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Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision.

Qingbo S Wang1,2,3, Ryuya Edahiro1,4, Ho Namkoong5

  • 1Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.

NAR Genomics and Bioinformatics
|November 2, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Knockoff-Finemap combination (KFc), a refined algorithm for statistical fine-mapping in expression quantitative loci (eQTL) studies. KFc improves the precision of identifying causal genetic variants by estimating the probability of their existence and adjusting posterior inclusion probabilities.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Statistical fine-mapping is crucial for identifying causal variants in expression quantitative loci (eQTL) studies.
  • The limited genetic heritability of gene expression poses challenges in eQTL fine-mapping.
  • Accurate prioritization of causal variants is essential for understanding gene regulation.

Purpose of the Study:

  • To introduce Knockoff-Finemap combination (KFc), a novel algorithm designed to refine statistical fine-mapping in eQTL studies.
  • To enhance the precision of identifying causal variants by accounting for the uncertainty of their existence.
  • To improve the calibration and reliability of posterior inclusion probabilities (PIPs).

Main Methods:

  • Development of the Knockoff-Finemap combination (KFc) algorithm.
  • KFc utilizes knockoff genotypes to estimate the probability of causal variant existence within a gene's cis-window.
  • Adjustment of posterior inclusion probabilities (PIPs) based on the estimated probability of causal variants.
  • Application to simulated and real gene expression datasets, including data from the Japan COVID-19 Task Force (JCTF).

Main Results:

  • KFc demonstrated a calibrated posterior inclusion probability (PIP) distribution with improved precision on simulated data.
  • Application to JCTF data showed significant enrichment of functional scores and reporter assay hits in top PIP bins.
  • Integration with external functional priors (GTEx) led to a higher proportion of hematopoietic trait causal variants in top PIP bins.
  • The KFc algorithm offers enhanced precision for a major fine-mapping approach.

Conclusions:

  • The KFc algorithm provides a significant improvement in the precision of statistical fine-mapping for eQTL studies.
  • KFc effectively addresses the challenge of uncertain causal variant existence, leading to more reliable variant prioritization.
  • This refined method enhances the utility of fine-mapping for genetic studies of gene expression and related traits.