Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs | Nature Communications
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Communications volume 12, Article number: 3394 (2021) Cite this article 12k Accesses 25 Citations 17 Altmetric Metrics details 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 large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQT
Introduction Although genome-wide association studies (GWAS) have identified large numbers of loci associated with complex traits1,2, identifying the underlying biological mechanisms is often difficult. Two particular challenges are that (1) the majority of the associated variants are in noncoding regions1, and (2) the association signals from GWAS studies typically contain a large number of variants in linkage disequilibrium (LD)3. Interpreting associations in GWAS to identify the underlying causal mechanisms requires an understanding of the function of noncoding variants at single-variant…
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