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This content will become publicly available on April 17, 2026

Title: A spectral framework to map QTLs affecting joint differential networks of gene co-expression
Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback Gasterosteus aculeatus data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.  more » « less
Award ID(s):
2141865 2023239
PAR ID:
10597688
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Bozdag, Serdar
Publisher / Repository:
PLOS
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
21
Issue:
4
ISSN:
1553-7358
Page Range / eLocation ID:
e1012953
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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