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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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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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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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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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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Longitudinal SNP-set association analysis of quantitative phenotypes.

Zhong Wang1,2,3, Ke Xu4,5, Xinyu Zhang4,5

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Genetic Epidemiology
|November 19, 2016
PubMed
Summary

We developed the Longitudinal SNP-set/sequence kernel association test (LSKAT) to analyze genetic influences on longitudinal health data. LSKAT improves power for detecting associations with rare variants in repeated measures, outperforming single-point tests.

Keywords:
association testinglinear mixed modellongitudinal studyquantitative traitvariant set

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Area of Science:

  • Genetic Epidemiology
  • Statistical Genetics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies provide robust data for assessing disease progression and genetic influences.
  • Existing genetic association methods often focus on single variants, limiting power for complex traits, especially with rare variants.

Purpose of the Study:

  • To introduce novel statistical methods, Longitudinal SNP-set/sequence kernel association test (LSKAT) and longitudinal trait burden test (LBT), for analyzing genetic associations with longitudinal quantitative phenotypes.
  • To enhance the power of genetic association testing for rare and common variants using repeated measurements.

Main Methods:

  • LSKAT employs a mixed-effects model to account for within-subject correlations in longitudinal data, adjusting for static and time-varying covariates.
  • LBT utilizes linear mixed models to test associations between a trait burden score and longitudinal phenotypes.
  • Both methods leverage repeated measures for increased statistical power and robustness.

Main Results:

  • Simulation studies show LSKAT performs well across various genetic models, while LBT excels when variants are consistently deleterious or protective.
  • LSKAT demonstrates superior power compared to methods using single time points or averaged data.
  • LSKAT is robust to potential misspecification of the data's covariance structure.

Conclusions:

  • LSKAT and LBT are powerful and robust methods for genetic association studies with longitudinal data.
  • The methods successfully identified an association with the circadian gene NR1D2 for longitudinally measured body mass index in the Framingham Heart Study.
  • These approaches advance the analysis of genetic contributions to disease trajectories.