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Towards improved fine-mapping of candidate causal variants

Nature Reviews Genetics volume 26, pages 847–861 (2025)Cite this article

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Abstract

Fine-mapping in genome-wide association studies aims to identify potentially causal genetic variants among a set of candidate variants that are often highly correlated with each other owing to linkage disequilibrium. A variety of statistical approaches are used in fine-mapping, almost all of which are based on a multiple regression framework to model the relationship between genotype and phenotype, while accommodating specific assumptions about the distribution of variant effect sizes and using different inference algorithms. Owing to their modelling flexibility and the ease of making inferential statements, these approaches are predominantly Bayesian in nature. Recently, these approaches have been improved by refining modelling assumptions, integrating additional information, accommodating summary statistics, and developing scalable computational algorithms that improve computation efficiency and fine-mapping resolution.

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Fig. 1: Difference in fine-mapping approaches illustrated in a simple scenario.
Fig. 2: A general workflow for fine-mapping analysis with summary statistics.
Fig. 3: Incorporating additional information enhances the power of fine-mapping.
Fig. 4: Analytic challenges in statistical fine-mapping.

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Acknowledgements

This study was supported by the National Institutes of Health (NIH) Grants R01HG009124 and R01GM144960. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank B. Gao for his help with interpreting the literature.

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Authors and Affiliations

  1. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Zheng Li & Xiang Zhou

  2. Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

    Zheng Li & Xiang Zhou

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  1. Zheng Li
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  2. Xiang Zhou
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The authors contributed equally to all aspects of the article.

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Correspondence to Xiang Zhou.

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Glossary

Bayes factors

Relative marginal likelihood of the observed data under one hypothesis compared with another, often quantifying the evidence in favour of an association versus no association.

Bayesian framework

A statistical framework that represents uncertainty in model parameters using probability distributions; it combines prior beliefs with observed data through Bayes’ theorem to compute posterior distributions.

Bernoulli distribution

A discrete probability distribution for binary data that describes the probability of an event with only two possible outcomes, coded as 1 for success and 0 for failure.

Causal configurations

Specific combinations of causal statuses across SNPs within a genomic locus.

Confounding factors

Variables that influence both the outcome and the explanatory variable, leading to spurious associations between outcome and explanatory variable themselves.

Density value

The value of the probability density function evaluated at a given point, capturing the relative possibility for a continuous random variable being near that point.

Double-exponential distribution

Also known as Laplace distribution, it is a continuous probability distribution that resembles the normal distribution but with a sharper peak at the centre and heavier tails to encourage sparsity.

Expectation-maximization algorithm

An iterative algorithm to find maximum likelihood estimates in models with latent or missing variables by alternating between expectation and maximization steps.

Expression quantitative trait loci

Genomic loci in which genetic variants are associated with gene expression levels.

First-order Taylor approximation

A linear approximation of a function based on its value and first-order derivative at a given point.

Generalized linear regression

A generalization of linear regression that relates the linear combination of explanatory variables to the outcome variable through a link function, allowing for different types of outcome variables (for example, counts, binary).

Global-local shrinkage prior

A type of Bayesian prior that incorporates a global parameter to impose overall shrinkage of SNP effect sizes towards zero, while using local parameters to allow SNP-specific adaptive shrinkage of individual effect sizes.

Kullback–Leibler divergence

A measure of the difference between two probability distributions, often used to assess how one distribution diverges from another distribution.

Linkage disequilibrium

(LD). The nonrandom association of alleles at different loci.

Logistic model

A type of generalized linear regression used for binary outcomes in which the log-odds of a binary outcome is modelled as a linear combination of explanatory variables.

Marginal P values

In genome-wide association studies, a marginal P value refers to the quantification of statistical evidence for an association between a single genetic variant and the phenotype, without accounting for the effects of other variants.

Meta-analysis

A statistical analysis that combines results from multiple studies to reach a single conclusion about a common research question.

Minor allele frequency

(MAF). The frequency of the less common allele at a genetic locus in a population.

Model space

The set of all possible causal configurations within the fine-mapping model.

Multiple linear regression

A regression analysis that models a continuous outcome variable as a linear combination of multiple explanatory variables.

Per-SNP heritability

The proportion of phenotypic variance explained by a single SNP.

Phenotype residuals

The portion of phenotypic variation that remains after accounting for the effects of known covariates in a statistical model; calculated as the difference between observed and model-predicted phenotype values.

Pleiotropy

A genetic phenomenon in which one gene affects multiple traits or diseases.

Poisson distribution

A discrete probability distribution for count data that expresses the probability of a given number of events occurring in a fixed interval of time.

Posterior distribution

The probability distribution of a model parameter given the observed data and prior information.

Posterior probability

The probability of an event given the observed data and prior information.

Probit model

A type of generalized linear regression used for binary outcomes in which the probability of success is modelled using the cumulative distribution function of the standard normal distribution.

Regression

A statistical analysis that estimates the relationship between an outcome variable and one or more explanatory variables.

Regularization

In the context of covariance matrix estimation, regularization refers to the technique of adjusting the sample covariance matrix, typically by adding a multiply of the identity matrix, to ensure invertibility and improve numerical stability.

Statistical power

The probability that a statistical test will correctly detect an effect if it truly exists.

Summary statistics

Effect size estimates and their standard errors from single-variant association analysis in genome-wide association studies, along with an SNP–SNP correlation matrix typically estimated from a reference panel.

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Li, Z., Zhou, X. Towards improved fine-mapping of candidate causal variants. Nat Rev Genet 26, 847–861 (2025). https://doi.org/10.1038/s41576-025-00869-4

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