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LDSC regression-based heritability estimates can be biased when summary statistics are obtained from meta-analysis or imputed variants

Motivation: Linkage disequilibrium score (LDSC) regression is a popular method to estimate heritability for complex traits using summary statistics and linkage disequilibrium (LD)

GIST

Motivation: Linkage disequilibrium score (LDSC) regression is a popular method to estimate heritability for complex traits using summary statistics and linkage disequilibrium (LD) reference panels, offering a practical alternative to methods requiring individual-level data. Despite its widespread use, LDSC regression can produce biased heritability estimates. The properties of LDSC regression were investigated using summary statistics from several large-scale Alzheimer's disease (AD) studies and a variety of LD reference panels. These heritability estimates were compared with those obtained from individual-level data. Results: When LDSC regression was applied to summary statistics obtained from meta-analysis, it led to an underestimation of heritability. This can occur if meta-analysis is used to combine studies of different ancestries leading to the caveat of the lack of an appropriate LD reference panel. Additionally meta-analyses often include studies with different phenotype definitions, that not only impacts heritability estimates but also makes them uninterpretable. Summary statistics generated from imputed variants, even those with high imputation accuracy, can lead to underestimation of heritability.

Clinical Editorial

bioRxiv (Biomedical Preprints) published a clinical update in Research Highlights on 09 Jul 2026. The item focuses on LDSC regression-based heritability estimates can be biased when summary statistics are obtained from meta-analysis or imputed variants. Review the original article for the full source wording and details.

Original source: https://www.biorxiv.org/content/10.64898/2026.07.05.736573v1?rss=1