Year: 2012
Type: article
Abstract: Longitudinal genetic studies provide a valuable resource for exploring key genetic and environmental factors that affect complex traits over time. Genetic analysis of longitudinal data that incorporate temporal variations is important for understanding genetic architecture and biological variations of common complex diseases. Although they are important, there is a paucity of statistical methods to analyze longitudinal human genetic data. In this article, longitudinal methods are developed for temporal association mapping to analyze population longitudinal data. Both parametric and nonparametric models are proposed. The models can be applied to multiple diallelic genetic markers such as single-nucleotide polymorphisms and multiallelic markers such as microsatellites. By analytical formulae, we show that the models take both the linkage disequilibrium and temporal trends into account simultaneously. Variance-covariance structure is constructed to model the single measurement variation and multiple measurement correlations of an individual based on the theory of stochastic processes. Novel penalized spline models are used to estimate the time-dependent mean functions and regression coefficients. The methods were applied to analyze Framingham Heart Study data of Genetic Analysis Workshop (GAW) 13 and GAW 16. The temporal trends and genetic effects of the systolic blood pressure are successfully detected by the proposed approaches. Simulation studies were performed to find out that the nonparametric penalized linear model is the best choice in fitting real data. The research sheds light on the important area of longitudinal genetic analysis, and it provides a basis for future methodological investigations and practical applications.
Source: Genetic Epidemiology
DOI: 10.1002/gepi.21673
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Funders: National Science Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Environmental Health Sciences, National Institute on Minority Health and Health Disparities, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G1028935811, G1261793039, G1276754516, Set based tests for genetic association and gene-environment interaction in longitudinal studies, Statistical and computational methods for rare variant association analysis, G1640378483, G1667031398, Statistical Methods for Finding Missing Heritability, G2144938473, G22458031, G2837638605, G290884426, G291913881, G3093995152, G3210485888, G36937976, Statistical Methods for Association Analysis of Complex Traits, Unified Statistical Methods for Sequence-Based Association Studies, G3990748671, G5108002584, G520755853, G5654179670, Institute for Clinical and Translational Research, G66341962, G6742279884, G755179758, G78770644, Michigan Center for Integrative Approaches to Health Disparities (CIAHD), Clinical and Translational Science Award, Efficient Design and Analytic Strategies for Enhancing the Power of Detecting G X, G8852914737, G9000004549, G9000004784, G9000006353, G9000017360, G9000019818, G9000025517, G9000050004, G9000063950, G9000065698, G9000082816, G9000140433, UCLA Clinical and Translational Science Institute, G991334795
OpenAlex ID: W1799199585