceedings of the 2011 Interbull meeting, 26-29 August 2011, Stavanger, Norway Lessons Learned in Pooling Data for Reference Populations 2011 This study set out to demonstrate the feasibility of merging data from 4 different experimental resource dairy populations (1 herd in each of Scotland and Ireland, and 2 in the Netherlands) to create a pooled reference population for joint genetic and genomic analyses. Data included a total of 60,058 weekly records from 1,630 Holstein-Friesian cows across the 4 herds and included 7 traits: milk, fat and protein yield, milk somatic cell count, live weight, dry matter intake, and energy intake and balance. Missing records were predicted using random regression models, so that at the end there were 44 weekly records, corresponding to the typical 305-day lactation, for each cow. Data were subsequently merged and analysed with mixed linear models. Genetic variance and heritability estimates were greater (P0.05) from zero. When estimable, the genetic correlation between herds for the same trait ranged from 0.85 to 0.99. Results suggested that merging experimental herd data into a single dataset is both feasible and sensible, despite potential differences in management and recording of the animals in the four herds. Merging experimental data will increase the precision of parameter estimates in a genetic analysis and augment the potential reference population in genome-wide association studies especially of difficult-to-record traits. en application/pdf https://research.wur.nl/en/publications/lessons-learned-in-pooling-data-for-reference-populations https://edepot.wur.nl/215723 Life Science Wageningen University & Research
institution WUR NL
collection DSpace
country Países bajos
countrycode NL
component Bibliográfico
access En linea
databasecode dig-wur-nl
tag biblioteca
region Europa del Oeste
libraryname WUR Library Netherlands
language English
topic Life Science
Life Science
spellingShingle Life Science
Life Science
Wall, E.
Coffey, M.
Veerkamp, R.F.
Mc Parland, S.
Banos, G.
Lessons Learned in Pooling Data for Reference Populations
description This study set out to demonstrate the feasibility of merging data from 4 different experimental resource dairy populations (1 herd in each of Scotland and Ireland, and 2 in the Netherlands) to create a pooled reference population for joint genetic and genomic analyses. Data included a total of 60,058 weekly records from 1,630 Holstein-Friesian cows across the 4 herds and included 7 traits: milk, fat and protein yield, milk somatic cell count, live weight, dry matter intake, and energy intake and balance. Missing records were predicted using random regression models, so that at the end there were 44 weekly records, corresponding to the typical 305-day lactation, for each cow. Data were subsequently merged and analysed with mixed linear models. Genetic variance and heritability estimates were greater (P0.05) from zero. When estimable, the genetic correlation between herds for the same trait ranged from 0.85 to 0.99. Results suggested that merging experimental herd data into a single dataset is both feasible and sensible, despite potential differences in management and recording of the animals in the four herds. Merging experimental data will increase the precision of parameter estimates in a genetic analysis and augment the potential reference population in genome-wide association studies especially of difficult-to-record traits.
format Article in monograph or in proceedings
topic_facet Life Science
author Wall, E.
Coffey, M.
Veerkamp, R.F.
Mc Parland, S.
Banos, G.
author_facet Wall, E.
Coffey, M.
Veerkamp, R.F.
Mc Parland, S.
Banos, G.
author_sort Wall, E.
title Lessons Learned in Pooling Data for Reference Populations
title_short Lessons Learned in Pooling Data for Reference Populations
title_full Lessons Learned in Pooling Data for Reference Populations
title_fullStr Lessons Learned in Pooling Data for Reference Populations
title_full_unstemmed Lessons Learned in Pooling Data for Reference Populations
title_sort lessons learned in pooling data for reference populations
url https://research.wur.nl/en/publications/lessons-learned-in-pooling-data-for-reference-populations
work_keys_str_mv AT walle lessonslearnedinpoolingdataforreferencepopulations
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AT veerkamprf lessonslearnedinpoolingdataforreferencepopulations
AT mcparlands lessonslearnedinpoolingdataforreferencepopulations
AT banosg lessonslearnedinpoolingdataforreferencepopulations
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