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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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Longitudinal Research02:20

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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Longitudinal Studies01:26

Longitudinal Studies

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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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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Related Experiment Video

Updated: Apr 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Longitudinal data analysis in genome-wide association studies.

Joseph Beyene1, Jemila S Hamid

  • 1Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, ON, Canada.

Genetic Epidemiology
|August 13, 2014
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) are exploring longitudinal phenotypes for disease progression. Researchers analyzed blood pressure data, comparing methods that account for time and family ties against single-time-point analyses.

Keywords:
blood pressuregenome-wide association studieshypertensionlongitudinal datamissing datamixed modelssingle-nucleotide polymorphisms

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Area of Science:

  • Genetics
  • Biostatistics
  • Genomic Epidemiology

Background:

  • Genome-wide association studies (GWAS) have identified numerous genetic variants (single-nucleotide polymorphisms, SNPs) linked to complex diseases using cross-sectional data.
  • There is increasing interest in longitudinal phenotypes to understand trait trajectories and disease progression, but analysis presents challenges.
  • The Genetic Analysis Workshop 18 (GAW18) provided a dataset for studying blood pressure phenotypes over time.

Purpose of the Study:

  • To summarize methods and strategies for genome-wide association studies (GWAS) of longitudinal blood pressure phenotypes.
  • To compare analytical approaches that incorporate temporal correlations and familial relatedness with traditional single-time-point analyses.
  • To evaluate the performance (type I error and power) of different GWAS methods using simulated and real data.

Main Methods:

  • Review and application of statistical methods designed for longitudinal genome-wide association studies (GWAS).
  • Incorporation of correlation across time points and familial relatedness into the genetic analyses.
  • Comparison of longitudinal analysis results with those obtained from single-time-point (baseline) analyses.
  • Utilized simulated data from Genetic Analysis Workshop 18 (GAW18) to assess statistical power and type I error rates.

Main Results:

  • Methods incorporating longitudinal data and familial relatedness offer advantages over single-time-point analyses for detecting genotype-phenotype associations.
  • The performance of different analytical strategies varied, highlighting the importance of choosing appropriate methods for longitudinal GWAS.
  • Simulated data analysis provided insights into the statistical power and accuracy of the evaluated GWAS approaches.

Conclusions:

  • Longitudinal data analysis in GWAS provides a more comprehensive understanding of genetic influences on trait trajectories and disease progression.
  • Accounting for temporal correlations and family structure is crucial for robust genetic association findings in longitudinal studies.
  • Further methodological development is needed to fully leverage the potential of longitudinal data in genetic research.