What genetic group structure to fit?: A Bayesian approach applied to yearling worm egg count data in Merino sheep

Abe Huisman; Daniel Brown

What genetic group structure to fit? A Bayesian approach applied to yearling worm egg count data in Merino sheep

Abe Huisman
, Daniel Brown

Animal Genetics and Breeding Unit

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Genetic groups need to be fitted in a genetic evaluation model to accommodate animals with unknown parents that come from a wide variety of sources. Different genetic grouping strategies were investigated for worm egg count data extracted from Sheep Genetics Australia's Merino database. A Bayesian approach was implemented that tested whether the genetic group variance was significantly different from zero. Four genetic grouping strategies were compared, 1) grouping on average fibre diameter, 2) groups by flock, 3) groups by flock-period, and 4) groups by flock-year. Two additional strategies were grouping strategies 3 and 4 with an assumed autocorrelation structure between genetic groups within flock.

Original language	English
Title of host publication	Proceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics
Editors	AAABG: Association for the Advancement of Animal Breeding, Genetics
Place of Publication	Armidale, Australia
Publisher	Association for the Advancement of Animal Breeding and Genetics (AAABG)
Pages	399-402
Volume	17
ISBN (Print)	1921208139
Publication status	Published - 2007
Event	AAABG 2007: 17th Conference of the Association for the Advancement of Animal Breeding and Genetics - University of New England, Armidale, Australia Duration: 23 Sept 2007 → 26 Sept 2007

Conference

Conference	AAABG 2007: 17th Conference of the Association for the Advancement of Animal Breeding and Genetics
City	Armidale, Australia
Period	23/09/07 → 26/09/07

Keywords

Animal Breeding

Access to Document

https://hdl.handle.net/1959.11/4138

Handle.net

Persistent link

Cite this

Huisman, A., & Brown, D. (2007). What genetic group structure to fit? A Bayesian approach applied to yearling worm egg count data in Merino sheep. In AAABG. Association for the Advancement of Animal Breeding, & Genetics (Eds.), Proceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics (Vol. 17, pp. 399-402). Association for the Advancement of Animal Breeding and Genetics (AAABG). https://hdl.handle.net/1959.11/4138

Huisman, Abe ; Brown, Daniel. / What genetic group structure to fit? A Bayesian approach applied to yearling worm egg count data in Merino sheep. Proceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics. editor / AAABG: Association for the Advancement of Animal Breeding ; Genetics. Vol. 17 Armidale, Australia : Association for the Advancement of Animal Breeding and Genetics (AAABG), 2007. pp. 399-402

@inproceedings{e7bad3294fd941c195c67ca88ac48703,

title = "What genetic group structure to fit?: A Bayesian approach applied to yearling worm egg count data in Merino sheep",

abstract = "Genetic groups need to be fitted in a genetic evaluation model to accommodate animals with unknown parents that come from a wide variety of sources. Different genetic grouping strategies were investigated for worm egg count data extracted from Sheep Genetics Australia's Merino database. A Bayesian approach was implemented that tested whether the genetic group variance was significantly different from zero. Four genetic grouping strategies were compared, 1) grouping on average fibre diameter, 2) groups by flock, 3) groups by flock-period, and 4) groups by flock-year. Two additional strategies were grouping strategies 3 and 4 with an assumed autocorrelation structure between genetic groups within flock.",

keywords = "Animal Breeding",

author = "Abe Huisman and Daniel Brown",

year = "2007",

language = "English",

isbn = "1921208139",

volume = "17",

pages = "399--402",

editor = "\{Association for the Advancement of Animal Breeding\}, AAABG: and Genetics",

booktitle = "Proceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics",

publisher = "Association for the Advancement of Animal Breeding and Genetics (AAABG)",

note = "AAABG 2007: 17th Conference of the Association for the Advancement of Animal Breeding and Genetics ; Conference date: 23-09-2007 Through 26-09-2007",

}

Huisman, A & Brown, D 2007, What genetic group structure to fit? A Bayesian approach applied to yearling worm egg count data in Merino sheep. in AAABG Association for the Advancement of Animal Breeding & Genetics (eds), Proceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics. vol. 17, Association for the Advancement of Animal Breeding and Genetics (AAABG), Armidale, Australia, pp. 399-402, AAABG 2007: 17th Conference of the Association for the Advancement of Animal Breeding and Genetics, Armidale, Australia, 23/09/07. <https://hdl.handle.net/1959.11/4138>

What genetic group structure to fit? A Bayesian approach applied to yearling worm egg count data in Merino sheep. / Huisman, Abe; Brown, Daniel.
Proceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics. ed. / AAABG: Association for the Advancement of Animal Breeding; Genetics. Vol. 17 Armidale, Australia: Association for the Advancement of Animal Breeding and Genetics (AAABG), 2007. p. 399-402.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - What genetic group structure to fit?

T2 - AAABG 2007: 17th Conference of the Association for the Advancement of Animal Breeding and Genetics

AU - Huisman, Abe

AU - Brown, Daniel

PY - 2007

Y1 - 2007

N2 - Genetic groups need to be fitted in a genetic evaluation model to accommodate animals with unknown parents that come from a wide variety of sources. Different genetic grouping strategies were investigated for worm egg count data extracted from Sheep Genetics Australia's Merino database. A Bayesian approach was implemented that tested whether the genetic group variance was significantly different from zero. Four genetic grouping strategies were compared, 1) grouping on average fibre diameter, 2) groups by flock, 3) groups by flock-period, and 4) groups by flock-year. Two additional strategies were grouping strategies 3 and 4 with an assumed autocorrelation structure between genetic groups within flock.

AB - Genetic groups need to be fitted in a genetic evaluation model to accommodate animals with unknown parents that come from a wide variety of sources. Different genetic grouping strategies were investigated for worm egg count data extracted from Sheep Genetics Australia's Merino database. A Bayesian approach was implemented that tested whether the genetic group variance was significantly different from zero. Four genetic grouping strategies were compared, 1) grouping on average fibre diameter, 2) groups by flock, 3) groups by flock-period, and 4) groups by flock-year. Two additional strategies were grouping strategies 3 and 4 with an assumed autocorrelation structure between genetic groups within flock.

KW - Animal Breeding

UR - http://trove.nla.gov.au/work/35062558?selectedversion=NBD42373479

UR - http://www.aaabg.org/livestocklibrary/2007/huisman399.pdf

M3 - Conference contribution

SN - 1921208139

VL - 17

SP - 399

EP - 402

BT - Proceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics

A2 - Association for the Advancement of Animal Breeding, AAABG:

A2 - Genetics, null

PB - Association for the Advancement of Animal Breeding and Genetics (AAABG)

CY - Armidale, Australia

Y2 - 23 September 2007 through 26 September 2007

ER -

Huisman A, Brown D. What genetic group structure to fit? A Bayesian approach applied to yearling worm egg count data in Merino sheep. In Association for the Advancement of Animal Breeding AAABG, Genetics, editors, Proceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics. Vol. 17. Armidale, Australia: Association for the Advancement of Animal Breeding and Genetics (AAABG). 2007. p. 399-402