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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...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

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Related Experiment Video

Updated: May 23, 2026

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

Correcting for Population Stratification in Genomewide Association Studies.

D Y Lin1, D Zeng

  • 1Department of Biostatistics, CB#7420, University of North Carolina, Chapel Hill, NC 27599-7420.

Journal of the American Statistical Association
|April 3, 2012
PubMed
Summary
This summary is machine-generated.

Genomewide association studies (GWAS) can yield spurious results due to population stratification. This study introduces a statistically sound method to correct for unmeasured confounders, improving genetic association studies for complex diseases.

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Last Updated: May 23, 2026

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

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Published on: July 27, 2021

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08:03

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Published on: December 7, 2021

Area of Science:

  • Genetics
  • Biostatistics
  • Population Genetics

Background:

  • Genomewide association studies (GWAS) are crucial for identifying the genetic underpinnings of complex human diseases.
  • Population stratification, arising from allele frequency and disease risk heterogeneity across ancestral subpopulations, can introduce spurious associations in GWAS.
  • Unmeasured confounding due to population stratification poses a significant challenge in genetic association studies.

Purpose of the Study:

  • To provide a statistically rigorous and computationally feasible solution for addressing unmeasured confounding in GWAS.
  • To develop a method for accurately estimating the odds ratio of disease associated with genetic variants, accounting for population structure.

Main Methods:

  • The study establishes identifiability conditions for the odds ratio: genotype must be independent of unknown population substructure, conditional on observed ancestry-informative markers in disease-free individuals.
  • A semiparametric logistic regression model is employed, incorporating an arbitrary function of a propensity score that links genotype probability to ancestry-informative markers.
  • The unknown function of the propensity score is approximated using B-splines, leading to a consistent and asymptotically normal estimator for the odds ratio, along with a consistent variance estimator.

Main Results:

  • The proposed statistical method provides a valid approach to estimate odds ratios in the presence of population stratification.
  • Simulation studies confirm the effectiveness and reliability of the developed inference procedures under realistic conditions.
  • The methodology was successfully applied to the Wellcome Trust Case-Control Study, demonstrating its practical utility.

Conclusions:

  • The developed method offers a robust solution to the problem of unmeasured confounding in genomewide association studies.
  • Accurate estimation of genetic effects on disease risk is achievable by accounting for population substructure using ancestry-informative markers and propensity scores.
  • This approach enhances the reliability of genetic discoveries for complex human diseases.