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Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

Longitudinal Studies

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

Polygenic Traits

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

Polygenic Traits

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: May 18, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Longitudinal association analysis of quantitative traits.

Ruzong Fan¹, Yiwei Zhang, Paul S Albert

¹Biostatistics 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.

Genetic Epidemiology

|September 12, 2012

Summary

New statistical methods analyze longitudinal genetic data, revealing temporal trends and genetic effects on complex traits like blood pressure. The nonparametric penalized linear model proved most effective for real-world data analysis.

Keywords:

association mapping longitudinal analysis quantitative trait loci

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Last Updated: May 18, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

Genetics
Biostatistics
Population Health

Background:

Longitudinal genetic studies are crucial for understanding complex traits influenced by genetic and environmental factors over time.
Analyzing temporal variations in genetic data is key to deciphering disease architecture but lacks sufficient statistical methods.
Existing methods often fail to adequately incorporate temporal dynamics in human genetic data analysis.

Purpose of the Study:

To develop novel statistical methods for temporal association mapping in population longitudinal genetic data.
To propose both parametric and nonparametric models capable of analyzing various genetic markers.
To address the paucity of analytical tools for longitudinal human genetic studies.

Main Methods:

Development of parametric and nonparametric models for temporal association mapping.
Incorporation of linkage disequilibrium and temporal trends using analytical formulae.
Application of penalized spline models and stochastic processes for time-dependent function estimation.
Utilized variance-covariance structures to model individual measurement correlations.

Main Results:

Successfully detected temporal trends and genetic effects on systolic blood pressure using Framingham Heart Study data (GAW 13 and GAW 16).
Demonstrated the models' ability to handle both diallelic and multiallelic genetic markers.
Simulation studies indicated the nonparametric penalized linear model as optimal for real data fitting.

Conclusions:

The developed longitudinal methods provide a robust framework for analyzing population genetic data over time.
These novel approaches enhance the understanding of genetic architecture and biological variations in complex diseases.
The research offers a foundation for future methodological advancements and practical applications in longitudinal genetic analysis.