QTL mapping using logistic regression

Yuandan Zhang; Bruce Tier

QTL mapping using logistic regression

Yuandan Zhang, Bruce Tier

Animal Genetics and Breeding Unit

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

Abstract

This study compares logistic regression (LR) with maximum likelihood (ML) for mapping quantitative trait loci (QTL) in halfsib family with respect to genotyping schemes (full and selective), various levels of marker informativeness and marker interval. Under selective genotyping, the power of ML is limited in regions with low information contents. In this case, LR performed better than ML and provides a straightforward and robust solution to such situation.

Original language	English
Title of host publication	Proceedings of the Association for the Advancement of Animal Breeding and Genetics
Editors	AAABG: Association for the Advancement of Animal Breeding, Genetics
Place of Publication	Collingwood, Australia
Pages	354-357
Volume	16
Publication status	Published - 2005
Event	AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics - Noosa Lakes, Australia Duration: 25 Sept 2005 → 28 Sept 2005

Conference

Conference	AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics
City	Noosa Lakes, Australia
Period	25/09/05 → 28/09/05

Keywords

Animal Breeding

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@inproceedings{bae1980c446740ada1364c30de40ad5a,

title = "QTL mapping using logistic regression",

abstract = "This study compares logistic regression (LR) with maximum likelihood (ML) for mapping quantitative trait loci (QTL) in halfsib family with respect to genotyping schemes (full and selective), various levels of marker informativeness and marker interval. Under selective genotyping, the power of ML is limited in regions with low information contents. In this case, LR performed better than ML and provides a straightforward and robust solution to such situation.",

keywords = "Animal Breeding",

author = "Yuandan Zhang and Bruce Tier",

year = "2005",

language = "English",

isbn = "064309234X",

volume = "16",

pages = "354--357",

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

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

note = "AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics ; Conference date: 25-09-2005 Through 28-09-2005",

}

Zhang, Y & Tier, B 2005, QTL mapping using logistic regression. in AAABG Association for the Advancement of Animal Breeding & Genetics (eds), Proceedings of the Association for the Advancement of Animal Breeding and Genetics. vol. 16, Collingwood, Australia, pp. 354-357, AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics, Noosa Lakes, Australia, 25/09/05. <https://hdl.handle.net/1959.11/4502>

QTL mapping using logistic regression. / Zhang, Yuandan; Tier, Bruce.
Proceedings of the Association for the Advancement of Animal Breeding and Genetics. ed. / AAABG: Association for the Advancement of Animal Breeding; Genetics. Vol. 16 Collingwood, Australia, 2005. p. 354-357.

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

TY - GEN

T1 - QTL mapping using logistic regression

AU - Zhang, Yuandan

AU - Tier, Bruce

PY - 2005

Y1 - 2005

N2 - This study compares logistic regression (LR) with maximum likelihood (ML) for mapping quantitative trait loci (QTL) in halfsib family with respect to genotyping schemes (full and selective), various levels of marker informativeness and marker interval. Under selective genotyping, the power of ML is limited in regions with low information contents. In this case, LR performed better than ML and provides a straightforward and robust solution to such situation.

AB - This study compares logistic regression (LR) with maximum likelihood (ML) for mapping quantitative trait loci (QTL) in halfsib family with respect to genotyping schemes (full and selective), various levels of marker informativeness and marker interval. Under selective genotyping, the power of ML is limited in regions with low information contents. In this case, LR performed better than ML and provides a straightforward and robust solution to such situation.

KW - Animal Breeding

UR - http://www.aaabg.org/livestocklibrary/2005/354zhang.pdf

UR - http://trove.nla.gov.au/work/13321110

M3 - Conference contribution

SN - 064309234X

SN - 0643092331

VL - 16

SP - 354

EP - 357

BT - Proceedings of the Association for the Advancement of Animal Breeding and Genetics

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

A2 - Genetics, null

CY - Collingwood, Australia

T2 - AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics

Y2 - 25 September 2005 through 28 September 2005

ER -

QTL mapping using logistic regression

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Conference

Keywords

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