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A scalable framework for identifying allelic series from summary statistics.

Zachary R McCaw1, Jianhui Gao2, Rounak Dey1

  • 1insitro, South San Francisco, CA, USA.

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|October 14, 2025
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Summary

We developed COAST-SS, a new method to identify genes with allelic series using summary statistics from genome-wide association studies. This approach enables therapeutic target discovery without individual-level genetic data.

Keywords:
allelic seriesblood lipidsburden testrare-variant association testingsequence kernel association testsummary statisticsvariant pathogenicitywhole-exome sequencing

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Area of Science:

  • Genetics
  • Pharmacogenomics
  • Statistical Genetics

Background:

  • Genes with dose-response relationships between function and phenotype are valuable therapeutic targets.
  • Identifying such genes, termed allelic series, was previously limited by the need for individual-level genetic data.
  • Genome-wide association studies (GWAS) generate abundant summary statistics, but these were not directly usable for allelic series identification.

Purpose of the Study:

  • To introduce COAST-SS, an extension of the original COAST method, designed to identify allelic series using only summary statistics.
  • To demonstrate the utility and robustness of COAST-SS in identifying genes with allelic series for complex traits.
  • To facilitate the discovery of novel therapeutic targets by leveraging readily available GWAS data.

Main Methods:

  • Developed COAST-SS, a novel statistical method that extends the COAST framework to utilize single-variant summary statistics.
  • Applied COAST-SS to identify allelic series for circulating lipid traits using large-scale real-world datasets (UK Biobank, MVP, TOPMed).
  • Conducted extensive simulations and real data analyses to validate COAST-SS performance and robustness, including its behavior under low linkage disequilibrium (LD).

Main Results:

  • COAST-SS successfully identifies allelic series with p-values comparable to the original COAST method, which requires individual-level data.
  • The method demonstrates robustness to misspecification of the linkage disequilibrium (LD) matrix, particularly in low LD scenarios common for rare variants.
  • Screening a meta-analyzed cohort of up to 840,000 subjects identified candidate allelic series for lipid traits.
  • Various variant pathogenicity annotation strategies showed similar power for detecting candidate allelic series.

Conclusions:

  • COAST-SS provides a powerful and accessible tool for identifying therapeutic targets by detecting allelic series using summary statistics.
  • The method overcomes previous data accessibility limitations, enabling broader application in genetic research and drug discovery.
  • COAST-SS is now integrated into the publicly available AllelicSeries R package, promoting wider adoption and research.