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Bayesian Effect Size Ranking to Prioritise Genetic Risk Variants in Common Diseases for Follow-Up Studies.

Daniel J M Crouch1, Jamie R J Inshaw1, Catherine C Robertson2

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Genetic Epidemiology
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PubMed
Summary

We introduce priorityFDR, a new method combining effect size and statistical significance to prioritize biological variables. This approach improves the identification of key genetic associations for type 1 diabetes research.

Keywords:
GWASeffect sizeempirical Bayesfalse discovery ratesignificance

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Large biological datasets, such as genome-wide association studies (GWAS), often yield numerous statistically significant associations with small effect sizes.
  • The False Discovery Rate (FDR) is a common method for managing false positives by ranking variables based on statistical significance.

Purpose of the Study:

  • To develop a complementary measure, priorityFDR, that ranks biological variables by both effect size and statistical significance.
  • To improve the prioritization of significant findings from large-scale biological datasets for further investigation.

Main Methods:

  • Development of the priorityFDR metric, integrating effect size and statistical significance.
  • Application of priorityFDR to the largest type 1 diabetes GWAS to date (15,573 cases, 158,408 controls).
  • Utilizing Mendelian Randomization to identify putatively causal genes associated with type 1 diabetes risk.

Main Results:

  • Identification of 26 independent genetic associations for type 1 diabetes, including two novel loci.
  • These novel loci exhibited qualitatively lower priorityFDR values compared to other significant signals.
  • Putatively causal type 1 diabetes risk genes and genes in the IL-2 pathway were disproportionately located near low priorityFDR signals.

Conclusions:

  • The priorityFDR metric enhances the prioritization of significant findings from large biological datasets.
  • Combining effect size and significance aids in identifying key variables for mechanistic follow-up studies.
  • This approach is valuable for genetic association studies and other large-scale biological data analyses.