Semi-synthetic data results in terms of (A) direct causal annotation, (B) annotation focused on horizontal pleiotropy only, (C) graph reconstruction, (D) combined annotation and graph reconstruction, and (E) CRCE estimation accuracy.

<p>Four of the graphs summarize two evaluation metrics. Arrows near the y-axis denote whether a higher (upward arrow) or a lower (downward arrow) score is better. We do not plot the results of cis-eQTL and SuSiE in (D) and (E) when they exhibit much worse performance. The cis-window and cTWAS...

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Bibliographic Details
Main Author:	Eric V. Strobl (16854789) (author)
Other Authors:	Eric R. Gamazon (8724606) (author)
Published:	2025
Subjects:	Genetics Molecular Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified underlying causal graph two complex diseases root causal mechanisms observational data alone gene expression levels eliminating pathology near causal graph using alleviate disease entirely algorithm simultaneously determines widespread pleiotropy therefore propose patient heterogeneity linkage disequilibrium existing algorithm diverse group distal relations directly cause annotate cis
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Semi-synthetic data results in terms of (A) direct causal annotation, (B) annotation focused on horizontal pleiotropy only, (C) graph reconstruction, (D) combined annotation and graph reconstruction, and (E) CRCE estimation accuracy.

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