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Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics.

Dominic Holland1, Yunpeng Wang2, Wesley K Thompson3

  • 1Multimodal Imaging Laboratory, University of CaliforniaSan Diego, La Jolla, CA, USA; Department of Neurosciences, University of CaliforniaSan Diego, La Jolla, CA, USA.

Frontiers in Genetics
|February 25, 2016
PubMed
Summary

We developed a new method to model millions of genetic association statistics (z-scores) from Genome-wide Association Studies (GWAS). This approach enhances understanding of complex trait genetic architectures and aids in discovering causal SNPs and genes.

Keywords:
GWASGaussian mixture modelSNP discoveryeffect sizeheritabilityputamenschizophrenia

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide Association Studies (GWAS) generate millions of single nucleotide polymorphism (SNP) association statistics (z-scores) for complex traits.
  • The vast number of SNPs and complex genetic architectures pose challenges for interpreting GWAS data and discovering causal variants.
  • Existing methods struggle to fully utilize the rich information within GWAS summary statistics for understanding genetic contributions.

Purpose of the Study:

  • To develop a parsimonious methodology for modeling SNP effect sizes and replication probabilities using only GWAS summary statistics.
  • To enhance the utility of GWAS data for causal SNP and gene discovery, mechanistic pathway elucidation, and future study design.
  • To estimate the degree of polygenicity for complex traits and predict the sample sizes needed to explain chip heritability.

Main Methods:

  • Modeled z-scores from GWAS as a mixture of Gaussian distributions to account for non-null effects.
  • Developed a four-parameter model for estimating polygenicity and predicting heritability explained by significant SNPs.
  • Applied the model to large-scale GWAS of schizophrenia and putamen volume, validating predictions with empirical data.

Main Results:

  • The Gaussian mixture model accurately predicts effect sizes and replication probabilities across various z-scores and sample sizes.
  • Estimated the polygenicity of schizophrenia at 0.037 and putamen volume at 0.001.
  • Determined that sample sizes of 10^6 and 10^5 are required to approach full heritability explanation for schizophrenia and putamen volume, respectively.

Conclusions:

  • The developed methodology provides a robust framework for analyzing GWAS summary statistics and understanding complex trait genetic architectures.
  • The model accurately estimates polygenicity and predicts future study requirements, applicable to a broad range of complex phenotypes.
  • Findings facilitate improved causal SNP discovery, mechanistic insights, and optimized future GWAS designs.