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Related Experiment Video

Updated: Oct 26, 2025

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
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The distribution of common-variant effect sizes.

Luke J O'Connor1

  • 1Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. loconnor@broadinstitute.org.

Nature Genetics
|July 30, 2021
PubMed
Summary

Fourier Mixture Regression (FMR) accurately estimates genetic effect-size distributions. Large sample sizes are needed to discover disease risk variants, with smaller, more numerous variants found across the genome.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Computational Biology

Background:

  • Estimating the genetic effect-size distribution of diseases is crucial for understanding genetic architecture.
  • Accurate estimation of risk variant number, effect sizes, and required sample sizes has been a significant challenge in genetic studies.

Purpose of the Study:

  • To introduce Fourier Mixture Regression (FMR) as a novel method for estimating genetic effect-size distributions.
  • To validate FMR's accuracy using both simulated and real genetic data.
  • To apply FMR to estimate the scale of genetic studies required for disease heritability discovery.

Main Methods:

  • Fourier Mixture Regression (FMR) was developed and applied to estimate effect-size distributions.
  • The method was validated using simulated datasets and applied to summary statistics from ten human diseases.

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  • Analysis focused on the relationship between sample size, genome-wide significant SNPs, and SNP heritability.

    • Main Results:

    • FMR accurately estimates both simulated and real genetic effect-size distributions.
    • For genome-wide significant SNPs to explain 50% of SNP heritability, 100,000 to 1,000,000 cases are estimated to be required across ten diseases.
    • While polygenicity varies across traits, effect size ranges are similar; smaller, more numerous variants constitute a substantial portion of the genome.

    Conclusions:

    • FMR provides an accurate method for characterizing genetic effect-size distributions.
    • Discovering the full spectrum of genetic risk variants requires substantially larger sample sizes than currently common.
    • Less stringent significance thresholds may be effective in very large studies if confounding is controlled, revealing numerous small-effect variants.