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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

Basic statistical analysis in genetic case-control studies.

Geraldine M Clarke¹, Carl A Anderson, Fredrik H Pettersson

¹Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. gclarke@well.ox.ac.uk

Nature Protocols

|February 5, 2011

Summary

This summary is machine-generated.

This protocol guides basic statistical analysis for genetic association case-control studies. It covers selecting tests, visualizing results, and controlling for multiple testing using popular bioinformatics tools.

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Area of Science:

Genetics
Biostatistics
Population Genetics

Background:

Population-based genetic association studies are crucial for understanding disease etiology.
Statistical analysis is fundamental for interpreting genetic data and identifying disease-related markers.
Previous protocols have covered study design, marker selection, and data quality control.

Purpose of the Study:

To provide a protocol for performing basic statistical analysis in genetic association case-control studies.
To guide researchers, even those with no prior software experience, through essential analytical steps.
To facilitate the interpretation and validation of genetic association findings.

Main Methods:

Selection of appropriate measures of association and disease models.
Application of suitable statistical tests for association analysis.
Utilizing bioinformatics tools (PLINK, R, Haploview) for single-nucleotide polymorphism data analysis.
Methods for controlling multiple testing and implementing replication strategies.
Visualization and interpretation of statistical results.

Main Results:

The protocol outlines a clear workflow for statistical analysis in genetic association studies.
It enables the use of popular software for handling and analyzing genetic data.
Provides guidance on interpreting results and ensuring their validity through multiple testing control and replication.

Conclusions:

This protocol offers a practical guide for conducting basic statistical analyses in genetic association studies.
It empowers researchers to effectively analyze genetic data and interpret findings using common bioinformatics tools.
Adherence to the described methods enhances the reliability and reproducibility of genetic association study results.