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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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

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FINEMAP-miss: fine-mapping genome-wide association studies with missing genotype information.

Joonas Kartau¹, Matti Pirinen^1,2,3

¹Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, 00014, Finland.

Bioinformatics (Oxford, England)

|November 9, 2025

Summary

We developed FINEMAP-miss, a novel statistical fine-mapping method to improve genome-wide association studies (GWAS) meta-analyses. FINEMAP-miss accurately identifies causal variants, even with missing data, outperforming existing methods.

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Last Updated: Jan 11, 2026

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

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Published on: June 21, 2018

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) meta-analyses combine multiple studies to increase statistical power.
Statistical fine-mapping aims to identify causal variants within genomic regions.
Existing fine-mapping methods are miscalibrated in meta-analyses due to varying sample sizes and missing data.

Purpose of the Study:

To develop a robust statistical fine-mapping method for GWAS meta-analyses that accounts for variant-specific missingness.
To improve the accuracy and calibration of fine-mapping in complex meta-analysis settings.

Main Methods:

Introduction of FINEMAP-miss, an extension of the FINEMAP model.
Incorporation of variant-specific missingness into the fine-mapping framework.
Validation through simulations and application to a breast cancer GWAS meta-analysis.

Main Results:

FINEMAP-miss demonstrates accurate calibration in meta-analysis simulations where standard methods fail.
FINEMAP-miss outperforms summary statistics imputation, especially with low imputation information or increased meta-analysis complexity.
Successful application of FINEMAP-miss to a breast cancer GWAS meta-analysis where other methods were not applicable.

Conclusions:

FINEMAP-miss provides a well-calibrated and improved approach for statistical fine-mapping in GWAS meta-analyses.
The method is particularly beneficial for complex datasets with missingness and varying sample sizes.
FINEMAP-miss enhances the ability to identify causal variants in large-scale genetic studies.