Application of Mapping Quantitative Trait Loci on Accelerated Failure Time Models.
碩士 === 國立嘉義大學 === 農學研究所 === 96 === ABSTRACT Most of approaches for mapping quantitative trait loci (QTL) are well developed for normally distributed and completely observed phenotypes. These approaches may not be applicable when the phenotype belongs to the failure time, which has a skewed distribut...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Online Access: | http://ndltd.ncl.edu.tw/handle/14031361166269515135 |
id |
ndltd-TW-096NCYU5416003 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-096NCYU54160032016-05-18T04:13:14Z http://ndltd.ncl.edu.tw/handle/14031361166269515135 Application of Mapping Quantitative Trait Loci on Accelerated Failure Time Models. 數量性狀基因座定位法在加速失敗時間模式下之應用 Tzu-Wei Chiu 邱慈緯 碩士 國立嘉義大學 農學研究所 96 ABSTRACT Most of approaches for mapping quantitative trait loci (QTL) are well developed for normally distributed and completely observed phenotypes. These approaches may not be applicable when the phenotype belongs to the failure time, which has a skewed distribution and is often loss to follow-up. This paper proposes the accelerated failure time model to describe the effect of QTL based on censored failure-time phenotypes.Applying the EM algorithm (expectation maximization algorithm) of Diao et al. (2004) to estimate regression parameters and search the entire chromosome for QTL, some simulation studies are presented to evaluate the performance of the proposed methods. Results from the simulated data indicate that the estimator of the QTL location and the estimators of the QTL effects have little bias, and that the proposed method may be more efficient in estimating regression parameters and detecting QTL as the numbers of markers increase. Shinn-Jia Tzeng 曾信嘉 學位論文 ; thesis 65 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立嘉義大學 === 農學研究所 === 96 === ABSTRACT
Most of approaches for mapping quantitative trait loci (QTL) are well developed for normally distributed and completely observed phenotypes. These approaches may not be applicable when the phenotype belongs to the failure time, which has a skewed distribution and is often loss to follow-up. This paper proposes the accelerated failure time model
to describe the effect of QTL based on censored failure-time phenotypes.Applying the EM algorithm (expectation maximization algorithm) of Diao et al. (2004) to estimate regression parameters and search the entire chromosome for QTL, some simulation studies are presented to evaluate the performance of the proposed methods. Results from the simulated data indicate that the estimator of the QTL location and the estimators of the QTL effects have little bias, and that the proposed method may be more efficient in estimating regression parameters and detecting QTL as the numbers of markers increase.
|
author2 |
Shinn-Jia Tzeng |
author_facet |
Shinn-Jia Tzeng Tzu-Wei Chiu 邱慈緯 |
author |
Tzu-Wei Chiu 邱慈緯 |
spellingShingle |
Tzu-Wei Chiu 邱慈緯 Application of Mapping Quantitative Trait Loci on Accelerated Failure Time Models. |
author_sort |
Tzu-Wei Chiu |
title |
Application of Mapping Quantitative Trait Loci on Accelerated Failure Time Models. |
title_short |
Application of Mapping Quantitative Trait Loci on Accelerated Failure Time Models. |
title_full |
Application of Mapping Quantitative Trait Loci on Accelerated Failure Time Models. |
title_fullStr |
Application of Mapping Quantitative Trait Loci on Accelerated Failure Time Models. |
title_full_unstemmed |
Application of Mapping Quantitative Trait Loci on Accelerated Failure Time Models. |
title_sort |
application of mapping quantitative trait loci on accelerated failure time models. |
url |
http://ndltd.ncl.edu.tw/handle/14031361166269515135 |
work_keys_str_mv |
AT tzuweichiu applicationofmappingquantitativetraitlocionacceleratedfailuretimemodels AT qiūcíwěi applicationofmappingquantitativetraitlocionacceleratedfailuretimemodels AT tzuweichiu shùliàngxìngzhuàngjīyīnzuòdìngwèifǎzàijiāsùshībàishíjiānmóshìxiàzhīyīngyòng AT qiūcíwěi shùliàngxìngzhuàngjīyīnzuòdìngwèifǎzàijiāsùshībàishíjiānmóshìxiàzhīyīngyòng |
_version_ |
1718271029187444736 |