ClipperQTL: ultrafast and powerful eGene identification method
Abstract A central task in expression quantitative trait locus analysis is to identify cis-eGenes, i.e., genes whose expression levels are regulated by at least one local genetic variant. Existing cis-eGene identification methods are either computationally expensive, requiring thousands of permutati...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-025-03662-y |
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| author | Heather J. Zhou Xinzhou Ge Jingyi Jessica Li |
| author_facet | Heather J. Zhou Xinzhou Ge Jingyi Jessica Li |
| author_sort | Heather J. Zhou |
| collection | DOAJ |
| description | Abstract A central task in expression quantitative trait locus analysis is to identify cis-eGenes, i.e., genes whose expression levels are regulated by at least one local genetic variant. Existing cis-eGene identification methods are either computationally expensive, requiring thousands of permutations per gene (FastQTL), or statistically underpowered (eigenMT and TreeQTL). We propose ClipperQTL, which requires only one permutation for data sets with large sample sizes (>450; ClipperQTL works on smaller data sets too). We show that ClipperQTL performs as well as FastQTL and runs up to 500 times faster. The R package ClipperQTL is available at https://github.com/heatherjzhou/ClipperQTL . |
| format | Article |
| id | doaj-art-3a6245136ee445d58dc58f314531d80c |
| institution | Kabale University |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-3a6245136ee445d58dc58f314531d80c2025-08-20T04:02:54ZengBMCGenome Biology1474-760X2025-07-0126111210.1186/s13059-025-03662-yClipperQTL: ultrafast and powerful eGene identification methodHeather J. Zhou0Xinzhou Ge1Jingyi Jessica Li2Department of Statistics and Data Science, University of California, Los AngelesDepartment of Statistics and Data Science, University of California, Los AngelesDepartment of Statistics and Data Science, University of California, Los AngelesAbstract A central task in expression quantitative trait locus analysis is to identify cis-eGenes, i.e., genes whose expression levels are regulated by at least one local genetic variant. Existing cis-eGene identification methods are either computationally expensive, requiring thousands of permutations per gene (FastQTL), or statistically underpowered (eigenMT and TreeQTL). We propose ClipperQTL, which requires only one permutation for data sets with large sample sizes (>450; ClipperQTL works on smaller data sets too). We show that ClipperQTL performs as well as FastQTL and runs up to 500 times faster. The R package ClipperQTL is available at https://github.com/heatherjzhou/ClipperQTL .https://doi.org/10.1186/s13059-025-03662-y |
| spellingShingle | Heather J. Zhou Xinzhou Ge Jingyi Jessica Li ClipperQTL: ultrafast and powerful eGene identification method Genome Biology |
| title | ClipperQTL: ultrafast and powerful eGene identification method |
| title_full | ClipperQTL: ultrafast and powerful eGene identification method |
| title_fullStr | ClipperQTL: ultrafast and powerful eGene identification method |
| title_full_unstemmed | ClipperQTL: ultrafast and powerful eGene identification method |
| title_short | ClipperQTL: ultrafast and powerful eGene identification method |
| title_sort | clipperqtl ultrafast and powerful egene identification method |
| url | https://doi.org/10.1186/s13059-025-03662-y |
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