Longitudinal Association Analysis of Quantitative Traits

Authors

  • Ruzong Fan,

    Corresponding author
    • Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Rockville, Maryland
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  • Yiwei Zhang,

    1. Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota
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  • Paul S. Albert,

    1. Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Rockville, Maryland
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  • Aiyi Liu,

    1. Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Rockville, Maryland
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  • Yuanjia Wang,

    1. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York
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  • Momiao Xiong

    1. Human Genetics Center, University of Texas - Houston, Houston, Texas
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Correspondence to: Ruzong Fan, Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics and Prevention Research, 6100 Executive Boulevard, Room 7B05, MSC 7510, Bethesda, MD 20852. E-mail: fanr@mail.nih.gov

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.

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