Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an add...
Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants’ effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked...
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Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs
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TN_cdi_doaj_primary_oai_doaj_org_article_f492bfb02a464d72ae49db0b2d0594f8
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_f492bfb02a464d72ae49db0b2d0594f8
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-021-23134-8
