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Related Experiment Video

Updated: Jul 16, 2026

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

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

Published on: June 21, 2018

Robust genomic control and robust delta centralization tests for case-control association studies.

Yong Zang¹, Hong Zhang, Yaning Yang

¹Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, PR China.

|February 21, 2007

Summary

Population substructure in genetic studies can cause false associations. Genomic control (GC) and delta centralization (DC) methods help correct this. Robust tests using GC and DC maintain accuracy when the genetic model is unknown.

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

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

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Published on: July 27, 2021

Area of Science:

Population genetics
Statistical genetics
Complex disease research

Background:

Population-based case-control studies are vital for identifying complex disease susceptibility markers.
Population substructure, including population stratification (PS) and cryptic relatedness (CR), can lead to spurious associations in these studies.
Genomic control (GC) and delta centralization (DC) are established methods for correcting population substructure.

Purpose of the Study:

To evaluate the performance of robust statistical tests that incorporate GC and DC corrections for population substructure.
To assess these methods when the underlying genetic model of a complex disease is unknown.
To compare the robustness and power of these corrected tests against optimal trend tests.

Main Methods:

Investigated three robust association tests utilizing GC and DC corrections.
Assessed test performance under conditions of unknown genetic models.
Evaluated control of Type I error rates and statistical power in the presence of population stratification and cryptic relatedness.

Main Results:

DC-corrected and GC-corrected maximum and Pearson's association tests demonstrate robustness when the genetic model is unknown.
These robust tests effectively control Type I error rates in the presence of population substructure (PS or CR).
The corrected robust tests exhibit high statistical power compared to optimal trend tests under unknown genetic models.

Conclusions:

Robust association tests incorporating GC or DC corrections are reliable for complex disease studies with unknown genetic models.
These methods provide accurate detection of susceptibility markers by mitigating spurious associations due to population substructure.
The DC- and GC-corrected maximum and Pearson's tests offer a powerful alternative to traditional trend tests when genetic models are not pre-specified.