Quantifying genetic mapping power in a Diversity Outbred mouse population

Jared L. Noel

Document Type

Article

Publication Date

2025

Keywords

JMG

Abstract

Genetic mapping is a powerful tool that can be used to elucidate the genetic basis of complex traits, including disease. Locating loci across the genome that contain variants which drive quantitative traits can provide a link between genetic variation and phenotypic variation, informing improved targeted therapeutics. A class of variants known as expression quantitative trait loci (eQTLs) are especially important to understanding the genetic basis of disease, but eQTL mapping studies are often limited by low statistical power. Here, we analyze Diversity Outbred (DO) mouse datasets to empirically evaluate how sample size and eQTL effect size impact power and false discovery rate in eQTL mapping. We demonstrate how decreasing sample size reduces detection power and mapping precision, a result that is accentuated in eQTLs with modest effect sizes. We find that the power to detect distal eQTLs is lost more rapidly with reduced sample size than the power to detect local eQTLs, introducing potential biases in biological interpretation. We quantify these effects by constructing power and false discovery rate curves, and we fit two generalized additive models to these curves which form the basis of our DO eQTL power calculator. Our results highlight the importance of adequate sample size in eQTL mapping in DO mice and provide an interactive tool to guide researchers in experimental design.

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