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Updated: Jun 21, 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

Multiple testing in genome-wide association studies via hidden Markov models.

Zhi Wei¹, Wenguang Sun, Kai Wang

¹Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. zhiwei@njit.edu

Bioinformatics (Oxford, England)

|August 6, 2009

Summary

This study introduces a new method, Pooled Local Index of Significance (PLIS), to improve genome-wide association studies (GWAS) by considering genetic marker dependencies. PLIS enhances the detection of disease-associated single nucleotide polymorphisms (SNPs) with greater accuracy and reproducibility.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

Genetics
Statistical genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) aim to identify genetic variants associated with diseases.
Conventional P-value based methods in GWAS often lack efficiency due to ignoring dependencies between adjacent single nucleotide polymorphisms (SNPs).

Purpose of the Study:

To develop a novel statistical framework that leverages dependency information among adjacent SNPs to enhance screening efficiency in GWAS.
To improve the power and reproducibility of identifying disease-associated SNPs.

Main Methods:

Modeling linear block dependency in SNP data using hidden Markov models (HMMs).
Developing a compound decision-theoretic framework for testing HMM-dependent hypotheses.
Proposing the data-driven Pooled Local Index of Significance (PLIS) procedure to control the false discovery rate (FDR).

Main Results:

PLIS is shown to be optimal with the smallest false negative rate (FNR) among valid FDR procedures.
PLIS demonstrates higher statistical power and better reproducibility compared to conventional P-value based methods like Benjamini-Hochberg (BH).
Application to type 1 diabetes GWAS data yielded more accurate results and improved reproducibility.

Conclusions:

The PLIS procedure generates substantially different genomic rankings compared to P-value based methods.
Integrating information from adjacent SNP locations increases the signal-to-noise ratio, leading to higher statistical power and reproducibility.
PLIS offers a promising direction for improving large-scale GWAS analysis.