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Updated: Jun 26, 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

A systematic search for SNPs/haplotypes associated with disease phenotypes using a haplotype-based stepwise

Yin Yang¹, Shuying Sue Li, Jason W Chien

¹Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave, N, Seattle, WA 98109, USA. yyang2@u.washington.edu

|December 24, 2008

Summary

The Haplotype-Based Stepwise Procedure (HBSP) effectively removes extra Single Nucleotide Polymorphisms (SNPs) in genetic association studies. This method enhances analytical power and simplifies interpretation for more cost-effective research.

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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

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

Published on: July 27, 2021

Area of Science:

Genetics
Bioinformatics

Background:

Genotyping technologies allow for the analysis of multiple Single Nucleotide Polymorphisms (SNPs) for haplotype association studies.
Including unnecessary SNPs can decrease the power of haplotype-based association analysis.

Purpose of the Study:

To propose and evaluate a Haplotype-Based Stepwise Procedure (HBSP) for eliminating extraneous SNPs.
To improve the power and efficiency of haplotype-based association analysis.

Main Methods:

Developed a Haplotype-Based Stepwise Procedure (HBSP) to identify and remove non-informative SNPs.
Applied HBSP to simulated and real genetic data from a study on the bactericidal/permeability-increasing (BPI) gene and pulmonary function.

Main Results:

HBSP maintained desired false positive error rates under the null hypothesis.
HBSP demonstrated adequate power to detect genetic associations in studies with varying subject numbers (500-2000).
Application of HBSP identified two specific SNPs with positive validation in the BPI gene study.

Conclusions:

HBSP preserves the core of haplotype-based association analysis while increasing power by excluding redundant SNPs.
Reducing the number of SNPs through HBSP leads to simpler interpretation and more cost-effective genetic studies.