A note on QTL detecting for censored traits

Yixin Fang

doi:10.1051/gse:2005037

A note on QTL detecting for censored traits

Yixin Fang

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Most existing statistical methods for mapping quantitative trait loci (QTL) assume that the phenotype follows a normal distribution and that it is fully observed. However, some phenotypes have skewed distributions and may be censored. This note proposes a simple and efficient approach to QTL detecting for censored traits with the Cox PH model without estimating the baseline hazard function which is "nuisance".

Original language	English (US)
Pages (from-to)	221-229
Number of pages	9
Journal	Genetics Selection Evolution
Volume	38
Issue number	2
DOIs	https://doi.org/10.1051/gse:2005037
State	Published - Mar 2006
Externally published	Yes

All Science Journal Classification (ASJC) codes

Ecology, Evolution, Behavior and Systematics
Animal Science and Zoology
Genetics

Keywords

Censored trait
Interval mapping
QTL detecting

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10.1051/gse:2005037

Cite this

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AU - Fang, Yixin

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AB - Most existing statistical methods for mapping quantitative trait loci (QTL) assume that the phenotype follows a normal distribution and that it is fully observed. However, some phenotypes have skewed distributions and may be censored. This note proposes a simple and efficient approach to QTL detecting for censored traits with the Cox PH model without estimating the baseline hazard function which is "nuisance".

KW - Censored trait

KW - Interval mapping

KW - QTL detecting

UR - https://www.scopus.com/pages/publications/33645781093

UR - https://www.scopus.com/pages/publications/33645781093#tab=citedBy

U2 - 10.1051/gse:2005037

DO - 10.1051/gse:2005037

M3 - Article

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AN - SCOPUS:33645781093

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VL - 38

SP - 221

EP - 229

JO - Genetics Selection Evolution

JF - Genetics Selection Evolution

IS - 2

ER -

A note on QTL detecting for censored traits

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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