Fast and precise genetic correlation + heritability analysis is now at your fingertips. Kudos @mitchiela.bsky.social

Check out our full preprint in @biorxiv!

Webtool ldlink.nih.gov/ldscore

GitHub github.com/CBIIT/ldsc/t...

Preprint doi.org/10.64898/202...

#Genomics #GWAS #LDLink #OpenScience

We are excited to integrate LDscore into the @theNCI LDlink suite!

By joining a platform with:
- 33,000+ annual active users
- 2,200+ citations

We’re moving the field from "managing HPC environments" to scientific discovery.

LDscore covers population LD references across 10 diverse populations, including African Americans and Latinos/Admixed Americans.

🌍 Diversity matters! New, expanded population references: LDscore allows for precision population matching, which is crucial even within subcontinental ancestry groups (e.g., using FIN for FinnGen RA). This is critical for isolating true polygenic signals from population stratification.

Precision is a core priority. We benchmarked the new Python 3 engine against the original command-line implementation using real GWAS data. The results are identical: total h2, intercept, and ratio metrics match perfectly. Accessible yet rigorous analysis.

Our first goal was to simplify LDSC analysis. LDscore moves the heavy lifting to the cloud, allowing you to compute LD scores, calculate h2, and estimate genetic correlation with a few clicks through a clean, intuitive interface. No local installation required.

LDSC is a cornerstone of statistical genetics. To make LDSC more accessible for the next generation of GWAS, we made LDscore: A scalable, Python 3-powered cloud platform that brings LD score regression directly to your browser.