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bioRxivGeneticsPreprint

Bayesian Multi-SNP Genetic Association Analysis: Control of FDR and Use of Summary Statistics

Yeji LeeiD, Francesca LucaiD, Roger Piqué-RegiiD, Xiaoquan WeniD

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Abstract summary

Claude Opus 5.5 · xhigh effort · 18 Oct 2026 · from the abstract · CC BY-NC-ND 4.0

DAP-G adds false discovery rate control and credible sets to Bayesian multi-SNP association analysis, runs about 20 times faster than existing software, and defines summary statistics matching individual-level data.

Multi-SNP analysis of GWAS and molecular QTL data quantifies association signals, but has lacked a formal procedure that controls type I errors across the signals it reports. The authors supply both an error-rate framework built on Bayesian posterior probabilities and a summary-statistic formulation meant to lose no power relative to individual-level genotypes.

Key results

  1. SuggestedAssociation signals can be arranged in a hierarchy of Bayesian posterior probabilities, which in turn supports false discovery rate control and Bayesian credible sets for multi-SNP analysis.

    Presented as a new representation with rigorous error control; the abstract reports no empirical false discovery rate, coverage or calibration numbers, and the demonstration rests on constructed simulations. · Abstract

  2. ClaimedRunning multi-SNP analysis from single-SNP z-statistics alone likely gives conservative results, whereas a specific set of sufficient summary statistics reproduces individual-level results exactly.

    Stated as the conclusion of their examination of data-reduction strategies, with the summary-statistic version said to be identical to individual-level analysis and to sacrifice no power; no concordance figures are reported. · Abstract

  3. SuggestedThe DAP-G implementation is markedly faster than the existing Bayesian multi-SNP software the authors compare against.

    Roughly 20-fold speedup over the comparison implementation; the abstract names neither the competing software, the hardware, nor the dataset used for timing. · Abstract

Methods, data and reproducibility

The authors formulate multi-SNP genetic association as a hierarchy of Bayesian posterior probabilities, derive sufficient summary statistics for that inference, and implement both in DAP-G; evaluation uses simulation studies plus an applied cis expression QTL workflow.

  • Constructed simulation studies used to evaluate the proposed procedures
  • Whole blood data from the GTEx project, analysed as a complete cis expression quantitative trait locus workflow · human
Methods & tools
DAP-G, GTEx
Reproducibility gaps
No accession or download link is given for the GTEx whole blood data used in the illustration, and no software versions or timing hardware are stated.
Limitations (2)
  • The error-rate and power claims rest on simulations designed by the authors plus one applied example, so behaviour under real linkage disequilibrium patterns beyond that example is untested here. · noted in this summary
  • The speed comparison names no competing implementation, dataset or hardware, so the roughly 20-fold figure cannot be reproduced from what is reported. · noted in this summary

Read this if you fine-map GWAS or molecular QTL signals and need calibrated error control, credible sets, or a summary-statistic route that does not cost power.

Citations
57

Abstract

Multi-SNP genetic association analysis has become increasingly important in analyzing data from genome-wide association studies (GWASs) and molecular quantitative trait loci (QTL) mapping studies. In this paper, we propose novel computational approaches to address two outstanding issues in Bayesian multi-SNP genetic association analysis: namely, the control of false positive discoveries of identified association signals and the maximization of the efficiency of statistical inference by utilizing summary statistics. Quantifying the strength and uncertainty of genetic association signals has been a long-standing theme in statistical genetics. However, there is a lack of formal statistical procedures that can rigorously control type I errors in multi-SNP analysis. We propose an intuitive hierarchical representation of genetic association signals based on Bayesian posterior probabilities, which subsequently enables rigorous control of false discovery rate (FDR) and construction of Bayesian credible sets. From the perspective of statistical data reduction, we examine the computational approaches of multi-SNP analysis using z-statistics from single-SNP association testing and conclude that they likely yield conservative results comparing to using individual-level data. Built on this result, we propose a set of sufficient summary statistics that can lead to identical results as individual-level data without sacrificing power. Our novel computational approaches are implemented in the software package, DAP-G (https://github.com/xqwen/dap), which applies to both GWASs and genome-wide molecular QTL mapping studies. It is highly computationally efficient and approximately 20 times faster than the state-of-the-art implementation of Bayesian multi-SNP analysis software. We demonstrate the proposed computational approaches using carefully constructed simulation studies and illustrate a complete workflow for multi-SNP analysis of cis expression quantitative trait loci using the whole blood data from the GTEx project.

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