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Fast estimation of genetic correlation for biobank-scale data.
Yue Wu1, Kathryn S Burch2, Andrea Ganna3
1Department of Computer Science, UCLA, Los Angeles, CA 90095, USA.
American Journal of Human Genetics
|December 3, 2021
Summary
We developed SCORE, a new method for estimating genetic correlation between complex traits. SCORE is scalable and accurate, providing more precise genetic correlation estimates than existing methods, even on large datasets like the UK Biobank.
Area of Science:
- Quantitative genetics
- Statistical genomics
- Bioinformatics
Background:
- Understanding genetic correlations between complex traits is crucial for biological insights.
- Existing methods for genetic correlation estimation face scalability challenges with large datasets or reduced precision with summary statistics.
Purpose of the Study:
- To introduce SCORE (scalable genetic correlation estimator), a novel method for accurate and scalable genetic correlation estimation.
- To improve upon the precision and computational efficiency of current genetic correlation methods.
Main Methods:
- SCORE utilizes a randomized method of moments approach.
- The method was validated on simulated data and applied to the large-scale UK Biobank dataset (≈300K individuals, ≈500K SNPs).
Main Results:
- SCORE achieved significant reductions in standard error compared to LD-score regression (LDSC) (44%) and high-definition likelihood (HDL) (20%).
- SCORE enabled rapid computation on the UK Biobank dataset, orders of magnitude faster than individual-based methods.
- Applied to UK Biobank data, SCORE identified 200 additional significant genetic correlations between trait pairs compared to LDSC.
Conclusions:
- SCORE offers a computationally efficient and statistically accurate solution for estimating genetic correlations in large biobanks.
- This method enhances the discovery of genetic relationships between complex traits, advancing our understanding of their shared genetic architecture.
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