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 cri...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012953 |
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| author | Jiaxin Hu Jesse N Weber Lauren E Fuess Natalie C Steinel Daniel I Bolnick Miaoyan Wang |
| author_facet | Jiaxin Hu Jesse N Weber Lauren E Fuess Natalie C Steinel Daniel I Bolnick Miaoyan Wang |
| author_sort | Jiaxin Hu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-07e297762d314f608eb8973f0052e54e |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-07e297762d314f608eb8973f0052e54e2025-08-20T03:52:04ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-04-01214e101295310.1371/journal.pcbi.1012953A spectral framework to map QTLs affecting joint differential networks of gene co-expression.Jiaxin HuJesse N WeberLauren E FuessNatalie C SteinelDaniel I BolnickMiaoyan WangStudying 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.https://doi.org/10.1371/journal.pcbi.1012953 |
| spellingShingle | Jiaxin Hu Jesse N Weber Lauren E Fuess Natalie C Steinel Daniel I Bolnick Miaoyan Wang A spectral framework to map QTLs affecting joint differential networks of gene co-expression. PLoS Computational Biology |
| title | A spectral framework to map QTLs affecting joint differential networks of gene co-expression. |
| title_full | A spectral framework to map QTLs affecting joint differential networks of gene co-expression. |
| title_fullStr | A spectral framework to map QTLs affecting joint differential networks of gene co-expression. |
| title_full_unstemmed | A spectral framework to map QTLs affecting joint differential networks of gene co-expression. |
| title_short | A spectral framework to map QTLs affecting joint differential networks of gene co-expression. |
| title_sort | spectral framework to map qtls affecting joint differential networks of gene co expression |
| url | https://doi.org/10.1371/journal.pcbi.1012953 |
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