ADELLE: A global testing method for trans-eQTL mapping

Takintayo Akinbiyi, Mary Sara McPeek and Mark Abney

PLOS Genetics, 2025, vol. 21, issue 1, 1-22

Abstract: Understanding the genetic regulatory mechanisms of gene expression is an ongoing challenge. Genetic variants that are associated with expression levels are readily identified when they are proximal to the gene (i.e., cis-eQTLs), but SNPs distant from the gene whose expression levels they are associated with (i.e., trans-eQTLs) have been much more difficult to discover, even though they account for a majority of the heritability in gene expression levels. A major impediment to the identification of more trans-eQTLs is the lack of statistical methods that are powerful enough to overcome the obstacles of small effect sizes and large multiple testing burden of trans-eQTL mapping. Here, we propose ADELLE, a powerful statistical testing framework that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that for detecting SNPs that are associated with 0.1%–2% of 10,000 traits, among the 8 methods we consider ADELLE is clearly the most powerful overall, with either the highest power or power not significantly different from the highest for all settings in that range. We apply ADELLE to a mouse advanced intercross line data set and show its ability to find trans-eQTLs that were not significant under a standard analysis. We also apply ADELLE to trans-eQTL mapping in the eQTLGen data, and for 1,451 previously identified trans-eQTLs, we discover trans association with additional expression traits beyond those previously identified. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.Author summary: Identification of trans-eQTLs, i.e., genetic variants that regulate expression of genes that are not proximal, has proved challenging, even though previous studies suggest that they may account for a large proportion of complex trait variance. Compared to cis-eQTLs, i.e., variants that regulate expression of proximal genes, trans-eQTLs are much harder to detect because their effect sizes tend to be smaller, and the space of possible genes whose expression they might be associated with is much bigger, leading to a higher burden of multiple comparisons. We developed ADELLE, a powerful statistical method that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that for detecting SNPs that are associated with 0.1%–2% of the expression traits, ADELLE is clearly the most powerful overall among the 8 methods we compare. We apply ADELLE to eQTLGen data and also to a mouse advanced intercross line data set and show its ability to detect trans-eQTL signal that was not significant under a standard analysis. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pgen00:1011563

DOI: 10.1371/journal.pgen.1011563

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