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LDSC regression-based heritability estimates can be biased when summary statistics are obtained from meta-analysis or
Biorxiv : the Preprint Server for Biology
|July 17, 2026
Summary
Linkage disequilibrium score (LDSC) regression can underestimate heritability for complex traits. Using imputed variants or meta-analysis summary statistics may lead to biased results, highlighting the need for careful data selection in genetic studies.
Area of Science:
- Genetics
- Biostatistics
Background:
- Linkage disequilibrium score (LDSC) regression is a widely used method for estimating heritability from summary statistics.
- It provides an alternative to methods requiring individual-level genetic data.
- However, LDSC regression may yield biased heritability estimates.
Purpose of the Study:
- To investigate the properties of LDSC regression using Alzheimer's disease (AD) summary statistics.
- To compare heritability estimates from LDSC regression with those derived from individual-level data.
- To identify potential biases in heritability estimation methods.
Main Methods:
- Applied LDSC regression to summary statistics from large-scale AD studies.
- Utilized various linkage disequilibrium (LD) reference panels.
- Compared LDSC regression results with estimates from individual-level genetic data analysis.
- Analyzed the impact of imputed variants versus genotyped variants on heritability estimates.
Main Results:
- LDSC regression applied to meta-analysis summary statistics resulted in heritability underestimation.
- Combining studies of different ancestries without appropriate LD reference panels exacerbated bias.
- Using imputed variants, even with high accuracy, led to reduced heritability estimates for AD.
- A decrease in heritability was observed when analyzing imputed variants compared to genotyped variants.
Conclusions:
- Estimating heritability using meta-analysis summary statistics can be unreliable due to potential biases.
- Imputed genetic data may lead to underestimation of heritability compared to genotyped or sequence data.
- Careful consideration of data sources (e.g., meta-analysis, imputed vs. genotyped variants) is crucial for accurate heritability estimation.
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