Summary association statistics from genome-wide association studies (GWAS) are widely available in large sample sizes across hundreds of complex traits. Analyses of such data can yield important insights, motivating the development of new statistical methods in this area.
Single variant association analysis (including meta-analyses, conditional association and imputation) can be performed effectively using summary association data. These methods often rely on linkage disequilibrium (LD) information from population reference panels.
Summary association data can be used to perform gene-based association tests to identify genes influencing complex traits. In particular, expression quantitative trait loci (eQTLs) can be integrated to identify genes whose expression levels influence complex traits, and rare variant association tests can aggregate evidence of association across multiple rare variants in a gene.
Statistical fine-mapping of causal variant (or variants) at GWAS loci can be performed using summary association data, leveraging information on the strength of association, functional genomic annotations and differences in LD patterns across different populations.
It is becoming increasingly clear that most complex traits and common diseases have a large number of causal variants with small effects. Summary association statistics can be used to understand these polygenic architectures and leverage them for polygenic risk prediction.
Summary association statistics have broad utility in cross-trait analyses, including detecting pleiotropic effects and inferring genetic correlations between traits. Pleiotropic effects can be used in Mendelian randomization analyses to draw inferences about causal relationships among traits.
During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.
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The authors are grateful to H. Finucane, S. Gazal, N. Mancuso and H. Shi for helpful discussions, and to G. Kichaev and R. Johnson for help with figure 3. The work of the authors is funded by US National Institutes of Health grants R01 HG006399, R01 MH101244, R01 GM105857 and R01 MH107649.
The authors declare no competing financial interests.
- Individual-level data
Genome-wide single nucleotide polymorphism genotypes and trait values for each individual included in a genome-wide association study.
- Summary association statistics
Estimated effect sizes and their standard errors for each single nucleotide polymorphism analysed in a genome-wide association study.
Association statistics that follow a standard normal distribution under the null model; often computed as per-allele effect sizes divided by their standard errors.
A method for combining data from different studies in which summary association statistics from each study are jointly analysed.
A method for combining data from different studies in which individual-level data from each study are merged and jointly analysed.
- Summary LD information
(summary linkage disequilibrium information). In-sample correlations between each pair of typed single nucleotide polymorphisms analysed in a genome-wide association study; can be restricted to proximal pairs of typed SNPs to limit the number of pairs of SNPs.
- Transcriptome-wide association studies
(TWAS). Studies that evaluate the association between the expression of each gene and a trait of interest; predicted expression may be used instead of measured expression to improve practicality.
- Mendelian randomization
A method that uses significantly associated single nucleotide polymorphisms as instrumental variables to quantify causal relationships between two traits.
- Burden tests
Gene-based rare variant tests in which all rare variants in a gene are assumed to have the same direction of effect.
- Overdispersion tests
Gene-based rare variant tests in which rare variants in a gene are assumed to impact trait in either direction.
- Posterior probability of causality
The inferred probability that a single nucleotide polymorphism is causal based on association data and optional prior information.
- Polygenic risk scores
A method of predicting trait by summing the predicted marginal effects of all markers below a P value threshold in a training sample multiplied by marker genotypes in a validation sample.
- LD score regression
A method of assessing trait polygenicity by regressing χ2 association statistics against linkage disequilibrium (LD) scores for each single nucleotide polymorphism (SNP), computed as sums of squared correlations of each SNP with all SNPs including itself.
The existence of a genetic variant (or variants) that affects more than one trait.
- Genetic correlation
The signed correlation across single nucleotide polymorphisms between causal effect sizes for two traits.
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Pasaniuc, B., Price, A. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet 18, 117–127 (2017). https://doi.org/10.1038/nrg.2016.142
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