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Longitudinal Research

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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...
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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...
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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...
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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.
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Constrained multivariate association with longitudinal phenotypes.

Phillip E Melton1, Juan M Peralta2, Laura Almasy3

  • 1The Curtin/UWA Centre for Genetic Origins of Health and Disease, Faculty of Health Sciences, Curtin University and Faculty of Medicine Dentistry & Health Sciences, The University of Western Australia, Perth, Australia.

BMC Proceedings
|December 17, 2016
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Summary
This summary is machine-generated.

This study shows that using a constrained multivariate approach with longitudinal systolic blood pressure data improves genetic association detection. However, it is most effective for variants with moderate to large effects, not gene-centric tests.

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Area of Science:

  • Genetic Epidemiology
  • Quantitative Trait Analysis
  • Longitudinal Data Analysis

Background:

  • Longitudinal data in genetic epidemiology can reveal time-dependent effects on complex diseases.
  • Current research often overlooks longitudinal data, focusing on single time points.
  • This study addresses the need to analyze repeated phenotype measurements in families.

Purpose of the Study:

  • To evaluate a constrained maximum-likelihood measured genotype approach for analyzing longitudinal quantitative traits.
  • To compare the effectiveness of constrained, unconstrained, and averaged phenotype approaches for genetic association.
  • To assess the impact of time on genetic effects for systolic blood pressure (SBP).

Main Methods:

  • Applied a constrained maximum-likelihood measured genotype method to SBP measurements from three time points.
  • Utilized the GAW19 whole-genome sequence family simulated data set with 200 replicates.
  • Compared three statistical approaches: constrained (equal effect across time), unconstrained (separate effects per time point), and averaged SBP.

Main Results:

  • The constrained method using three time points demonstrated increased power to detect genetic associations compared to two time points.
  • Averaging SBP was effective for variants with large effects but less powerful for those with smaller effects.
  • Averaging SBP outperformed both constrained and unconstrained approaches when employing a gene-centric kernel-based test.

Conclusions:

  • The constrained multivariate approach enhances genetic signal compared to bivariate methods.
  • This method is effective for variants explaining a moderate to large proportion of phenotypic variance.
  • The approach showed limitations for gene-centric tests, indicating areas for further methodological development.