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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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...
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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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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Related Experiment Video

Updated: Jun 20, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Trend tests for genetic association using population-based cross-sectional complex survey data.

Dewei She1, Yan Li, Hong Zhang

  • 1Department of Statistics, The George Washington University, Washington, DC 20052, USA.

Biostatistics (Oxford, England)
|September 12, 2009
PubMed
Summary

New statistical tests accurately analyze genetic associations in complex survey data like the Third National Health and Nutrition Examination Survey (NHANES III). These methods improve the reliability of genetic studies for the US population.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

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

Published on: July 27, 2021

Area of Science:

  • Population Genetics
  • Statistical Genetics
  • Survey Methodology

Background:

  • Genetic association studies are crucial for understanding health factors in diverse populations.
  • Standard statistical tests assume simple random samples, which do not apply to complex survey designs like NHANES III.
  • Complex sampling designs can compromise the accuracy (Type I error and power) of traditional genetic association tests.

Purpose of the Study:

  • To develop and evaluate novel statistical tests for genetic trend analysis that accommodate complex survey designs.
  • To provide recommendations for choosing appropriate statistical tests based on genetic model knowledge.
  • To apply these new methods to real-world data for identifying genetic associations with health conditions.

Main Methods:

  • Derivation of trend tests using Wald and quasi-score statistics, with and without genetic model assumptions.
  • Accounting for complex sampling designs, including multistage cluster sampling and sample weighting.
  • Monte Carlo simulation studies to assess the finite-sample properties of the proposed test procedures.

Main Results:

  • The developed statistical tests effectively account for complex survey designs, improving accuracy.
  • Simulation studies demonstrated the performance of the new methods under various conditions.
  • Recommendations are provided for selecting the optimal test statistic depending on prior genetic model information.

Conclusions:

  • The proposed statistical tests offer a robust approach for analyzing genetic association data from complex surveys.
  • Application to NHANES III data successfully tested associations between specific genetic loci (ADRB2, VDR, TGFB1) and health outcomes (obesity, blood lead level, asthma).
  • These methods enhance the ability to discover genotype-phenotype relationships in large, complex population datasets.