Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing.

Journal of the Royal Statistical Society. Series C, Applied Statistics
Yuanjia WangRunze Li

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.

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Citations

Sep 12, 2012·Genetic Epidemiology·Ruzong FanMomiao Xiong
May 26, 2017·Statistics in Medicine·Jing QianAndrea S Foulkes
Oct 3, 2014·Journal of Hypertension·Maria Teresa BonatiAlberto Zanchetti
Sep 5, 2013·Medicine and Science in Sports and Exercise·Paul T Williams
Nov 20, 2016·Genetic Epidemiology·Zhong WangZuoheng Wang

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