To build upon the existing work in which we generated additive heritability estimates across the UK biobank traits, we have now generated and curated a collection of genetic correlations between each of traits and disorders.

The subset of traits that we host here represent the collection of traits with medium or high confidence of significant \(h^2\) as detailed on our heritability results website. Please click below to explore the results and download the data!


Browse \(r_g\) Download Results Visualise \(r_g\)


For further information, please feel free to contact us at: .

FAQ

Where can I find the genetic correlation results?

We’ve built a browser to allow you to peruse and download the results.

What method have you used to estimate genetic correlation?

We use LD score regression to estimate genetic correlation. Full code and technical details are available in the github repository. We have added some flags to enable more rapid evaluation of \(r_g\) across a large collection of phenotypes at the ldsc repository here.

What about phenotypic correlations?

For each phenotype, we residualised out covariates used in the GWAS’, and then evaluated the correlations. Caitlin Carey in Elise Robinson’s lab generated the phenotypic correlations using this python script.

Are the primary GWAS results used for this analysis available?

Absolutely! GWAS results are available for download. Please check out this introductory post for much more information.

Credits

This website and browser was generated by Duncan Palmer, and all errors are his. Please report any issues in the \(r_g\) website github repository.

Genetic correlation analysis team:

Liam Abbott, Benjamin Neale, Duncan Palmer.

Neale Lab UKBB team:

Liam Abbott, Jon Bloom, Sam Bryant, Caitlin Carey, Claire Churchhouse, Andrea Ganna, Jackie Goldstein, Daniel Howrigan, Dan King, Benjamin Neale, Duncan Palmer, Tim Poterba, Cotton Seed, Raymond Walters.