Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs
Finding causal variants and genes from GWAS loci results remains a challenge. Here, the authors train a model to predict if a variant affects nearby gene expression, and apply the method to identify new possible causal eQTLs and mechanisms of GWAS loci.
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Nature Portfolio
2021
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oai:doaj.org-article:f492bfb02a464d72ae49db0b2d0594f82021-12-02T15:02:50ZLeveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs10.1038/s41467-021-23134-82041-1723https://doaj.org/article/f492bfb02a464d72ae49db0b2d0594f82021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23134-8https://doaj.org/toc/2041-1723Finding causal variants and genes from GWAS loci results remains a challenge. Here, the authors train a model to predict if a variant affects nearby gene expression, and apply the method to identify new possible causal eQTLs and mechanisms of GWAS loci.Qingbo S. WangDavid R. KelleyJacob UlirschMasahiro KanaiShuvom SadhukaRan CuiCarlos AlborsNathan ChengYukinori OkadaThe Biobank Japan ProjectFrancois AguetKristin G. ArdlieDaniel G. MacArthurHilary K. FinucaneNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Science Q |
| spellingShingle |
Science Q Qingbo S. Wang David R. Kelley Jacob Ulirsch Masahiro Kanai Shuvom Sadhuka Ran Cui Carlos Albors Nathan Cheng Yukinori Okada The Biobank Japan Project Francois Aguet Kristin G. Ardlie Daniel G. MacArthur Hilary K. Finucane Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs |
| description |
Finding causal variants and genes from GWAS loci results remains a challenge. Here, the authors train a model to predict if a variant affects nearby gene expression, and apply the method to identify new possible causal eQTLs and mechanisms of GWAS loci. |
| format |
article |
| author |
Qingbo S. Wang David R. Kelley Jacob Ulirsch Masahiro Kanai Shuvom Sadhuka Ran Cui Carlos Albors Nathan Cheng Yukinori Okada The Biobank Japan Project Francois Aguet Kristin G. Ardlie Daniel G. MacArthur Hilary K. Finucane |
| author_facet |
Qingbo S. Wang David R. Kelley Jacob Ulirsch Masahiro Kanai Shuvom Sadhuka Ran Cui Carlos Albors Nathan Cheng Yukinori Okada The Biobank Japan Project Francois Aguet Kristin G. Ardlie Daniel G. MacArthur Hilary K. Finucane |
| author_sort |
Qingbo S. Wang |
| title |
Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs |
| title_short |
Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs |
| title_full |
Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs |
| title_fullStr |
Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs |
| title_full_unstemmed |
Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs |
| title_sort |
leveraging supervised learning for functionally informed fine-mapping of cis-eqtls identifies an additional 20,913 putative causal eqtls |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/f492bfb02a464d72ae49db0b2d0594f8 |
| work_keys_str_mv |
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