Dissecting the genetics of complex traits using summary association statistics

Journal name:
Nature Reviews Genetics
Year published:
Published online


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.

At a glance


  1. Illustration of summary association statistics.
    Figure 1: Illustration of summary association statistics.

    Per-allele single nucleotide polymorphism (SNP) effect sizes (and their standard error (s.e.) values) are typically estimated by regressing the phenotype on the genotype values at the SNP of interest (top). At large sample sizes, the vector of z-scores (effect sizes divided by their standard errors) at a locus is approximated by a multivariate normal distribution with mean 0 and variance equal to the linkage disequilibrium (LD) matrix (bottom). MVN, multivariate normal.

  2. TWAS using predicted expression and summary data.
    Figure 2: TWAS using predicted expression and summary data.

    Transcriptome-wide association studies (TWAS) using predicted expression and summary data follow two steps. First, transcriptome reference data are used to build a linear predictor for gene expression, typically using single nucleotide polymorphisms (SNPs) from the 1 Mb local region around the gene with regularized effect sizes (for example, using a Bayesian sparse linear mixed model81). Second, this predictor is applied to summary genome-wide association z-scores, and gene–trait association z-scores are computed, testing the null model of no association between a gene and a trait. eQTL, expression quantitative trait loci; LD, linkage disequilibrium.

  3. Leveraging functional annotation and trans-ethnic data to improve fine-mapping.
    Figure 3: Leveraging functional annotation and trans-ethnic data to improve fine-mapping.

    A sample locus with simulated fine-mapping data in Europeans and Africans is displayed. The top panel shows the 99% credible set (denoted in red) produced by leveraging functional annotation data (DNase I hypersensitivity sites (DHSs)) in trans-ethnic fine-mapping. The middle and bottom panels show the −log10 P values (left) and linkage disequilibrium (right) in Europeans and in Africans.


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  1. Departments of Human Genetics, and Pathology and Laboratory Medicine, University of California, Los Angeles, California 90095, USA.

    • Bogdan Pasaniuc
  2. Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA.

    • Alkes L. Price
  3. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA.

    • Alkes L. Price

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The authors declare no competing interests.

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  • Bogdan Pasaniuc

    Bogdan Pasaniuc is an assistant professor in the Department of Pathology and Laboratory Medicine and the Department of Human Genetics in the David Geffen School of Medicine at the University of California, Los Angeles, USA. He obtained his Ph.D. in computer science and trained as postdoctoral fellow at the International Computer Science Institute, Berkeley, California, USA, followed by a postdoctoral fellowship at the Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA. His research group develops computational and statistical methods to understand the genetic basis of complex traits, focusing on integrative genomics, fine-mapping and heritability analyses.

  • Alkes L. Price

    Alkes L. Price is an associate professor in the Program in Genetic Epidemiology and Statistical Genetics in the Department of Epidemiology at the Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA, with a secondary appointment in the Department of Biostatistics. He is an associate member of the Program in Medical and Population Genetics at the Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. His research focuses on the development of statistical methods for uncovering the genetic basis of human disease, and on the population genetics underlying these methods. Areas of interest include functional components of heritability, common versus rare variant architectures, and disease mapping in structured populations.

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