title: Longitudinal association analysis of quantitative traits.
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.
authors.affiliation: 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.
authors.forename: Ruzong
authors.initials: R
authors.lastname: Fan
authors.forename: Yiwei
authors.initials: Y
authors.lastname: Zhang
authors.forename: Paul S
authors.initials: PS
authors.lastname: Albert
authors.forename: Aiyi
authors.initials: A
authors.lastname: Liu
authors.forename: Yuanjia
authors.initials: Y
authors.lastname: Wang
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
mesh_terms: D000328:Adult; D000483:Alleles; D001794:Blood Pressure / Q000235:genetics; D005260:Female; D056726:Genetic Association Studies / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics; D005828:Genetics, Population; D006306:Health Surveys; D006321:Heart; D006801:Humans; D016014:Linear Models; D015810:Linkage Disequilibrium / Q000235:genetics; D008137:Longitudinal Studies; D008297:Male; D008404:Massachusetts; D008875:Middle Aged; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D040641:Quantitative Trait Loci / Q000235:genetics*; D018709:Statistics, Nonparametric; D013269:Stochastic Processes; D013997:Time Factors
chemical_list: D005819:Genetic Markers