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Basics of Multivariate Analysis in Neuroimaging Data

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Bayesian multivariant fine mapping using the Laplace prior.

Kevin Walters¹, Hannuun Yaacob^1,2

¹School of Mathematics and Statistics, University of Sheffield, Sheffield, UK.

Genetic Epidemiology

|February 5, 2023

Summary

The Laplace prior improves Bayesian fine-mapping by ranking genetic variants more effectively than the Gaussian prior. This method enhances the identification of causal single nucleotide polymorphisms (SNPs) in genetic studies.

Keywords:

Bayesian Laplace prior fine mapping multi-SNP

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Area of Science:

Genetics
Statistical Genetics
Computational Biology

Background:

Bayesian fine-mapping is crucial for identifying causal variants in genetic studies.
The Gaussian prior is the standard for effect size priors in multi-single nucleotide polymorphism (SNP) analyses.
Alternative priors may offer improved performance in fine-mapping studies.

Purpose of the Study:

To introduce and evaluate the Laplace prior for Bayesian multi-SNP fine-mapping.
To compare the ranking performance of the Laplace prior against the Gaussian prior and FINEMAP.
To assess the utility of the Laplace prior in a large-scale genetic dataset.

Main Methods:

Implementation of the Laplace prior in Bayesian multi-SNP fine-mapping.
Comparison of posterior inclusion probability (PIP) rankings between Laplace and Gaussian priors.
Evaluation using simulation scenarios and reanalysis of iCOGS case-control data.

Main Results:

The Laplace prior demonstrated superior ranking performance compared to the Gaussian prior and FINEMAP in simulated scenarios.
The Laplace prior showed better worst-case scenario properties.
Differences in top-ranked SNPs were observed even in a large dataset (iCOGS) when using the Laplace prior versus the Gaussian prior.

Conclusions:

The Laplace prior is a viable and potentially superior alternative to the Gaussian prior for Bayesian fine-mapping.
Its improved ranking performance can aid in more accurate causal variant identification.
The Laplace prior offers advantages, particularly for moderately sized fine-mapping studies.