ArticleNature communications2026
Estimating genetic correlation jointly using individual-level and summary-level GWAS data.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Estimating genetic correlation jointly using individual-level and summary-level GWAS data.Nature communications · 2026Article
- Estimating trans-ancestry genetic correlation with unbalanced data resources.Journal of the American Statistical Association · 2024Article
- Decomposing heritability and genetic covariance by direct and indirect effect paths.PLoS genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
With increasing access to individual-level data from genome-wide association studies, it is now common for researchers to have individual-level data of some traits, whereas for other traits only publicly released summary statistics are available due to privacy and safety concerns. Current genetic-correlation methods require both traits to be in the same format. When one trait has individual-level data and the other has only summary statistics, researchers often convert individual-level data to summary statistics and then apply summary-based methods, which is inefficient and can lose information. Here we introduce GENJI, a method for estimating within-population or transethnic genetic correlation based on individual-level data for one trait and summary-level data for the other. Across extensive simulations and real data analyses, GENJI produces more reliable and efficient estimation than summary data-based methods. We further show that more accurate genetic correlation estimation can improve cross-population polygenic risk prediction.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.