Exact association-test distributions for prior-informed stepwise SNP prioritization
Abstract
Prioritizing causal variants in Genome-Wide Association Studies (GWAS) is challenging due to linkage disequilibrium and high dimensionality. We propose an iterative algorithm for the selection of genetic variables that allows the integration of biological priors. The proposed algorithm requires the specification of the power function of a test statistic and incorporates a priori information on each SNP. We derive the approximate and exact distributions of several commonly used test statistics. At each step of the proposed algorithm, we select the SNP with the largest a posteriori probability of being causal. We consider several stopping criteria for the algorithm. We conduct comprehensive simulation studies to assess the performance of several versions of the proposed algorithm and to make a comparison to some existing competitors. The use of the proposed prioritization algorithm is illustrated with a cis-eQTL application to a human gene-expression data, using European discovery and Yoruba cross-population replication.
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BibTeX
@misc{doulabe2026exact,
title = {{Exact association-test distributions for prior-informed stepwise SNP prioritization}},
author = {N. Kossi Doulabe and Anne-Sophie Charest and Lajmi Lakhal-Chaieb},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.05203},
eprint = {2611.05203},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.05203},
note = {CC0},
}
RIS
TY - UNPB
TI - Exact association-test distributions for prior-informed stepwise SNP prioritization
AU - N. Kossi Doulabe
AU - Anne-Sophie Charest
AU - Lajmi Lakhal-Chaieb
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.05203
UR - https://arxiv.org/abs/2611.05203
AB - Prioritizing causal variants in Genome-Wide Association Studies (GWAS) is challenging due to linkage disequilibrium and high dimensionality. We propose an iterative algorithm for the selection of genetic variables that allows the integration of biological priors. The proposed algorithm requires the specification of the power function of a test statistic and incorporates a priori information on each SNP. We derive the approximate and exact distributions of several commonly used test statistics. At each step of the proposed algorithm, we select the SNP with the largest a posteriori probability of being causal. We consider several stopping criteria for the algorithm. We conduct comprehensive simulation studies to assess the performance of several versions of the proposed algorithm and to make a comparison to some existing competitors. The use of the proposed prioritization algorithm is illustrated with a cis-eQTL application to a human gene-expression data, using European discovery and Yoruba cross-population replication.
ER -