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Related Concept Videos

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.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Published on: December 7, 2021

Allowing for population stratification in association analysis.

Huaizhen Qin1, Xiaofeng Zhu

  • 1Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH, USA. hxq21@case.edu

Methods in Molecular Biology (Clifton, N.J.)
|February 7, 2012
PubMed
Summary
This summary is machine-generated.

Adjusting for local ancestry, not just global, improves genetic association studies. Using local principal components (PCs) effectively corrects for local population structure, preventing false discoveries and aiding disease gene localization.

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Area of Science:

  • Genetics
  • Population Genetics
  • Statistical Genetics

Background:

  • Genetic association studies require correction for population structure to prevent bias.
  • Traditional methods often adjust only for global ancestry, which may be insufficient.
  • Local population structure, varying across genomic regions, can be a significant confounding factor.

Purpose of the Study:

  • To address the inadequacy of global ancestry adjustments in genetic studies.
  • To propose a method for correcting local population structure when ancestral information is unavailable.
  • To enhance the accuracy of disease gene localization by accounting for local ancestries.

Main Methods:

  • Proposed employing local principal components (PCs) to represent unknown local ancestries.
  • Developed an algorithm to adjust for local PCs during genotype-phenotype association testing.
  • Applied the algorithm to European American data with known spurious associations.

Main Results:

  • The proposed method successfully eliminated a known spurious association between LCT gene SNPs and height.
  • The algorithm effectively corrects for local population structure without requiring explicit ancestral population information.
  • The computational burden of the proposed method is acceptable.

Conclusions:

  • Adjusting for local ancestry is crucial for accurate genetic association studies.
  • Local PCs provide a viable proxy for local ancestries in the absence of direct inference.
  • The developed method offers a robust approach to mitigate false-positives arising from local population structure.