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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/125125

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/125125

Title:	Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing
Authors:	Wang, Y. 黃佳慧 Huang, C.-H. Fang, Y. Qiong, Y. Li, R.
Contributors:	統計系
Keywords:	Genome-wide association study;Penalized splines;Quantitative trait locus
Date:	2012-01
Issue Date:	2019-08-13 09:19:04 (UTC+8)
Abstract:	In family-based longitudinal genetic studies, investigators collect repeated measurements on a trait that changes with time along with genetic markers. Since repeated measurements are nested within subjects and subjects are nested within families, both the subject-level and measurement-level correlations must be taken into account in the statistical analysis to achieve more accurate estimation. In such studies, the primary interests include to test for quantitative trait locus (QTL) effect, and to estimate age-specific QTL effect and residual polygenic heritability function. We propose flexible semiparametric models along with their statistical estimation and hypothesis testing procedures for longitudinal genetic designs. We employ penalized splines to estimate nonparametric functions in the models. We find that misspecifying the baseline function or the genetic effect function in a parametric analysis may lead to substantially inflated or highly conservative type I error rate on testing and large mean squared error on estimation. We apply the proposed approaches to examine age-specific effects of genetic variants reported in a recent genome-wide association study of blood pressure collected in the Framingham Heart Study.
Relation:	Journal of the Royal Statistical Society, Series C, Vol.61, pp.1-24
Data Type:	article
DOI 連結:	https://doi.org/10.1111/j.1467-9876.2011.01016.x
DOI:	10.1111/j.1467-9876.2011.01016.x
Appears in Collections:	[統計學系] 期刊論文

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