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From genome-wide associations to candidate causal variants by statistical fine-mapping


Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues.

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This research was supported by the US Public Health Service and National Institutes of Health (contract grant number GM065450).

Reviewer information

Nature Reviews thanks D. Conti and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

All authors contributed to researching content for the article, discussing content and writing. D.J.S. was responsible for reviewing and editing the manuscript before submission.

Competing interests

The authors declare no competing interests.

Correspondence to Daniel J. Schaid.

Supplementary information

Supplementary Box 1


Genome-wide association studies

(GWAS). Scans of genetic markers, typically single-nucleotide polymorphisms (SNPs), across DNA of many subjects to find variants statistically associated with a complex trait.

Complex traits

Either quantitative traits (for example, blood pressure and height) or common diseases (for example, major cancers) that are caused by many genetic and environmental factors working together, each having a relatively small effect and few, if any, being absolutely required for disease to occur.

Tag SNPs

Single-nucleotide polymorphisms (SNPs) that are sufficiently correlated with neighbouring SNPs such that the tag SNP serves as a surrogate for unmeasured SNPs.

Linkage disequilibrium

(LD). Nonrandom association of alleles at different loci on a haplotype in a given population. LD is key to fine-mapping because coinheritance without recombination of alleles from different variants implies that the variants are proximal on the same chromosome.

Causal variants

Genetic variants that mechanistically contribute to diseases or quantitative traits but are not fully penetrant in the sense that the variant may not be a sufficient cause in isolation.


To refine the genomic localization of causal variants by the use of statistical, bioinformatic or functional methods.

Penalized regression

A way to estimate regression coefficients by maximizing the log-likelihood of the data while placing a penalty that constrains the size of the regression coefficients, shrinking small coefficients towards zero, sometimes exactly to zero. Although this causes coefficient estimates to be biased, it improves the overall prediction of the model by decreasing the variance of the coefficient estimates.

Summary statistics

Measures of statistical association between a trait and one or more single-nucleotide polymorphisms (SNPs) that summarize the size of effects of the SNPs on the trait, the variances of the effect sizes and how the effect sizes are correlated among themselves. For case–control studies, summary statistics include the estimated log-odds ratios from logistic regression, the variances of the log-odds ratios and the correlations among the log-odds ratios.


A type of genetic association study that includes subjects from more than one ethnic background.

Multiple testing correction

When testing more than one statistical association, the probability of declaring at least one significant result increases as the number of statistical tests increases. If each of m independent statistical tests uses P value < α to declare significance, then the chance that at least one of the m tests is found to be significant is approximately . Multiple testing correction maintains the overall chance of declaring at least one significant result by using more stringent P value thresholds for each association tested. The Bonferroni correction uses P value < α/m to test each association.

Statistical power

The probability of correctly rejecting a null hypothesis of no statistical association between a single-nucleotide polymorphism (SNP) and a trait when in truth a statistical association exists. Power depends on the magnitude of the SNP effect, the sample size and the P value threshold for deciding statistical significance.


A combination of alleles found on the same chromosome.

Haplotype block

A set of highly associated alleles on a chromosome that tend to be inherited together.

Genotype imputation

A method for estimating (imputing) the unobserved genotypes of study subjects, both for individuals with missing or unreliable genotypes at a genotyped single-nucleotide polymorphism (SNP) and for all individuals at an ungenotyped SNP.

Recombination hot spots

Genomic regions where the rate of recombination is much higher than the neutral expectation.


A technique to build a prediction model by randomly partitioning the sample into a training set to train the model (for example, determining which single-nucleotide polymorphisms (SNPs) to include in a model) and a test set to measure its predictive performance (for example, average squared prediction error). It is common to split the original sample into ten equally sized subsamples, use nine to train and one to test, repeat this process ten times such that each of the ten subsamples is used as a test sample, and then average the predictive performance over the ten training subsamples.

Prior probability

In Bayesian probability theory, the probability distribution assigned to parameters of interest, specified to represent prior knowledge of their values before observing the data.

Posterior probability

In Bayesian probability theory, the updated probability distribution of parameters of interest, conditional on the observed data.

Posterior inclusion probability

(PIP). The marginal probability that a single-nucleotide polymorphism (SNP) is included in any causal model, conditional on the observed data, thereby providing weight of evidence that a SNP should be included as potentially causative.

Expression quantitative trait loci

(eQTLs). Genomic regions that harbour one or more nucleotide variants that influence the amount of expression of a gene.

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Fig. 1: Flow of a typical process from initial GWAS to annotation of SNPs selected from fine-mapping analyses.
Fig. 2: Hypothetical examples of fine-mapping strategies.
Fig. 3: Power of conditional analysis.
Fig. 4: Posterior probability for a single causal SNP when 5–40 SNPs are in a region of interest.